Dirk Hovy


2020

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“You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases
Dirk Hovy | Federico Bianchi | Tommaso Fornaciari
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The main goal of machine translation has been to convey the correct content. Stylistic considerations have been at best secondary. We show that as a consequence, the output of three commercial machine translation systems (Bing, DeepL, Google) make demographically diverse samples from five languages “sound” older and more male than the original. Our findings suggest that translation models reflect demographic bias in the training data. This opens up interesting new research avenues in machine translation to take stylistic considerations into account.

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Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview
Deven Santosh Shah | H. Andrew Schwartz | Dirk Hovy
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted individually, without a unifying framework to organize efforts within the field. This situation leads to repetitive approaches, and focuses overly on bias symptoms/effects, rather than on their origins, which could limit the development of effective countermeasures. In this paper, we propose a unifying predictive bias framework for NLP. We summarize the NLP literature and suggest general mathematical definitions of predictive bias. We differentiate two consequences of bias: outcome disparities and error disparities, as well as four potential origins of biases: label bias, selection bias, model overamplification, and semantic bias. Our framework serves as an overview of predictive bias in NLP, integrating existing work into a single structure, and providing a conceptual baseline for improved frameworks.

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Integrating Ethics into the NLP Curriculum
Emily M. Bender | Dirk Hovy | Alexandra Schofield
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

To raise awareness among future NLP practitioners and prevent inertia in the field, we need to place ethics in the curriculum for all NLP students—not as an elective, but as a core part of their education. Our goal in this tutorial is to empower NLP researchers and practitioners with tools and resources to teach others about how to ethically apply NLP techniques. We will present both high-level strategies for developing an ethics-oriented curriculum, based on experience and best practices, as well as specific sample exercises that can be brought to a classroom. This highly interactive work session will culminate in a shared online resource page that pools lesson plans, assignments, exercise ideas, reading suggestions, and ideas from the attendees. Though the tutorial will focus particularly on examples for university classrooms, we believe these ideas can extend to company-internal workshops or tutorials in a variety of organizations. In this setting, a key lesson is that there is no single approach to ethical NLP: each project requires thoughtful consideration about what steps can be taken to best support people affected by that project. However, we can learn (and teach) what issues to be aware of, what questions to ask, and what strategies are available to mitigate harm.

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Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success
Farzana Rashid | Tommaso Fornaciari | Dirk Hovy | Eduardo Blanco | Fernando Vega-Redondo
Findings of the Association for Computational Linguistics: EMNLP 2020

When interacting with each other, we motivate, advise, inform, show love or power towards our peers. However, the way we interact may also hold some indication on how successful we are, as people often try to help each other to achieve their goals. We study the chat interactions of thousands of aspiring entrepreneurs who discuss and develop business models. We manually annotate a set of about 5,500 chat interactions with four dimensions of interaction styles (motivation, cooperation, equality, advice). We find that these styles can be reliably predicted, and that the communication styles can be used to predict a number of indices of business success. Our findings indicate that successful communicators are also successful in other domains.

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A Report on the VarDial Evaluation Campaign 2020
Mihaela Gaman | Dirk Hovy | Radu Tudor Ionescu | Heidi Jauhiainen | Tommi Jauhiainen | Krister Lindén | Nikola Ljubešić | Niko Partanen | Christoph Purschke | Yves Scherrer | Marcos Zampieri
Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects

This paper presents the results of the VarDial Evaluation Campaign 2020 organized as part of the seventh workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with COLING 2020. The campaign included three shared tasks each focusing on a different challenge of language and dialect identification: Romanian Dialect Identification (RDI), Social Media Variety Geolocation (SMG), and Uralic Language Identification (ULI). The campaign attracted 30 teams who enrolled to participate in one or multiple shared tasks and 14 of them submitted runs across the three shared tasks. Finally, 11 papers describing participating systems are published in the VarDial proceedings and referred to in this report.

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Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
David Bamman | Dirk Hovy | David Jurgens | Brendan O'Connor | Svitlana Volkova
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

2019

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Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers
Hanh Nguyen | Dirk Hovy
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

User reviews provide a significant source of information for companies to understand their market and audience. In order to discover broad trends in this source, researchers have typically used topic models such as Latent Dirichlet Allocation (LDA). However, while there are metrics to choose the “best” number of topics, it is not clear whether the resulting topics can also provide in-depth, actionable product analysis. Our paper examines this issue by analyzing user reviews from the Best Buy US website for smart speakers. Using coherence scores to choose topics, we test whether the results help us to understand user interests and concerns. We find that while coherence scores are a good starting point to identify a number of topics, it still requires manual adaptation based on domain knowledge to provide market insights. We show that the resulting dimensions capture brand performance and differences, and differentiate the market into two distinct groups with different properties.

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Geolocation with Attention-Based Multitask Learning Models
Tommaso Fornaciari | Dirk Hovy
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

Geolocation, predicting the location of a post based on text and other information, has a huge potential for several social media applications. Typically, the problem is modeled as either multi-class classification or regression. In the first case, the classes are geographic areas previously identified; in the second, the models directly predict geographic coordinates. The former requires discretization of the coordinates, but yields better performance. The latter is potentially more precise and true to the nature of the problem, but often results in worse performance. We propose to combine the two approaches in an attentionbased multitask convolutional neural network that jointly predicts both discrete locations and continuous geographic coordinates. We evaluate the multi-task (MTL) model against singletask models and prior work. We find that MTL significantly improves performance, reporting large gains on one data set, but also note that the correlation between labels and coordinates has a marked impact on the effectiveness of including a regression task.

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Dense Node Representation for Geolocation
Tommaso Fornaciari | Dirk Hovy
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

Prior research has shown that geolocation can be substantially improved by including user network information. While effective, it suffers from the curse of dimensionality, since networks are usually represented as sparse adjacency matrices of connections, which grow exponentially with the number of users. In order to incorporate this information, we therefore need to limit the network size, in turn limiting performance and risking sample bias. In this paper, we address these limitations by instead using dense network representations. We explore two methods to learn continuous node representations from either 1) the network structure with node2vec (Grover and Leskovec, 2016), or 2) textual user mentions via doc2vec (Le and Mikolov, 2014). We combine both methods with input from social media posts in an attention-based convolutional neural network and evaluate the contribution of each component on geolocation performance. Our method enables us to incorporate arbitrarily large networks in a fixed-length vector, without limiting the network size. Our models achieve competitive results with similar state-of-the-art methods, but with much fewer model parameters, while being applicable to networks of virtually any size.

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Identifying Linguistic Areas for Geolocation
Tommaso Fornaciari | Dirk Hovy
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

Geolocating social media posts relies on the assumption that language carries sufficient geographic information. However, locations are usually given as continuous latitude/longitude tuples, so we first need to define discrete geographic regions that can serve as labels. Most studies use some form of clustering to discretize the continuous coordinates (Han et al., 2016). However, the resulting regions do not always correspond to existing linguistic areas. Consequently, accuracy at 100 miles tends to be good, but degrades for finer-grained distinctions, when different linguistic regions get lumped together. We describe a new algorithm, Point-to-City (P2C), an iterative k-d tree-based method for clustering geographic coordinates and associating them with towns. We create three sets of labels at different levels of granularity, and compare performance of a state-of-the-art geolocation model trained and tested with P2C labels to one with regular k-d tree labels. Even though P2C results in substantially more labels than the baseline, model accuracy increases significantly over using traditional labels at the fine-grained level, while staying comparable at 100 miles. The results suggest that identifying meaningful linguistic areas is crucial for improving geolocation at a fine-grained level.

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Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP
Silviu Paun | Dirk Hovy
Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP

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Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing
Aparna Garimella | Carmen Banea | Dirk Hovy | Rada Mihalcea
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for these differences. To address this, we annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles’ authors, and build taggers and parsers trained on this data that show performance differences in text written by men and women. Further analyses reveal numerous part-of-speech tags and syntactic relations whose prediction performances benefit from the prevalence of a specific gender in the training data. The results underscore the importance of accounting for gendered differences in syntactic tasks, and outline future venues for developing more accurate taggers and parsers. We release our data to the research community.

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Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
Svitlana Volkova | David Jurgens | Dirk Hovy | David Bamman | Oren Tsur
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science

2018

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Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning
Sotiris Lamprinidis | Daniel Hardt | Dirk Hovy
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Newspapers need to attract readers with headlines, anticipating their readers’ preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. We find that pre-trained word embeddings provide significant improvements over untrained embeddings, as do the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging. However, we also find that performance is very similar to that of a simple Logistic Regression model over character n-grams. Feature analysis reveals structural patterns of headline popularity, including the use of forward-looking deictic expressions and second person pronouns.

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Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information
Dirk Hovy | Tommaso Fornaciari
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Most text-classification approaches represent the input based on textual features, either feature-based or continuous. However, this ignores strong non-linguistic similarities like homophily: people within a demographic group use language more similar to each other than to non-group members. We use homophily cues to retrofit text-based author representations with non-linguistic information, and introduce a trade-off parameter. This approach increases in-class similarity between authors, and improves classification performance by making classes more linearly separable. We evaluate the effect of our method on two author-attribute prediction tasks with various training-set sizes and parameter settings. We find that our method can significantly improve classification performance, especially when the number of labels is large and limited labeled data is available. It is potentially applicable as preprocessing step to any text-classification task.

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Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting
Dirk Hovy | Christoph Purschke
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. In most languages, dialects exist along a continuum, and are commonly discretized by combining the extent of several preselected linguistic variables. However, the selection of these variables is theory-driven and itself insensitive to change. We use Doc2Vec on a corpus of 16.8M anonymous online posts in the German-speaking area to learn continuous document representations of cities. These representations capture continuous regional linguistic distinctions, and can serve as input to downstream NLP tasks sensitive to regional variation. By incorporating geographic information via retrofitting and agglomerative clustering with structure, we recover dialect areas at various levels of granularity. Evaluating these clusters against an existing dialect map, we achieve a match of up to 0.77 V-score (harmonic mean of cluster completeness and homogeneity). Our results show that representation learning with retrofitting offers a robust general method to automatically expose dialectal differences and regional variation at a finer granularity than was previously possible.

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Comparing Bayesian Models of Annotation
Silviu Paun | Bob Carpenter | Jon Chamberlain | Dirk Hovy | Udo Kruschwitz | Massimo Poesio
Transactions of the Association for Computational Linguistics, Volume 6

The analysis of crowdsourced annotations in natural language processing is concerned with identifying (1) gold standard labels, (2) annotator accuracies and biases, and (3) item difficulties and error patterns. Traditionally, majority voting was used for 1, and coefficients of agreement for 2 and 3. Lately, model-based analysis of corpus annotations have proven better at all three tasks. But there has been relatively little work comparing them on the same datasets. This paper aims to fill this gap by analyzing six models of annotation, covering different approaches to annotator ability, item difficulty, and parameter pooling (tying) across annotators and items. We evaluate these models along four aspects: comparison to gold labels, predictive accuracy for new annotations, annotator characterization, and item difficulty, using four datasets with varying degrees of noise in the form of random (spammy) annotators. We conclude with guidelines for model selection, application, and implementation.

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Proceedings of the Second ACL Workshop on Ethics in Natural Language Processing
Mark Alfano | Dirk Hovy | Margaret Mitchell | Michael Strube
Proceedings of the Second ACL Workshop on Ethics in Natural Language Processing

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The Social and the Neural Network: How to Make Natural Language Processing about People again
Dirk Hovy
Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media

Over the years, natural language processing has increasingly focused on tasks that can be solved by statistical models, but ignored the social aspects of language. These limitations are in large part due to historically available data and the limitations of the models, but have narrowed our focus and biased the tools demographically. However, with the increased availability of data sets including socio-demographic information and more expressive (neural) models, we have the opportunity to address both issues. I argue that this combination can broaden the focus of NLP to solve a whole new range of tasks, enable us to generate novel linguistic insights, and provide fairer tools for everyone.

2017

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Multitask Learning for Mental Health Conditions with Limited Social Media Data
Adrian Benton | Margaret Mitchell | Dirk Hovy
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Language contains information about the author’s demographic attributes as well as their mental state, and has been successfully leveraged in NLP to predict either one alone. However, demographic attributes and mental states also interact with each other, and we are the first to demonstrate how to use them jointly to improve the prediction of mental health conditions across the board. We model the different conditions as tasks in a multitask learning (MTL) framework, and establish for the first time the potential of deep learning in the prediction of mental health from online user-generated text. The framework we propose significantly improves over all baselines and single-task models for predicting mental health conditions, with particularly significant gains for conditions with limited data. In addition, our best MTL model can predict the presence of conditions (neuroatypicality) more generally, further reducing the error of the strong feed-forward baseline.

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Proceedings of the First ACL Workshop on Ethics in Natural Language Processing
Dirk Hovy | Shannon Spruit | Margaret Mitchell | Emily M. Bender | Michael Strube | Hanna Wallach
Proceedings of the First ACL Workshop on Ethics in Natural Language Processing

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Proceedings of the Second Workshop on NLP and Computational Social Science
Dirk Hovy | Svitlana Volkova | David Bamman | David Jurgens | Brendan O’Connor | Oren Tsur | A. Seza Doğruöz
Proceedings of the Second Workshop on NLP and Computational Social Science

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Proceedings of the First Workshop on Abusive Language Online
Zeerak Waseem | Wendy Hui Kyong Chung | Dirk Hovy | Joel Tetreault
Proceedings of the First Workshop on Abusive Language Online

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Huntsville, hospitals, and hockey teams: Names can reveal your location
Bahar Salehi | Dirk Hovy | Eduard Hovy | Anders Søgaard
Proceedings of the 3rd Workshop on Noisy User-generated Text

Geolocation is the task of identifying a social media user’s primary location, and in natural language processing, there is a growing literature on to what extent automated analysis of social media posts can help. However, not all content features are equally revealing of a user’s location. In this paper, we evaluate nine name entity (NE) types. Using various metrics, we find that GEO-LOC, FACILITY and SPORT-TEAM are more informative for geolocation than other NE types. Using these types, we improve geolocation accuracy and reduce distance error over various famous text-based methods.

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End-to-End Information Extraction without Token-Level Supervision
Rasmus Berg Palm | Dirk Hovy | Florian Laws | Ole Winther
Proceedings of the Workshop on Speech-Centric Natural Language Processing

Most state-of-the-art information extraction approaches rely on token-level labels to find the areas of interest in text. Unfortunately, these labels are time-consuming and costly to create, and consequently, not available for many real-life IE tasks. To make matters worse, token-level labels are usually not the desired output, but just an intermediary step. End-to-end (E2E) models, which take raw text as input and produce the desired output directly, need not depend on token-level labels. We propose an E2E model based on pointer networks, which can be trained directly on pairs of raw input and output text. We evaluate our model on the ATIS data set, MIT restaurant corpus and the MIT movie corpus and compare to neural baselines that do use token-level labels. We achieve competitive results, within a few percentage points of the baselines, showing the feasibility of E2E information extraction without the need for token-level labels. This opens up new possibilities, as for many tasks currently addressed by human extractors, raw input and output data are available, but not token-level labels.

2016

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Exploring Language Variation Across Europe - A Web-based Tool for Computational Sociolinguistics
Dirk Hovy | Anders Johannsen
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Language varies not only between countries, but also along regional and socio-demographic lines. This variation is one of the driving factors behind language change. However, investigating language variation is a complex undertaking: the more factors we want to consider, the more data we need. Traditional qualitative methods are not well-suited to do this, an therefore restricted to isolated factors. This reduction limits the potential insights, and risks attributing undue importance to easily observed factors. While there is a large interest in linguistics to increase the quantitative aspect of such studies, it requires training in both variational linguistics and computational methods, a combination that is still not common. We take a first step here to alleviating the problem by providing an interface, www.languagevariation.com, to explore large-scale language variation along multiple socio-demographic factors – without programming knowledge. It makes use of large amounts of data and provides statistical analyses, maps, and interactive features that will enable scholars to explore language variation in a data-driven way.

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Learning a POS tagger for AAVE-like language
Anna Jørgensen | Dirk Hovy | Anders Søgaard
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
Zeerak Waseem | Dirk Hovy
Proceedings of the NAACL Student Research Workshop

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The Enemy in Your Own Camp: How Well Can We Detect Statistically-Generated Fake Reviews – An Adversarial Study
Dirk Hovy
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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The Social Impact of Natural Language Processing
Dirk Hovy | Shannon L. Spruit
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations
Gavin Abercrombie | Dirk Hovy
Proceedings of the ACL 2016 Student Research Workshop

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Proceedings of the First Workshop on NLP and Computational Social Science
David Bamman | A. Seza Doğruöz | Jacob Eisenstein | Dirk Hovy | David Jurgens | Brendan O’Connor | Alice Oh | Oren Tsur | Svitlana Volkova
Proceedings of the First Workshop on NLP and Computational Social Science

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SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)
Nathan Schneider | Dirk Hovy | Anders Johannsen | Marine Carpuat
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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The Rating Game: Sentiment Rating Reproducibility from Text
Lasse Borgholt | Peter Simonsen | Dirk Hovy
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Cross-lingual syntactic variation over age and gender
Anders Johannsen | Dirk Hovy | Anders Søgaard
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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Personality Traits on Twitter—or—How to Get 1,500 Personality Tests in a Week
Barbara Plank | Dirk Hovy
Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Challenges of studying and processing dialects in social media
Anna Jørgensen | Dirk Hovy | Anders Søgaard
Proceedings of the Workshop on Noisy User-generated Text

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Mining for unambiguous instances to adapt part-of-speech taggers to new domains
Dirk Hovy | Barbara Plank | Héctor Martínez Alonso | Anders Søgaard
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Demographic Factors Improve Classification Performance
Dirk Hovy
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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If all you have is a bit of the Bible: Learning POS taggers for truly low-resource languages
Željko Agić | Dirk Hovy | Anders Søgaard
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)

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Tagging Performance Correlates with Author Age
Dirk Hovy | Anders Søgaard
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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More or less supervised supersense tagging of Twitter
Anders Johannsen | Dirk Hovy | Héctor Martínez Alonso | Barbara Plank | Anders Søgaard
Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)

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Copenhagen-Malmö: Tree Approximations of Semantic Parsing Problems
Natalie Schluter | Anders Søgaard | Jakob Elming | Dirk Hovy | Barbara Plank | Héctor Martínez Alonso | Anders Johanssen | Sigrid Klerke
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Experiments with crowdsourced re-annotation of a POS tagging data set
Dirk Hovy | Barbara Plank | Anders Søgaard
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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How Well can We Learn Interpretable Entity Types from Text?
Dirk Hovy
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Linguistically debatable or just plain wrong?
Barbara Plank | Dirk Hovy | Anders Søgaard
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Learning part-of-speech taggers with inter-annotator agreement loss
Barbara Plank | Dirk Hovy | Anders Søgaard
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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What’s in a p-value in NLP?
Anders Søgaard | Anders Johannsen | Barbara Plank | Dirk Hovy | Hector Martínez Alonso
Proceedings of the Eighteenth Conference on Computational Natural Language Learning

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Robust Cross-Domain Sentiment Analysis for Low-Resource Languages
Jakob Elming | Barbara Plank | Dirk Hovy
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Adapting taggers to Twitter with not-so-distant supervision
Barbara Plank | Dirk Hovy | Ryan McDonald | Anders Søgaard
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Selection Bias, Label Bias, and Bias in Ground Truth
Anders Søgaard | Barbara Plank | Dirk Hovy
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Tutorial Abstracts

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Augmenting English Adjective Senses with Supersenses
Yulia Tsvetkov | Nathan Schneider | Dirk Hovy | Archna Bhatia | Manaal Faruqui | Chris Dyer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification. Results show that accuracy for automatic adjective type classification is high, but synsets are considerably more difficult to classify, even for trained human annotators. We release the manually annotated data, the classifier, and the induced supersense labeling of 12,304 WordNet adjective synsets.

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Crowdsourcing and annotating NER for Twitter #drift
Hege Fromreide | Dirk Hovy | Anders Søgaard
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present two new NER datasets for Twitter; a manually annotated set of 1,467 tweets (kappa=0.942) and a set of 2,975 expert-corrected, crowdsourced NER annotated tweets from the dataset described in Finin et al. (2010). In our experiments with these datasets, we observe two important points: (a) language drift on Twitter is significant, and while off-the-shelf systems have been reported to perform well on in-sample data, they often perform poorly on new samples of tweets, (b) state-of-the-art performance across various datasets can be obtained from crowdsourced annotations, making it more feasible to “catch up” with language drift.

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When POS data sets don’t add up: Combatting sample bias
Dirk Hovy | Barbara Plank | Anders Søgaard
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Several works in Natural Language Processing have recently looked into part-of-speech annotation of Twitter data and typically used their own data sets. Since conventions on Twitter change rapidly, models often show sample bias. Training on a combination of the existing data sets should help overcome this bias and produce more robust models than any trained on the individual corpora. Unfortunately, combining the existing corpora proves difficult: many of the corpora use proprietary tag sets that have little or no overlap. Even when mapped to a common tag set, the different corpora systematically differ in their treatment of various tags and tokens. This includes both pre-processing decisions, as well as default labels for frequent tokens, thus exhibiting data bias and label bias, respectively. Only if we address these biases can we combine the existing data sets to also overcome sample bias. We present a systematic study of several Twitter POS data sets, the problems of label and data bias, discuss their effects on model performance, and show how to overcome them to learn models that perform well on various test sets, achieving relative error reduction of up to 21%.

2013

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A Walk-Based Semantically Enriched Tree Kernel Over Distributed Word Representations
Shashank Srivastava | Dirk Hovy | Eduard Hovy
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Identifying Metaphorical Word Use with Tree Kernels
Dirk Hovy | Shashank Srivastava | Sujay Kumar Jauhar | Mrinmaya Sachan | Kartik Goyal | Huying Li | Whitney Sanders | Eduard Hovy
Proceedings of the First Workshop on Metaphor in NLP

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Learning Whom to Trust with MACE
Dirk Hovy | Taylor Berg-Kirkpatrick | Ashish Vaswani | Eduard Hovy
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Exploiting Partial Annotations with EM Training
Dirk Hovy | Eduard Hovy
Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure

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When Did that Happen? — Linking Events and Relations to Timestamps
Dirk Hovy | James Fan | Alfio Gliozzo | Siddharth Patwardhan | Christopher Welty
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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Unsupervised Mining of Lexical Variants from Noisy Text
Stephan Gouws | Dirk Hovy | Donald Metzler
Proceedings of the First workshop on Unsupervised Learning in NLP

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Unsupervised Discovery of Domain-Specific Knowledge from Text
Dirk Hovy | Chunliang Zhang | Eduard Hovy | Anselmo Peñas
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Models and Training for Unsupervised Preposition Sense Disambiguation
Dirk Hovy | Ashish Vaswani | Stephen Tratz | David Chiang | Eduard Hovy
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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What’s in a Preposition? Dimensions of Sense Disambiguation for an Interesting Word Class
Dirk Hovy | Stephen Tratz | Eduard Hovy
Coling 2010: Posters

2009

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Disambiguation of Preposition Sense Using Linguistically Motivated Features
Stephen Tratz | Dirk Hovy
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium

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