Eduardo Blanco


2020

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Beyond Possession Existence: Duration and Co-Possession
Dhivya Chinnappa | Srikala Murugan | Eduardo Blanco
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper introduces two tasks: determining (a) the duration of possession relations and (b) co-possessions, i.e., whether multiple possessors possess a possessee at the same time. We present new annotations on top of corpora annotating possession existence and experimental results. Regarding possession duration, we derive the time spans we work with empirically from annotations indicating lower and upper bounds. Regarding co-possessions, we use a binary label. Cohen’s kappa coefficients indicate substantial agreement, and experimental results show that text is more useful than the image for solving these tasks.

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Predicting the Focus of Negation: Model and Error Analysis
Md Mosharaf Hossain | Kathleen Hamilton | Alexis Palmer | Eduardo Blanco
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.

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WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts
Dhivya Chinnappa | Alexis Palmer | Eduardo Blanco
Proceedings of the 12th Language Resources and Evaluation Conference

This paper presents WikiPossessions, a new benchmark corpus for the task of temporally-oriented possession (TOP), or tracking objects as they change hands over time. We annotate Wikipedia articles for 90 different well-known artifacts paintings, diamonds, and archaeological artifacts), producing 799 artifact-possessor relations with associated attributes. For each article, we also produce a full possession timeline. The full version of the task combines straightforward entity-relation extraction with complex temporal reasoning, as well as verification of textual support for the relevant types of knowledge. Specifically, to complete the full TOP task for a given article, a system must do the following: a) identify possessors; b) anchor possessors to times/events; c) identify temporal relations between each temporal anchor and the possession relation it corresponds to; d) assign certainty scores to each possessor and each temporal relation; and e) assemble individual possession events into a global possession timeline. In addition to the corpus, we release evaluation scripts and a baseline model for the task.

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Detecting Negation Cues and Scopes in Spanish
Salud María Jiménez-Zafra | Roser Morante | Eduardo Blanco | María Teresa Martín Valdivia | L. Alfonso Ureña López
Proceedings of the 12th Language Resources and Evaluation Conference

In this work we address the processing of negation in Spanish. We first present a machine learning system that processes negation in Spanish. Specifically, we focus on two tasks: i) negation cue detection and ii) scope identification. The corpus used in the experimental framework is the SFU Corpus. The results for cue detection outperform state-of-the-art results, whereas for scope detection this is the first system that performs the task for Spanish. Moreover, we provide a qualitative error analysis aimed at understanding the limitations of the system and showing which negation cues and scopes are straightforward to predict automatically, and which ones are challenging.

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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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It’s not a Non-Issue: Negation as a Source of Error in Machine Translation
Md Mosharaf Hossain | Antonios Anastasopoulos | Eduardo Blanco | Alexis Palmer
Findings of the Association for Computational Linguistics: EMNLP 2020

As machine translation (MT) systems progress at a rapid pace, questions of their adequacy linger. In this study we focus on negation, a universal, core property of human language that significantly affects the semantics of an utterance. We investigate whether translating negation is an issue for modern MT systems using 17 translation directions as test bed. Through thorough analysis, we find that indeed the presence of negation can significantly impact downstream quality, in some cases resulting in quality reductions of more than 60%. We also provide a linguistically motivated analysis that directly explains the majority of our findings. We release our annotations and code to replicate our analysis here: https://github.com/mosharafhossain/negation-mt.

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Determining Event Outcomes: The Case of #fail
Srikala Murugan | Dhivya Chinnappa | Eduardo Blanco
Findings of the Association for Computational Linguistics: EMNLP 2020

This paper targets the task of determining event outcomes in social media. We work with tweets containing either #cookingFail or #bakingFail, and show that many of the events described in them resulted in something edible. Tweets that contain images are more likely to result in edible albeit imperfect outcomes. Experimental results show that edibility is easier to predict than outcome quality.

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Extracting Adherence Information from Electronic Health Records
Jordan Sanders | Meghana Gudala | Kathleen Hamilton | Nishtha Prasad | Jordan Stovall | Eduardo Blanco | Jane E Hamilton | Kirk Roberts
Proceedings of the 28th International Conference on Computational Linguistics

Patient adherence is a critical factor in health outcomes. We present a framework to extract adherence information from electronic health records, including both sentence-level information indicating general adherence information (full, partial, none, etc.) and span-level information providing additional information such as adherence type (medication or nonmedication), reasons and outcomes. We annotate and make publicly available a new corpus of 3,000 de-identified sentences, and discuss the language physicians use to document adherence information. We also explore models based on state-of-the-art transformers to automate both tasks.

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An Analysis of Natural Language Inference Benchmarks through the Lens of Negation
Md Mosharaf Hossain | Venelin Kovatchev | Pranoy Dutta | Tiffany Kao | Elizabeth Wei | Eduardo Blanco
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Negation is underrepresented in existing natural language inference benchmarks. Additionally, one can often ignore the few negations in existing benchmarks and still make the right inference judgments. In this paper, we present a new benchmark for natural language inference in which negation plays a critical role. We also show that state-of-the-art transformers struggle making inference judgments with the new pairs.

2019

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Extracting Possessions from Social Media: Images Complement Language
Dhivya Chinnappa | Srikala Murugan | Eduardo Blanco
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

This paper describes a new dataset and experiments to determine whether authors of tweets possess the objects they tweet about. We work with 5,000 tweets and show that both humans and neural networks benefit from images in addition to text. We also introduce a simple yet effective strategy to incorporate visual information into any neural network beyond weights from pretrained networks. Specifically, we consider the tags identified in an image as an additional textual input, and leverage pretrained word embeddings as usually done with regular text. Experimental results show this novel strategy is beneficial.

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Incorporating Emoji Descriptions Improves Tweet Classification
Abhishek Singh | Eduardo Blanco | Wei Jin
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)

Tweets are short messages that often include specialized language such as hashtags and emojis. In this paper, we present a simple strategy to process emojis: replace them with their natural language description and use pretrained word embeddings as normally done with standard words. We show that this strategy is more effective than using pretrained emoji embeddings for tweet classification. Specifically, we obtain new state-of-the-art results in irony detection and sentiment analysis despite our neural network is simpler than previous proposals.

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A Corpus of Negations and their Underlying Positive Interpretations
Zahra Sarabi | Erin Killian | Eduardo Blanco | Alexis Palmer
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Negation often conveys implicit positive meaning. In this paper, we present a corpus of negations and their underlying positive interpretations. We work with negations from Simple Wikipedia, automatically generate potential positive interpretations, and then collect manual annotations that effectively rewrite the negation in positive terms. This procedure yields positive interpretations for approximately 77% of negations, and the final corpus includes over 5,700 negations and over 5,900 positive interpretations. We also present baseline results using seq2seq neural models.

2018

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Annotating Temporally-Anchored Spatial Knowledge by Leveraging Syntactic Dependencies
Alakananda Vempala | Eduardo Blanco
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Annotating If the Authors of a Tweet are Located at the Locations They Tweet About
Vivek Doudagiri | Alakananda Vempala | Eduardo Blanco
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Mining Possessions: Existence, Type and Temporal Anchors
Dhivya Chinnappa | Eduardo Blanco
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

This paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions, and assign temporal anchors indicating when the possession holds between possessor and possessee. We present new annotations for this task, and experimental results using both traditional classifiers and neural networks. Results show that the three subtasks (predicting possession existence, possession type and temporal anchors) can be automated.

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Determining Event Durations: Models and Error Analysis
Alakananda Vempala | Eduardo Blanco | Alexis Palmer
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

This paper presents models to predict event durations. We introduce aspectual features that capture deeper linguistic information than previous work, and experiment with neural networks. Our analysis shows that tense, aspect and temporal structure of the clause provide useful clues, and that an LSTM ensemble captures relevant context around the event.

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Possessors Change Over Time: A Case Study with Artworks
Dhivya Chinnappa | Eduardo Blanco
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper presents a corpus and experimental results to extract possession relations over time. We work with Wikipedia articles about artworks, and extract possession relations along with temporal information indicating when these relations are true. The annotation scheme yields many possessors over time for a given artwork, and experimental results show that an LSTM ensemble can automate the task.

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Characterizing Interactions and Relationships between People
Farzana Rashid | Eduardo Blanco
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper presents a set of dimensions to characterize the association between two people. We distinguish between interactions (when somebody refers to somebody in a conversation) and relationships (a sequence of interactions). We work with dialogue scripts from the TV show Friends, and do not impose any restrictions on the interactions and relationships. We introduce and analyze a new corpus, and present experimental results showing that the task can be automated.

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Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Eduardo Blanco | Wei Lu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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Proceedings of the Workshop on Computational Semantics beyond Events and Roles
Eduardo Blanco | Roser Morante
Proceedings of the Workshop on Computational Semantics beyond Events and Roles

2017

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Determining Whether and When People Participate in the Events They Tweet About
Krishna Chaitanya Sanagavarapu | Alakananda Vempala | Eduardo Blanco
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

This paper describes an approach to determine whether people participate in the events they tweet about. Specifically, we determine whether people are participants in events with respect to the tweet timestamp. We target all events expressed by verbs in tweets, including past, present and events that may occur in the future. We present new annotations using 1,096 event mentions, and experimental results showing that the task is challenging.

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If No Media Were Allowed inside the Venue, Was Anybody Allowed?
Zahra Sarabi | Eduardo Blanco
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

This paper presents a framework to understand negation in positive terms. Specifically, we extract positive meaning from negation when the negation cue syntactically modifies a noun or adjective. Our approach is grounded on generating potential positive interpretations automatically, and then scoring them. Experimental results show that interpretations scored high can be reliably identified.

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Proceedings of the Workshop Computational Semantics Beyond Events and Roles
Eduardo Blanco | Roser Morante | Roser Saurí
Proceedings of the Workshop Computational Semantics Beyond Events and Roles

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Dimensions of Interpersonal Relationships: Corpus and Experiments
Farzana Rashid | Eduardo Blanco
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper presents a corpus and experiments to determine dimensions of interpersonal relationships. We define a set of dimensions heavily inspired by work in social science. We create a corpus by retrieving pairs of people, and then annotating dimensions for their relationships. A corpus analysis shows that dimensions can be annotated reliably. Experimental results show that given a pair of people, values to dimensions can be assigned automatically.

2016

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Annotating Temporally-Anchored Spatial Knowledge on Top of OntoNotes Semantic Roles
Alakananda Vempala | Eduardo Blanco
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents a two-step methodology to annotate spatial knowledge on top of OntoNotes semantic roles. First, we manipulate semantic roles to automatically generate potential additional spatial knowledge. Second, we crowdsource annotations with Amazon Mechanical Turk to either validate or discard the potential additional spatial knowledge. The resulting annotations indicate whether entities are or are not located somewhere with a degree of certainty, and temporally anchor this spatial information. Crowdsourcing experiments show that the additional spatial knowledge is ubiquitous and intuitive to humans, and experimental results show that it can be inferred automatically using standard supervised machine learning techniques.

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Automatic Extraction of Implicit Interpretations from Modal Constructions
Jordan Sanders | Eduardo Blanco
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Understanding Negation in Positive Terms Using Syntactic Dependencies
Zahra Sarabi | Eduardo Blanco
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Automatic Generation and Scoring of Positive Interpretations from Negated Statements
Eduardo Blanco | Zahra Sarabi
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Beyond Plain Spatial Knowledge: Determining Where Entities Are and Are Not Located, and For How Long
Alakananda Vempala | Eduardo Blanco
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM)
Eduardo Blanco | Roser Morante | Roser Saurí
Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM)

2015

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Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics (ExProM 2015)
Eduardo Blanco | Roser Morante | Caroline Sporleder
Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics (ExProM 2015)

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Inferring Temporally-Anchored Spatial Knowledge from Semantic Roles
Eduardo Blanco | Alakananda Vempala
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Leveraging Verb-Argument Structures to Infer Semantic Relations
Eduardo Blanco | Dan Moldovan
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

2013

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A Semantically Enhanced Approach to Determine Textual Similarity
Eduardo Blanco | Dan Moldovan
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Choosing the Right Words: Characterizing and Reducing Error of the Word Count Approach
Hansen Andrew Schwartz | Johannes Eichstaedt | Eduardo Blanco | Lukasz Dziurzynski | Margaret L. Kern | Stephanie Ramones | Martin Seligman | Lyle Ungar
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

2012

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Fine-Grained Focus for Pinpointing Positive Implicit Meaning from Negated Statements
Eduardo Blanco | Dan Moldovan
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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*SEM 2012 Shared Task: Resolving the Scope and Focus of Negation
Roser Morante | Eduardo Blanco
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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Polaris: Lymba’s Semantic Parser
Dan Moldovan | Eduardo Blanco
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Semantic representation of text is key to text understanding and reasoning. In this paper, we present Polaris, Lymba's semantic parser. Polaris is a supervised semantic parser that given text extracts semantic relations. It extracts relations from a wide variety of lexico-syntactic patterns, including verb-argument structures, noun compounds and others. The output can be provided in several formats: XML, RDF triples, logic forms or plain text, facilitating interoperability with other tools. Polaris is implemented using eight separate modules. Each module is explained and a detailed example of processing using a sample sentence is provided. Overall results using a benchmark are discussed. Per module performance, including errors made and pruned by each module are also analyzed.

2011

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A Model for Composing Semantic Relations
Eduardo Blanco | Dan Moldovan
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

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Semantic Representation of Negation Using Focus Detection
Eduardo Blanco | Dan Moldovan
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Unsupervised Learning of Semantic Relation Composition
Eduardo Blanco | Dan Moldovan
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Composition of Semantic Relations: Model and Applications
Eduardo Blanco | Hakki C. Cankaya | Dan Moldovan
Coling 2010: Posters

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Automatic Discovery of Manner Relations and its Applications
Eduardo Blanco | Dan Moldovan
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2008

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Causal Relation Extraction
Eduardo Blanco | Nuria Castell | Dan Moldovan
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper presents a supervised method for the detection and extraction of Causal Relations from open domain text. First we give a brief outline of the definition of causation and how it relates to other Semantic Relations, as well as a characterization of their encoding. In this work, we only consider marked and explicit causations. Our approach first identifies the syntactic patterns that may encode a causation, then we use Machine Learning techniques to decide whether or not a pattern instance encodes a causation. We focus on the most productive pattern, a verb phrase followed by a relator and a clause, and its reverse version, a relator followed by a clause and a verb phrase. As relators we consider the words as, after, because and since. We present a set of lexical, syntactic and semantic features for the classification task, their rationale and some examples. The results obtained are discussed and the errors analyzed.