Dan Jurafsky

Also published as: Daniel Jurafsky


2020

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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Dan Jurafsky | Joyce Chai | Natalie Schluter | Joel Tetreault
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models
Dan Iter | Kelvin Guu | Larry Lansing | Dan Jurafsky
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent models for unsupervised representation learning of text have employed a number of techniques to improve contextual word representations but have put little focus on discourse-level representations. We propose Conpono, an inter-sentence objective for pretraining language models that models discourse coherence and the distance between sentences. Given an anchor sentence, our model is trained to predict the text k sentences away using a sampled-softmax objective where the candidates consist of neighboring sentences and sentences randomly sampled from the corpus. On the discourse representation benchmark DiscoEval, our model improves over the previous state-of-the-art by up to 13% and on average 4% absolute across 7 tasks. Our model is the same size as BERT-Base, but outperforms the much larger BERT-Large model and other more recent approaches that incorporate discourse. We also show that Conpono yields gains of 2%-6% absolute even for tasks that do not explicitly evaluate discourse: textual entailment (RTE), common sense reasoning (COPA) and reading comprehension (ReCoRD).

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Social Bias Frames: Reasoning about Social and Power Implications of Language
Maarten Sap | Saadia Gabriel | Lianhui Qin | Dan Jurafsky | Noah A. Smith | Yejin Choi
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Warning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but rather the implied meanings, that frame people’s judgments about others. For example, given a statement that “we shouldn’t lower our standards to hire more women,” most listeners will infer the implicature intended by the speaker - that “women (candidates) are less qualified.” Most semantic formalisms, to date, do not capture such pragmatic implications in which people express social biases and power differentials in language. We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others. In addition, we introduce the Social Bias Inference Corpus to support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups. We then establish baseline approaches that learn to recover Social Bias Frames from unstructured text. We find that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias (80% F1), they are not effective at spelling out more detailed explanations in terms of Social Bias Frames. Our study motivates future work that combines structured pragmatic inference with commonsense reasoning on social implications.

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DeSMOG: Detecting Stance in Media On Global Warming
Yiwei Luo | Dallas Card | Dan Jurafsky
Findings of the Association for Computational Linguistics: EMNLP 2020

Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, “Leading scientists agree that global warming is a serious concern,” framing a clause which affirms their own stance (“that global warming is serious”) as an opinion endorsed ("[scientists] agree”) by a reputable source (“leading”). In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: “Mistaken scientists claim [...]." Our work studies opinion-framing in the global warming (GW) debate, an increasingly partisan issue that has received little attention in NLP. We introduce DeSMOG, a dataset of stance-labeled GW sentences, and train a BERT classifier to study novel aspects of argumentation in how different sides of a debate represent their own and each other’s opinions. From 56K news articles, we find that similar linguistic devices for self-affirming and opponent-doubting discourse are used across GW-accepting and skeptic media, though GW-skeptical media shows more opponent-doubt. We also find that authors often characterize sources as hypocritical, by ascribing opinions expressing the author’s own view to source entities known to publicly endorse the opposing view. We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.

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Utility is in the Eye of the User: A Critique of NLP Leaderboards
Kawin Ethayarajh | Dan Jurafsky
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Benchmarks such as GLUE have helped drive advances in NLP by incentivizing the creation of more accurate models. While this leaderboard paradigm has been remarkably successful, a historical focus on performance-based evaluation has been at the expense of other qualities that the NLP community values in models, such as compactness, fairness, and energy efficiency. In this opinion paper, we study the divergence between what is incentivized by leaderboards and what is useful in practice through the lens of microeconomic theory. We frame both the leaderboard and NLP practitioners as consumers and the benefit they get from a model as its utility to them. With this framing, we formalize how leaderboards – in their current form – can be poor proxies for the NLP community at large. For example, a highly inefficient model would provide less utility to practitioners but not to a leaderboard, since it is a cost that only the former must bear. To allow practitioners to better estimate a model’s utility to them, we advocate for more transparency on leaderboards, such as the reporting of statistics that are of practical concern (e.g., model size, energy efficiency, and inference latency).

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Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models
Isabel Papadimitriou | Dan Jurafsky
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We propose transfer learning as a method for analyzing the encoding of grammatical structure in neural language models. We train LSTMs on non-linguistic data and evaluate their performance on natural language to assess which kinds of data induce generalizable structural features that LSTMs can use for natural language. We find that training on non-linguistic data with latent structure (MIDI music or Java code) improves test performance on natural language, despite no overlap in surface form or vocabulary. To pinpoint the kinds of abstract structure that models may be encoding to lead to this improvement, we run similar experiments with two artificial parentheses languages: one which has a hierarchical recursive structure, and a control which has paired tokens but no recursion. Surprisingly, training a model on either of these artificial languages leads the same substantial gains when testing on natural language. Further experiments on transfer between natural languages controlling for vocabulary overlap show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced by pre-training correspond to the cross-linguistic syntactic properties. Our results provide insights into the ways that neural models represent abstract syntactic structure, and also about the kind of structural inductive biases which allow for natural language acquisition.

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With Little Power Comes Great Responsibility
Dallas Card | Peter Henderson | Urvashi Khandelwal | Robin Jia | Kyle Mahowald | Dan Jurafsky
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored by the NLP community. Underpowered experiments make it more difficult to discern the difference between statistical noise and meaningful model improvements, and increase the chances of exaggerated findings. By meta-analyzing a set of existing NLP papers and datasets, we characterize typical power for a variety of settings and conclude that underpowered experiments are common in the NLP literature. In particular, for several tasks in the popular GLUE benchmark, small test sets mean that most attempted comparisons to state of the art models will not be adequately powered. Similarly, based on reasonable assumptions, we find that the most typical experimental design for human rating studies will be underpowered to detect small model differences, of the sort that are frequently studied. For machine translation, we find that typical test sets of 2000 sentences have approximately 75% power to detect differences of 1 BLEU point. To improve the situation going forward, we give an overview of best practices for power analysis in NLP and release a series of notebooks to assist with future power analyses.

2019

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Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts
Julia Kruk | Jonah Lubin | Karan Sikka | Xiao Lin | Dan Jurafsky | Ajay Divakaran
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Computing author intent from multimodal data like Instagram posts requires modeling a complex relationship between text and image. For example, a caption might evoke an ironic contrast with the image, so neither caption nor image is a mere transcript of the other. Instead they combine—via what has been called meaning multiplication (Bateman et al.)- to create a new meaning that has a more complex relation to the literal meanings of text and image. Here we introduce a multimodal dataset of 1299 Instagram posts labeled for three orthogonal taxonomies: the authorial intent behind the image-caption pair, the contextual relationship between the literal meanings of the image and caption, and the semiotic relationship between the signified meanings of the image and caption. We build a baseline deep multimodal classifier to validate the taxonomy, showing that employing both text and image improves intent detection by 9.6 compared to using only the image modality, demonstrating the commonality of non-intersective meaning multiplication. The gain with multimodality is greatest when the image and caption diverge semiotically. Our dataset offers a new resource for the study of the rich meanings that result from pairing text and image.

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Neural Text Style Transfer via Denoising and Reranking
Joseph Lee | Ziang Xie | Cindy Wang | Max Drach | Dan Jurafsky | Andrew Ng
Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation

We introduce a simple method for text style transfer that frames style transfer as denoising: we synthesize a noisy corpus and treat the source style as a noisy version of the target style. To control for aspects such as preserving meaning while modifying style, we propose a reranking approach in the data synthesis phase. We evaluate our method on three novel style transfer tasks: transferring between British and American varieties, text genres (formal vs. casual), and lyrics from different musical genres. By measuring style transfer quality, meaning preservation, and the fluency of generated outputs, we demonstrate that our method is able both to produce high-quality output while maintaining the flexibility to suggest syntactically rich stylistic edits.

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From Insanely Jealous to Insanely Delicious: Computational Models for the Semantic Bleaching of English Intensifiers
Yiwei Luo | Dan Jurafsky | Beth Levin
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

We introduce novel computational models for modeling semantic bleaching, a widespread category of change in which words become more abstract or lose elements of meaning, like the development of “arrive” from its earlier meaning ‘become at shore.’ We validate our methods on a widespread case of bleaching in English: de-adjectival adverbs that originate as manner adverbs (as in “awfully behaved”) and later become intensifying adverbs (as in “awfully nice”). Our methods formally quantify three reflexes of bleaching: decreasing similarity to the source meaning (e.g., “awful”), increasing similarity to a fully bleached prototype (e.g., “very”), and increasing productivity (e.g., the breadth of adjectives that an adverb modifies). We also test a new causal model and find evidence that bleaching is initially triggered in contexts such as “conspicuously evident” and “insanely jealous”, where an adverb premodifies a semantically similar adjective. These contexts provide a form of “bridging context” (Evans and Wilkins, 2000) that allow a manner adverb to be reinterpreted as an intensifying adverb similar to “very”.

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Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings
Dorottya Demszky | Nikhil Garg | Rob Voigt | James Zou | Jesse Shapiro | Matthew Gentzkow | Dan Jurafsky
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force. We quantify these aspects with existing lexical methods, and propose clustering of tweet embeddings as a means to identify salient topics for analysis across events; human evaluations show that our approach generates more cohesive topics than traditional LDA-based models. We apply our methods to study 4.4M tweets on 21 mass shootings. We provide evidence that the discussion of these events is highly polarized politically and that this polarization is primarily driven by partisan differences in framing rather than topic choice. We identify framing devices, such as grounding and the contrasting use of the terms “terrorist” and “crazy”, that contribute to polarization. Results pertaining to topic choice, affect and illocutionary force suggest that Republicans focus more on the shooter and event-specific facts (news) while Democrats focus more on the victims and call for policy changes. Our work contributes to a deeper understanding of the way group divisions manifest in language and to computational methods for studying them.

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Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms
Diyi Yang | Jiaao Chen | Zichao Yang | Dan Jurafsky | Eduard Hovy
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Modeling what makes a request persuasive - eliciting the desired response from a reader - is critical to the study of propaganda, behavioral economics, and advertising. Yet current models can’t quantify the persuasiveness of requests or extract successful persuasive strategies. Building on theories of persuasion, we propose a neural network to quantify persuasiveness and identify the persuasive strategies in advocacy requests. Our semi-supervised hierarchical neural network model is supervised by the number of people persuaded to take actions and partially supervised at the sentence level with human-labeled rhetorical strategies. Our method outperforms several baselines, uncovers persuasive strategies - offering increased interpretability of persuasive speech - and has applications for other situations with document-level supervision but only partial sentence supervision.

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Recursive Routing Networks: Learning to Compose Modules for Language Understanding
Ignacio Cases | Clemens Rosenbaum | Matthew Riemer | Atticus Geiger | Tim Klinger | Alex Tamkin | Olivia Li | Sandhini Agarwal | Joshua D. Greene | Dan Jurafsky | Christopher Potts | Lauri Karttunen
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We introduce Recursive Routing Networks (RRNs), which are modular, adaptable models that learn effectively in diverse environments. RRNs consist of a set of functions, typically organized into a grid, and a meta-learner decision-making component called the router. The model jointly optimizes the parameters of the functions and the meta-learner’s policy for routing inputs through those functions. RRNs can be incorporated into existing architectures in a number of ways; we explore adding them to word representation layers, recurrent network hidden layers, and classifier layers. Our evaluation task is natural language inference (NLI). Using the MultiNLI corpus, we show that an RRN’s routing decisions reflect the high-level genre structure of that corpus. To show that RRNs can learn to specialize to more fine-grained semantic distinctions, we introduce a new corpus of NLI examples involving implicative predicates, and show that the model components become fine-tuned to the inferential signatures that are characteristic of these predicates.

2018

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JESC: Japanese-English Subtitle Corpus
Reid Pryzant | Youngjoo Chung | Dan Jurafsky | Denny Britz
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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RtGender: A Corpus for Studying Differential Responses to Gender
Rob Voigt | David Jurgens | Vinodkumar Prabhakaran | Dan Jurafsky | Yulia Tsvetkov
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction
Ziang Xie | Guillaume Genthial | Stanley Xie | Andrew Ng | Dan Jurafsky
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Translation-based methods for grammar correction that directly map noisy, ungrammatical text to their clean counterparts are able to correct a broad range of errors; however, such techniques are bottlenecked by the need for a large parallel corpus of noisy and clean sentence pairs. In this paper, we consider synthesizing parallel data by noising a clean monolingual corpus. While most previous approaches introduce perturbations using features computed from local context windows, we instead develop error generation processes using a neural sequence transduction model trained to translate clean examples to their noisy counterparts. Given a corpus of clean examples, we propose beam search noising procedures to synthesize additional noisy examples that human evaluators were nearly unable to discriminate from nonsynthesized examples. Surprisingly, when trained on additional data synthesized using our best-performing noising scheme, our model approaches the same performance as when trained on additional nonsynthesized data.

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Deconfounded Lexicon Induction for Interpretable Social Science
Reid Pryzant | Kelly Shen | Dan Jurafsky | Stefan Wagner
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

NLP algorithms are increasingly used in computational social science to take linguistic observations and predict outcomes like human preferences or actions. Making these social models transparent and interpretable often requires identifying features in the input that predict outcomes while also controlling for potential confounds. We formalize this need as a new task: inducing a lexicon that is predictive of a set of target variables yet uncorrelated to a set of confounding variables. We introduce two deep learning algorithms for the task. The first uses a bifurcated architecture to separate the explanatory power of the text and confounds. The second uses an adversarial discriminator to force confound-invariant text encodings. Both elicit lexicons from learned weights and attentional scores. We use them to induce lexicons that are predictive of timely responses to consumer complaints (controlling for product), enrollment from course descriptions (controlling for subject), and sales from product descriptions (controlling for seller). In each domain our algorithms pick words that are associated with narrative persuasion; more predictive and less confound-related than those of standard feature weighting and lexicon induction techniques like regression and log odds.

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Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts
Matthew Lamm | Arun Chaganty | Christopher D. Manning | Dan Jurafsky | Percy Liang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

To understand a sentence like “whereas only 10% of White Americans live at or below the poverty line, 28% of African Americans do” it is important not only to identify individual facts, e.g., poverty rates of distinct demographic groups, but also the higher-order relations between them, e.g., the disparity between them. In this paper, we propose the task of Textual Analogy Parsing (TAP) to model this higher-order meaning. Given a sentence such as the one above, TAP outputs a frame-style meaning representation which explicitly specifies what is shared (e.g., poverty rates) and what is compared (e.g., White Americans vs. African Americans, 10% vs. 28%) between its component facts. Such a meaning representation can enable new applications that rely on discourse understanding such as automated chart generation from quantitative text. We present a new dataset for TAP, baselines, and a model that successfully uses an ILP to enforce the structural constraints of the problem.

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Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies
Anjalie Field | Doron Kliger | Shuly Wintner | Jennifer Pan | Dan Jurafsky | Yulia Tsvetkov
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Amidst growing concern over media manipulation, NLP attention has focused on overt strategies like censorship and “fake news”. Here, we draw on two concepts from political science literature to explore subtler strategies for government media manipulation: agenda-setting (selecting what topics to cover) and framing (deciding how topics are covered). We analyze 13 years (100K articles) of the Russian newspaper Izvestia and identify a strategy of distraction: articles mention the U.S. more frequently in the month directly following an economic downturn in Russia. We introduce embedding-based methods for cross-lingually projecting English frames to Russian, and discover that these articles emphasize U.S. moral failings and threats to the U.S. Our work offers new ways to identify subtle media manipulation strategies at the intersection of agenda-setting and framing.

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Measuring the Evolution of a Scientific Field through Citation Frames
David Jurgens | Srijan Kumar | Raine Hoover | Dan McFarland | Dan Jurafsky
Transactions of the Association for Computational Linguistics, Volume 6

Citations have long been used to characterize the state of a scientific field and to identify influential works. However, writers use citations for different purposes, and this varied purpose influences uptake by future scholars. Unfortunately, our understanding of how scholars use and frame citations has been limited to small-scale manual citation analysis of individual papers. We perform the largest behavioral study of citations to date, analyzing how scientific works frame their contributions through different types of citations and how this framing affects the field as a whole. We introduce a new dataset of nearly 2,000 citations annotated for their function, and use it to develop a state-of-the-art classifier and label the papers of an entire field: Natural Language Processing. We then show how differences in framing affect scientific uptake and reveal the evolution of the publication venues and the field as a whole. We demonstrate that authors are sensitive to discourse structure and publication venue when citing, and that how a paper frames its work through citations is predictive of the citation count it will receive. Finally, we use changes in citation framing to show that the field of NLP is undergoing a significant increase in consensus.

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Detecting Institutional Dialog Acts in Police Traffic Stops
Vinodkumar Prabhakaran | Camilla Griffiths | Hang Su | Prateek Verma | Nelson Morgan | Jennifer L. Eberhardt | Dan Jurafsky
Transactions of the Association for Computational Linguistics, Volume 6

We apply computational dialog methods to police body-worn camera footage to model conversations between police officers and community members in traffic stops. Relying on the theory of institutional talk, we develop a labeling scheme for police speech during traffic stops, and a tagger to detect institutional dialog acts (Reasons, Searches, Offering Help) from transcribed text at the turn (78% F-score) and stop (89% F-score) level. We then develop speech recognition and segmentation algorithms to detect these acts at the stop level from raw camera audio (81% F-score, with even higher accuracy for crucial acts like conveying the reason for the stop). We demonstrate that the dialog structures produced by our tagger could reveal whether officers follow law enforcement norms like introducing themselves, explaining the reason for the stop, and asking permission for searches. This work may therefore inform and aid efforts to ensure the procedural justice of police-community interactions.

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Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context
Urvashi Khandelwal | He He | Peng Qi | Dan Jurafsky
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We know very little about how neural language models (LM) use prior linguistic context. In this paper, we investigate the role of context in an LSTM LM, through ablation studies. Specifically, we analyze the increase in perplexity when prior context words are shuffled, replaced, or dropped. On two standard datasets, Penn Treebank and WikiText-2, we find that the model is capable of using about 200 tokens of context on average, but sharply distinguishes nearby context (recent 50 tokens) from the distant history. The model is highly sensitive to the order of words within the most recent sentence, but ignores word order in the long-range context (beyond 50 tokens), suggesting the distant past is modeled only as a rough semantic field or topic. We further find that the neural caching model (Grave et al., 2017b) especially helps the LSTM to copy words from within this distant context. Overall, our analysis not only provides a better understanding of how neural LMs use their context, but also sheds light on recent success from cache-based models.

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Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia
Dan Iter | Jong Yoon | Dan Jurafsky
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

Schizophrenia is a mental disorder which afflicts an estimated 0.7% of adults world wide. It affects many areas of mental function, often evident from incoherent speech. Diagnosing schizophrenia relies on subjective judgments resulting in disagreements even among trained clinicians. Recent studies have proposed the use of natural language processing for diagnosis by drawing on automatically-extracted linguistic features like discourse coherence and lexicon. Here, we present the first benchmark comparison of previously proposed coherence models for detecting symptoms of schizophrenia and evaluate their performance on a new dataset of recorded interviews between subjects and clinicians. We also present two alternative coherence metrics based on modern sentence embedding techniques that outperform the previous methods on our dataset. Lastly, we propose a novel computational model for reference incoherence based on ambiguous pronoun usage and show that it is a highly predictive feature on our data. While the number of subjects is limited in this pilot study, our results suggest new directions for diagnosing common symptoms of schizophrenia.

2017

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Incorporating Dialectal Variability for Socially Equitable Language Identification
David Jurgens | Yulia Tsvetkov | Dan Jurafsky
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Language identification (LID) is a critical first step for processing multilingual text. Yet most LID systems are not designed to handle the linguistic diversity of global platforms like Twitter, where local dialects and rampant code-switching lead language classifiers to systematically miss minority dialect speakers and multilingual speakers. We propose a new dataset and a character-based sequence-to-sequence model for LID designed to support dialectal and multilingual language varieties. Our model achieves state-of-the-art performance on multiple LID benchmarks. Furthermore, in a case study using Twitter for health tracking, our method substantially increases the availability of texts written by underrepresented populations, enabling the development of “socially inclusive” NLP tools.

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A Two-stage Sieve Approach for Quote Attribution
Grace Muzny | Michael Fang | Angel Chang | Dan Jurafsky
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3. Quote attribution, determining who said what in a given text, is important for tasks like creating dialogue systems, and in newer areas like computational literary studies, where it creates opportunities to analyze novels at scale rather than only a few at a time. We release QuoteLi3, which contains more than 6,000 annotations linking quotes to speaker mentions and quotes to speaker entities, and introduce a new algorithm for quote attribution. Our two-stage algorithm first links quotes to mentions, then mentions to entities. Using two stages encapsulates difficult sub-problems and improves system performance. The modular design allows us to tune for overall performance or higher precision, which is useful for many real-world use cases. Our system achieves an average F-score of 87.5 across three novels, outperforming previous systems, and can be tuned for precision of 90.4 at a recall of 65.1.

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Neural Net Models of Open-domain Discourse Coherence
Jiwei Li | Dan Jurafsky
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. Yet existing models of coherence focus on measuring individual aspects of coherence (lexical overlap, rhetorical structure, entity centering) in narrow domains. In this paper, we describe domain-independent neural models of discourse coherence that are capable of measuring multiple aspects of coherence in existing sentences and can maintain coherence while generating new sentences. We study both discriminative models that learn to distinguish coherent from incoherent discourse, and generative models that produce coherent text, including a novel neural latent-variable Markovian generative model that captures the latent discourse dependencies between sentences in a text. Our work achieves state-of-the-art performance on multiple coherence evaluations, and marks an initial step in generating coherent texts given discourse contexts.

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Adversarial Learning for Neural Dialogue Generation
Jiwei Li | Will Monroe | Tianlin Shi | Sébastien Jean | Alan Ritter | Dan Jurafsky
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We apply adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning problem where we jointly train two systems: a generative model to produce response sequences, and a discriminator—analagous to the human evaluator in the Turing test— to distinguish between the human-generated dialogues and the machine-generated ones. In this generative adversarial network approach, the outputs from the discriminator are used to encourage the system towards more human-like dialogue. Further, we investigate models for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines

2016

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Distinguishing Past, On-going, and Future Events: The EventStatus Corpus
Ruihong Huang | Ignacio Cases | Dan Jurafsky | Cleo Condoravdi | Ellen Riloff
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
William L. Hamilton | Kevin Clark | Jure Leskovec | Dan Jurafsky
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Deep Reinforcement Learning for Dialogue Generation
Jiwei Li | Will Monroe | Alan Ritter | Dan Jurafsky | Michel Galley | Jianfeng Gao
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change
William L. Hamilton | Jure Leskovec | Dan Jurafsky
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Visualizing and Understanding Neural Models in NLP
Jiwei Li | Xinlei Chen | Eduard Hovy | Dan Jurafsky
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing
Vinodkumar Prabhakaran | William L. Hamilton | Dan McFarland | Dan Jurafsky
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
William L. Hamilton | Jure Leskovec | Dan Jurafsky
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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A computational analysis of poetic style: Imagism and its influence on modern professional and amateur poetry
Justine T. Kao | Dan Jurafsky
Linguistic Issues in Language Technology, Volume 12, 2015 - Literature Lifts up Computational Linguistics

How do standards of poetic beauty change as a function of time and expertise? Here we use computational methods to compare the stylistic features of 359 English poems written by 19th century professional poets, Imagist poets, contemporary professional poets, and contemporary amateur poets. Building upon techniques designed to analyze style and sentiment in texts, we examine elements of poetic craft such as imagery, sound devices, emotive language, and diction. We find that contemporary professional poets use significantly more concrete words than 19th century poets, fewer emotional words, and more complex sound devices. These changes are consistent with the tenets of Imagism, an early 20thcentury literary movement. Further analyses show that contemporary amateur poems resemble 19th century professional poems more than contemporary professional poems on several dimensions. The stylistic similarities between contemporary amateur poems and 19th century professional poems suggest that elite standards of poetic beauty in the past “trickled down” to influence amateur works in the present. Our results highlight the influence of Imagism on the modern aesthetic and reveal the dynamics between “high” and “low” art. We suggest that computational linguistics may shed light on the forces and trends that shape poetic style.

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Do Multi-Sense Embeddings Improve Natural Language Understanding?
Jiwei Li | Dan Jurafsky
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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When Are Tree Structures Necessary for Deep Learning of Representations?
Jiwei Li | Thang Luong | Dan Jurafsky | Eduard Hovy
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Lexicon-Free Conversational Speech Recognition with Neural Networks
Andrew Maas | Ziang Xie | Dan Jurafsky | Andrew Ng
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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A Hierarchical Neural Autoencoder for Paragraphs and Documents
Jiwei Li | Thang Luong | Dan Jurafsky
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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The Users Who Say ‘Ni’: Audience Identification in Chinese-language Restaurant Reviews
Rob Voigt | Dan Jurafsky
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Obituary: Charles J. Fillmore
Dan Jurafsky
Computational Linguistics, Volume 40, Issue 3 - September 2014

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On the Importance of Text Analysis for Stock Price Prediction
Heeyoung Lee | Mihai Surdeanu | Bill MacCartney | Dan Jurafsky
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We investigate the importance of text analysis for stock price prediction. In particular, we introduce a system that forecasts companies’ stock price changes (UP, DOWN, STAY) in response to financial events reported in 8-K documents. Our results indicate that using text boosts prediction accuracy over 10% (relative) over a strong baseline that incorporates many financially-rooted features. This impact is most important in the short term (i.e., the next day after the financial event) but persists for up to five days.

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Event Extraction Using Distant Supervision
Kevin Reschke | Martin Jankowiak | Mihai Surdeanu | Christopher Manning | Daniel Jurafsky
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Distant supervision is a successful paradigm that gathers training data for information extraction systems by automatically aligning vast databases of facts with text. Previous work has demonstrated its usefulness for the extraction of binary relations such as a person’s employer or a film’s director. Here, we extend the distant supervision approach to template-based event extraction, focusing on the extraction of passenger counts, aircraft types, and other facts concerning airplane crash events. We present a new publicly available dataset and event extraction task in the plane crash domain based on Wikipedia infoboxes and newswire text. Using this dataset, we conduct a preliminary evaluation of four distantly supervised extraction models which assign named entity mentions in text to entries in the event template. Our results indicate that joint inference over sequences of candidate entity mentions is beneficial. Furthermore, we demonstrate that the Searn algorithm outperforms a linear-chain CRF and strong baselines with local inference.

2013

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Breaking Out of Local Optima with Count Transforms and Model Recombination: A Study in Grammar Induction
Valentin I. Spitkovsky | Hiyan Alshawi | Daniel Jurafsky
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Tradition and Modernity in 20th Century Chinese Poetry
Rob Voigt | Dan Jurafsky
Proceedings of the Workshop on Computational Linguistics for Literature

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Positive Diversity Tuning for Machine Translation System Combination
Daniel Cer | Christopher D. Manning | Dan Jurafsky
Proceedings of the Eighth Workshop on Statistical Machine Translation

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A computational approach to politeness with application to social factors
Cristian Danescu-Niculescu-Mizil | Moritz Sudhof | Dan Jurafsky | Jure Leskovec | Christopher Potts
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Linguistic Models for Analyzing and Detecting Biased Language
Marta Recasens | Cristian Danescu-Niculescu-Mizil | Dan Jurafsky
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Implicatures and Nested Beliefs in Approximate Decentralized-POMDPs
Adam Vogel | Christopher Potts | Dan Jurafsky
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Generating Recommendation Dialogs by Extracting Information from User Reviews
Kevin Reschke | Adam Vogel | Dan Jurafsky
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules
Heeyoung Lee | Angel Chang | Yves Peirsman | Nathanael Chambers | Mihai Surdeanu | Dan Jurafsky
Computational Linguistics, Volume 39, Issue 4 - December 2013

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Same Referent, Different Words: Unsupervised Mining of Opaque Coreferent Mentions
Marta Recasens | Matthew Can | Daniel Jurafsky
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Emergence of Gricean Maxims from Multi-Agent Decision Theory
Adam Vogel | Max Bodoia | Christopher Potts | Daniel Jurafsky
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Parsing Time: Learning to Interpret Time Expressions
Gabor Angeli | Christopher Manning | Daniel Jurafsky
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Capitalization Cues Improve Dependency Grammar Induction
Valentin I. Spitkovsky | Hiyan Alshawi | Daniel Jurafsky
Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure

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A Computational Analysis of Style, Affect, and Imagery in Contemporary Poetry
Justine Kao | Dan Jurafsky
Proceedings of the NAACL-HLT 2012 Workshop on Computational Linguistics for Literature

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Towards a Literary Machine Translation: The Role of Referential Cohesion
Rob Voigt | Dan Jurafsky
Proceedings of the NAACL-HLT 2012 Workshop on Computational Linguistics for Literature

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Towards a Computational History of the ACL: 1980-2008
Ashton Anderson | Dan Jurafsky | Daniel A. McFarland
Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries

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He Said, She Said: Gender in the ACL Anthology
Adam Vogel | Dan Jurafsky
Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries

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Joint Entity and Event Coreference Resolution across Documents
Heeyoung Lee | Marta Recasens | Angel Chang | Mihai Surdeanu | Dan Jurafsky
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Three Dependency-and-Boundary Models for Grammar Induction
Valentin I. Spitkovsky | Hiyan Alshawi | Daniel Jurafsky
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Lateen EM: Unsupervised Training with Multiple Objectives, Applied to Dependency Grammar Induction
Valentin I. Spitkovsky | Hiyan Alshawi | Daniel Jurafsky
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Unsupervised Dependency Parsing without Gold Part-of-Speech Tags
Valentin I. Spitkovsky | Hiyan Alshawi | Angel X. Chang | Daniel Jurafsky
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Using Query Patterns to Learn the Duration of Events
Andrey Gusev | Nathanael Chambers | Divye Raj Khilnani | Pranav Khaitan | Steven Bethard | Dan Jurafsky
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

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Punctuation: Making a Point in Unsupervised Dependency Parsing
Valentin I. Spitkovsky | Hiyan Alshawi | Daniel Jurafsky
Proceedings of the Fifteenth Conference on Computational Natural Language Learning

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A Study of Academic Collaborations in Computational Linguistics using a Latent Mixture of Authors Model
Nikhil Johri | Daniel Ramage | Daniel McFarland | Daniel Jurafsky
Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities

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Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task
Heeyoung Lee | Yves Peirsman | Angel Chang | Nathanael Chambers | Mihai Surdeanu | Dan Jurafsky
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

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Template-Based Information Extraction without the Templates
Nathanael Chambers | Dan Jurafsky
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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The Best Lexical Metric for Phrase-Based Statistical MT System Optimization
Daniel Cer | Christopher D. Manning | Daniel Jurafsky
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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From Baby Steps to Leapfrog: How “Less is More” in Unsupervised Dependency Parsing
Valentin I. Spitkovsky | Hiyan Alshawi | Daniel Jurafsky
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Phrasal: A Statistical Machine Translation Toolkit for Exploring New Model Features
Daniel Cer | Michel Galley | Daniel Jurafsky | Christopher D. Manning
Proceedings of the NAACL HLT 2010 Demonstration Session

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Viterbi Training Improves Unsupervised Dependency Parsing
Valentin I. Spitkovsky | Hiyan Alshawi | Daniel Jurafsky | Christopher D. Manning
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

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Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)
Chu-Ren Huang | Dan Jurafsky
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Coling 2010: Posters
Chu-Ren Huang | Dan Jurafsky
Coling 2010: Posters

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Improving the Use of Pseudo-Words for Evaluating Selectional Preferences
Nathanael Chambers | Daniel Jurafsky
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Learning to Follow Navigational Directions
Adam Vogel | Daniel Jurafsky
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Profiting from Mark-Up: Hyper-Text Annotations for Guided Parsing
Valentin I. Spitkovsky | Daniel Jurafsky | Hiyan Alshawi
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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A Database of Narrative Schemas
Nathanael Chambers | Dan Jurafsky
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper describes a new language resource of events and semantic roles that characterize real-world situations. Narrative schemas contain sets of related events (edit and publish), a temporal ordering of the events (edit before publish), and the semantic roles of the participants (authors publish books). This type of world knowledge was central to early research in natural language understanding, scripts being one of the main formalisms, they represented common sequences of events that occur in the world. Unfortunately, most of this knowledge was hand-coded and time consuming to create. Current machine learning techniques, as well as a new approach to learning through coreference chains, has allowed us to automatically extract rich event structure from open domain text in the form of narrative schemas. The narrative schema resource described in this paper contains approximately 5000 unique events combined into schemas of varying sizes. We describe the resource, how it is learned, and a new evaluation of the coverage of these schemas over unseen documents.

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Parsing to Stanford Dependencies: Trade-offs between Speed and Accuracy
Daniel Cer | Marie-Catherine de Marneffe | Dan Jurafsky | Chris Manning
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We investigate a number of approaches to generating Stanford Dependencies, a widely used semantically-oriented dependency representation. We examine algorithms specifically designed for dependency parsing (Nivre, Nivre Eager, Covington, Eisner, and RelEx) as well as dependencies extracted from constituent parse trees created by phrase structure parsers (Charniak, Charniak-Johnson, Bikel, Berkeley and Stanford). We found that constituent parsers systematically outperform algorithms designed specifically for dependency parsing. The most accurate method for generating dependencies is the Charniak-Johnson reranking parser, with 89% (labeled) attachment F1 score. The fastest methods are Nivre, Nivre Eager, and Covington, used with a linear classifier to make local parsing decisions, which can parse the entire Penn Treebank development set (section 22) in less than 10 seconds on an Intel Xeon E5520. However, this speed comes with a substantial drop in F1 score (about 76% for labeled attachment) compared to competing methods. By tuning how much of the search space is explored by the Charniak-Johnson parser, we are able to arrive at a balanced configuration that is both fast and nearly as good as the most accurate approaches.

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A Multi-Pass Sieve for Coreference Resolution
Karthik Raghunathan | Heeyoung Lee | Sudarshan Rangarajan | Nathanael Chambers | Mihai Surdeanu | Dan Jurafsky | Christopher Manning
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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It’s Not You, it’s Me: Detecting Flirting and its Misperception in Speed-Dates
Rajesh Ranganath | Dan Jurafsky | Dan McFarland
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Machine Translation Evaluation with Textual Entailment Features
Sebastian Padó | Michel Galley | Daniel Jurafsky | Christopher D. Manning
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Disambiguating “DE” for Chinese-English Machine Translation
Pi-Chuan Chang | Daniel Jurafsky | Christopher D. Manning
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Discriminative Reordering with Chinese Grammatical Relations Features
Pi-Chuan Chang | Huihsin Tseng | Dan Jurafsky | Christopher D. Manning
Proceedings of the Third Workshop on Syntax and Structure in Statistical Translation (SSST-3) at NAACL HLT 2009

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Robust Machine Translation Evaluation with Entailment Features
Sebastian Padó | Michel Galley | Dan Jurafsky | Christopher D. Manning
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Unsupervised Learning of Narrative Schemas and their Participants
Nathanael Chambers | Dan Jurafsky
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Distant supervision for relation extraction without labeled data
Mike Mintz | Steven Bills | Rion Snow | Daniel Jurafsky
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation
Dan Jurafsky | Rajesh Ranganath | Dan McFarland
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2008

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Which Words Are Hard to Recognize? Prosodic, Lexical, and Disfluency Factors that Increase ASR Error Rates
Sharon Goldwater | Dan Jurafsky | Christopher D. Manning
Proceedings of ACL-08: HLT

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Unsupervised Learning of Narrative Event Chains
Nathanael Chambers | Dan Jurafsky
Proceedings of ACL-08: HLT

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Regularization and Search for Minimum Error Rate Training
Daniel Cer | Dan Jurafsky | Christopher D. Manning
Proceedings of the Third Workshop on Statistical Machine Translation

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Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks
Rion Snow | Brendan O’Connor | Daniel Jurafsky | Andrew Ng
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Studying the History of Ideas Using Topic Models
David Hall | Daniel Jurafsky | Christopher D. Manning
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Jointly Combining Implicit Constraints Improves Temporal Ordering
Nathanael Chambers | Daniel Jurafsky
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

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To Memorize or to Predict: Prominence labeling in Conversational Speech
Ani Nenkova | Jason Brenier | Anubha Kothari | Sasha Calhoun | Laura Whitton | David Beaver | Dan Jurafsky
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Resolving “You” in Multi-Party Dialog
Surabhi Gupta | John Niekrasz | Matthew Purver | Dan Jurafsky
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

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Disambiguating Between Generic and Referential “You” in Dialog
Surabhi Gupta | Matthew Purver | Dan Jurafsky
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

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Classifying Temporal Relations Between Events
Nathanael Chambers | Shan Wang | Dan Jurafsky
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

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Measuring Importance and Query Relevance in Topic-focused Multi-document Summarization
Surabhi Gupta | Ani Nenkova | Dan Jurafsky
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

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Learning to Merge Word Senses
Rion Snow | Sushant Prakash | Daniel Jurafsky | Andrew Y. Ng
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Semantic Taxonomy Induction from Heterogenous Evidence
Rion Snow | Daniel Jurafsky | Andrew Y. Ng
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Dan Jurafsky | Eric Gaussier
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

2005

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Morphological features help POS tagging of unknown words across language varieties
Huihsin Tseng | Daniel Jurafsky | Christopher Manning
Proceedings of the Fourth SIGHAN Workshop on Chinese Language Processing

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A Conditional Random Field Word Segmenter for Sighan Bakeoff 2005
Huihsin Tseng | Pichuan Chang | Galen Andrew | Daniel Jurafsky | Christopher Manning
Proceedings of the Fourth SIGHAN Workshop on Chinese Language Processing

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Semantic Role Labeling Using Different Syntactic Views
Sameer Pradhan | Wayne Ward | Kadri Hacioglu | James Martin | Daniel Jurafsky
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

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Semantic Role Chunking Combining Complementary Syntactic Views
Sameer Pradhan | Kadri Hacioglu | Wayne Ward | James H. Martin | Daniel Jurafsky
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

2004

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Shallow Semantic Parsing using Support Vector Machines
Sameer S. Pradhan | Wayne H. Ward | Kadri Hacioglu | James H. Martin | Dan Jurafsky
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004

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Shallow Semantic Parsing of Chinese
Honglin Sun | Daniel Jurafsky
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004

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Parsing Arguments of Nominalizations in English and Chinese
Sameer Pradhan | Honglin Sun | Wayne Ward | James H. Martin | Daniel Jurafsky
Proceedings of HLT-NAACL 2004: Short Papers

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Automatic Tagging of Arabic Text: From Raw Text to Base Phrase Chunks
Mona Diab | Kadri Hacioglu | Daniel Jurafsky
Proceedings of HLT-NAACL 2004: Short Papers

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Semantic Role Labeling by Tagging Syntactic Chunks
Kadri Hacioglu | Sameer Pradhan | Wayne Ward | James H. Martin | Daniel Jurafsky
Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004

2003

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The Effect of Rhythm on Structural Disambiguation in Chinese
Honglin Sun | Dan Jurafsky
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing

2002

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Automatic Labeling of Semantic Roles
Daniel Gildea | Daniel Jurafsky
Computational Linguistics, Volume 28, Number 3, September 2002

2001

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Knowledge-Free Induction of Inflectional Morphologies
Patrick Schone | Daniel Jurafsky
Second Meeting of the North American Chapter of the Association for Computational Linguistics

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Is Knowledge-Free Induction of Multiword Unit Dictionary Headwords a Solved Problem?
Patrick Schone | Daniel Jurafsky
Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing

2000

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Dialogue act modeling for automatic tagging and recognition of conversational speech
Andreas Stolcke | Klaus Ries | Noah Coccaro | Elizabeth Shriberg | Rebecca Bates | Daniel Jurafsky | Paul Taylor | Rachel Martin | Carol Van Ess-Dykema | Marie Meteer
Computational Linguistics, Volume 26, Number 3, September 2000

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Automatic Labeling of Semantic Roles
Daniel Gildea | Daniel Jurafsky
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics

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Knowledge-Free Induction of Morphology Using Latent Semantic Analysis
Patrick Schone | Daniel Jurafsky
Fourth Conference on Computational Natural Language Learning and the Second Learning Language in Logic Workshop

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Verb Subcategorization Frequency Differences between Business- News and Balanced Corpora: The Role of Verb Sense
Douglas Roland | Daniel Jurafsky | Lise Menn | Susanne Gahl | Elezabeth Elder | Chris Riddoch
The Workshop on Comparing Corpora

1998

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How Verb Subcategorization Frequencies Are Affected By Corpus Choice
Douglas Roland | Daniel Jurafsky
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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How Verb Subcategorization Frequencies are Affected by Corpus Choice
Douglas Roland | Daniel Jurafsky
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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Lexical, Prosodic, and Syntactic Cues for Dialog Acts
Daniel Jurafsky | Elizabeth Shriberg | Barbara Fox | Traci Curl
Discourse Relations and Discourse Markers

1996

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Learning Bias and Phonological-Rule Induction
Daniel Gildea | Daniel Jurafsky
Computational Linguistics, Volume 22, Number 4, December 1996

1995

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Learning Phonological Rule Probabilities from Speech Corpora with Exploratory Computational Phonology
Gary Tajchman | Daniel Jurafsky | Eric Fosler
33rd Annual Meeting of the Association for Computational Linguistics

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Automatic Induction of Finite State Transducers for Simple Phonological Rules
Daniel Gildea | Daniel Jurafsky
33rd Annual Meeting of the Association for Computational Linguistics

1990

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Representing and Integrating Linguistic Knowledge
Daniel Jurafsky
COLING 1990 Volume 2: Papers presented to the 13th International Conference on Computational Linguistics

1988

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Issues in Relating Syntax and Semantics
Daniel Jurafsky
Coling Budapest 1988 Volume 1: International Conference on Computational Linguistics

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