Steven Bethard


2019

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Pre-trained Contextualized Character Embeddings Lead to Major Improvements in Time Normalization: a Detailed Analysis
Dongfang Xu | Egoitz Laparra | Steven Bethard

Recent studies have shown that pre-trained contextual word embeddings, which assign the same word different vectors in different contexts, improve performance in many tasks. But while contextual embeddings can also be trained at the character level, the effectiveness of such embeddings has not been studied. We derive character-level contextual embeddings from Flair (Akbik et al., 2018), and apply them to a time normalization task, yielding major performance improvements over the previous state-of-the-art: 51% error reduction in news and 33% in clinical notes. We analyze the sources of these improvements, and find that pre-trained contextual character embeddings are more robust to term variations, infrequent terms, and cross-domain changes. We also quantify the size of context that pre-trained contextual character embeddings take advantage of, and show that such embeddings capture features like part-of-speech and capitalization.

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Incivility Detection in Online Comments
Farig Sadeque | Stephen Rains | Yotam Shmargad | Kate Kenski | Kevin Coe | Steven Bethard

Incivility in public discourse has been a major concern in recent times as it can affect the quality and tenacity of the discourse negatively. In this paper, we present neural models that can learn to detect name-calling and vulgarity from a newspaper comment section. We show that in contrast to prior work on detecting toxic language, fine-grained incivilities like namecalling cannot be accurately detected by simple models like logistic regression. We apply the models trained on the newspaper comments data to detect uncivil comments in a Russian troll dataset, and find that despite the change of domain, the model makes accurate predictions.

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University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities
Vikas Yadav | Egoitz Laparra | Ti-Tai Wang | Mihai Surdeanu | Steven Bethard

We present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for the SemEval 2019 task 12. We achieved fourth place on tasks 1 and 3. We implemented a deep-affix based LSTM-CRF NER model for task 1, which utilizes only character, word, pre- fix and suffix information for the identification of geolocation entities. Despite using just the training data provided by task organizers and not using any lexicon features, we achieved 78.85% strict micro F-score on task 1. We used the unsupervised population heuristics for task 3 and achieved 52.99% strict micro-F1 score in this task.

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Proceedings of the 2nd Clinical Natural Language Processing Workshop
Anna Rumshisky | Kirk Roberts | Steven Bethard | Tristan Naumann

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A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction
Chen Lin | Timothy Miller | Dmitriy Dligach | Steven Bethard | Guergana Savova

Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to build a sentence-agnostic framework for the task of CONTAINS temporal relation extraction. We establish a new state-of-the-art result for the task, 0.684F for in-domain (0.055-point improvement) and 0.565F for cross-domain (0.018-point improvement), by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated “silver” instances.

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Inferring missing metadata from environmental policy texts
Steven Bethard | Egoitz Laparra | Sophia Wang | Yiyun Zhao | Ragheb Al-Ghezi | Aaron Lien | Laura López-Hoffman

The National Environmental Policy Act (NEPA) provides a trove of data on how environmental policy decisions have been made in the United States over the last 50 years. Unfortunately, there is no central database for this information and it is too voluminous to assess manually. We describe our efforts to enable systematic research over US environmental policy by extracting and organizing metadata from the text of NEPA documents. Our contributions include collecting more than 40,000 NEPA-related documents, and evaluating rule-based baselines that establish the difficulty of three important tasks: identifying lead agencies, aligning document versions, and detecting reused text.

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Alignment over Heterogeneous Embeddings for Question Answering
Vikas Yadav | Steven Bethard | Mihai Surdeanu

We propose a simple, fast, and mostly-unsupervised approach for non-factoid question answering (QA) called Alignment over Heterogeneous Embeddings (AHE). AHE simply aligns each word in the question and candidate answer with the most similar word in the retrieved supporting paragraph, and weighs each alignment score with the inverse document frequency of the corresponding question/answer term. AHE’s similarity function operates over embeddings that model the underlying text at different levels of abstraction: character (FLAIR), word (BERT and GloVe), and sentence (InferSent), where the latter is the only supervised component in the proposed approach. Despite its simplicity and lack of supervision, AHE obtains a new state-of-the-art performance on the “Easy” partition of the AI2 Reasoning Challenge (ARC) dataset (64.6% accuracy), top-two performance on the “Challenge” partition of ARC (34.1%), and top-three performance on the WikiQA dataset (74.08% MRR), outperforming many other complex, supervised approaches. Our error analysis indicates that alignments over character, word, and sentence embeddings capture substantially different semantic information. We exploit this with a simple meta-classifier that learns how much to trust the predictions over each representation, which further improves the performance of unsupervised AHE.

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Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models
Rebecca Sharp | Adarsh Pyarelal | Benjamin Gyori | Keith Alcock | Egoitz Laparra | Marco A. Valenzuela-Escárcega | Ajay Nagesh | Vikas Yadav | John Bachman | Zheng Tang | Heather Lent | Fan Luo | Mithun Paul | Steven Bethard | Kobus Barnard | Clayton Morrison | Mihai Surdeanu

Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed approach is implemented through three systems: Eidos, INDRA, and Delphi. Eidos is an open-domain machine reading system designed to extract causal relations from natural language. It is rule-based, allowing for rapid domain transfer, customizability, and interpretability. INDRA aggregates multiple sources of causal information and performs assembly to create a coherent knowledge base and assess its reliability. This assembled knowledge serves as the starting point for modeling. Delphi is a modeling framework that assembles quantified causal fragments and their contexts into executable probabilistic models that respect the semantics of the original text, and can be used to support decision making.

2018

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A Survey on Recent Advances in Named Entity Recognition from Deep Learning models
Vikas Yadav | Steven Bethard

Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.

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From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations
Egoitz Laparra | Dongfang Xu | Steven Bethard

This paper presents the first model for time normalization trained on the SCATE corpus. In the SCATE schema, time expressions are annotated as a semantic composition of time entities. This novel schema favors machine learning approaches, as it can be viewed as a semantic parsing task. In this work, we propose a character level multi-output neural network that outperforms previous state-of-the-art built on the TimeML schema. To compare predictions of systems that follow both SCATE and TimeML, we present a new scoring metric for time intervals. We also apply this new metric to carry out a comparative analysis of the annotations of both schemes in the same corpus.

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Proceedings of The 12th International Workshop on Semantic Evaluation
Marianna Apidianaki | Saif M. Mohammad | Jonathan May | Ekaterina Shutova | Steven Bethard | Marine Carpuat

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SemEval 2018 Task 6: Parsing Time Normalizations
Egoitz Laparra | Dongfang Xu | Ahmed Elsayed | Steven Bethard | Martha Palmer

This paper presents the outcomes of the Parsing Time Normalization shared task held within SemEval-2018. The aim of the task is to parse time expressions into the compositional semantic graphs of the Semantically Compositional Annotation of Time Expressions (SCATE) schema, which allows the representation of a wider variety of time expressions than previous approaches. Two tracks were included, one to evaluate the parsing of individual components of the produced graphs, in a classic information extraction way, and another one to evaluate the quality of the time intervals resulting from the interpretation of those graphs. Though 40 participants registered for the task, only one team submitted output, achieving 0.55 F1 in Track 1 (parsing) and 0.70 F1 in Track 2 (intervals).

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Deep Affix Features Improve Neural Named Entity Recognizers
Vikas Yadav | Rebecca Sharp | Steven Bethard

We propose a practical model for named entity recognition (NER) that combines word and character-level information with a specific learned representation of the prefixes and suffixes of the word. We apply this approach to multilingual and multi-domain NER and show that it achieves state of the art results on the CoNLL 2002 Spanish and Dutch and CoNLL 2003 German NER datasets, consistently achieving 1.5-2.3 percent over the state of the art without relying on any dictionary features. Additionally, we show improvement on SemEval 2013 task 9.1 DrugNER, achieving state of the art results on the MedLine dataset and the second best results overall (-1.3% from state of the art). We also establish a new benchmark on the I2B2 2010 Clinical NER dataset with 84.70 F-score.

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Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction
Chen Lin | Timothy Miller | Dmitriy Dligach | Hadi Amiri | Steven Bethard | Guergana Savova

Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance. We propose to build a recurrent neural network with multiple semantically heterogeneous embeddings within a self-training framework. Our framework makes use of labeled, unlabeled, and social media data, operates on basic features, and is scalable and generalizable. With this method, we establish the state-of-the-art result for both in- and cross-domain for a clinical temporal relation extraction task.

2017

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Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments
Quynh Ngoc Thi Do | Steven Bethard | Marie-Francine Moens

Implicit semantic role labeling (iSRL) is the task of predicting the semantic roles of a predicate that do not appear as explicit arguments, but rather regard common sense knowledge or are mentioned earlier in the discourse. We introduce an approach to iSRL based on a predictive recurrent neural semantic frame model (PRNSFM) that uses a large unannotated corpus to learn the probability of a sequence of semantic arguments given a predicate. We leverage the sequence probabilities predicted by the PRNSFM to estimate selectional preferences for predicates and their arguments. On the NomBank iSRL test set, our approach improves state-of-the-art performance on implicit semantic role labeling with less reliance than prior work on manually constructed language resources.

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Unsupervised Domain Adaptation for Clinical Negation Detection
Timothy Miller | Steven Bethard | Hadi Amiri | Guergana Savova

Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical negation detection, finding only modest gains that fall well short of in-domain performance.

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Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
Chen Lin | Timothy Miller | Dmitriy Dligach | Steven Bethard | Guergana Savova

Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo-tokens for CNNs. With this method, we establish a new state-of-the-art result for a clinical temporal relation extraction task.

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Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Steven Bethard | Marine Carpuat | Marianna Apidianaki | Saif M. Mohammad | Daniel Cer | David Jurgens

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SemEval-2017 Task 12: Clinical TempEval
Steven Bethard | Guergana Savova | Martha Palmer | James Pustejovsky

Clinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition (brain cancer)? Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were evaluated on clinical and pathology notes from Mayo Clinic cancer patients, annotated with an extension of TimeML for the clinical domain. 11 teams participated in the tasks, with the best systems achieving F1 scores above 0.55 for time expressions, above 0.70 for event expressions, and above 0.40 for temporal relations. Most tasks observed about a 20 point drop over Clinical TempEval 2016, where systems were trained and evaluated on the same domain (colon cancer).

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Neural Temporal Relation Extraction
Dmitriy Dligach | Timothy Miller | Chen Lin | Steven Bethard | Guergana Savova

We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-based encoding.

2016

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Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning
Upendra Sapkota | Thamar Solorio | Manuel Montes | Steven Bethard

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Semi-supervised CLPsych 2016 Shared Task System Submission
Nicolas Rey-Villamizar | Prasha Shrestha | Thamar Solorio | Farig Sadeque | Steven Bethard | Ted Pedersen

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Improving Temporal Relation Extraction with Training Instance Augmentation
Chen Lin | Timothy Miller | Dmitriy Dligach | Steven Bethard | Guergana Savova

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Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)
Anna Rumshisky | Kirk Roberts | Steven Bethard | Tristan Naumann

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Visualizing the Content of a Children’s Story in a Virtual World: Lessons Learned
Quynh Ngoc Thi Do | Steven Bethard | Marie-Francine Moens

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Analysis of Anxious Word Usage on Online Health Forums
Nicolas Rey-Villamizar | Prasha Shrestha | Farig Sadeque | Steven Bethard | Ted Pedersen | Arjun Mukherjee | Thamar Solorio

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Why Do They Leave: Modeling Participation in Online Depression Forums
Farig Sadeque | Ted Pedersen | Thamar Solorio | Prasha Shrestha | Nicolas Rey-Villamizar | Steven Bethard

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Age and Gender Prediction on Health Forum Data
Prasha Shrestha | Nicolas Rey-Villamizar | Farig Sadeque | Ted Pedersen | Steven Bethard | Thamar Solorio

Health support forums have become a rich source of data that can be used to improve health care outcomes. A user profile, including information such as age and gender, can support targeted analysis of forum data. But users might not always disclose their age and gender. It is desirable then to be able to automatically extract this information from users’ content. However, to the best of our knowledge there is no such resource for author profiling of health forum data. Here we present a large corpus, with close to 85,000 users, for profiling and also outline our approach and benchmark results to automatically detect a user’s age and gender from their forum posts. We use a mix of features from a user’s text as well as forum specific features to obtain accuracy well above the baseline, thus showing that both our dataset and our method are useful and valid.

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A Semantically Compositional Annotation Scheme for Time Normalization
Steven Bethard | Jonathan Parker

We present a new annotation scheme for normalizing time expressions, such as “three days ago”, to computer-readable forms, such as 2016-03-07. The annotation scheme addresses several weaknesses of the existing TimeML standard, allowing the representation of time expressions that align to more than one calendar unit (e.g., “the past three summers”), that are defined relative to events (e.g., “three weeks postoperative”), and that are unions or intersections of smaller time expressions (e.g., “Tuesdays and Thursdays”). It achieves this by modeling time expression interpretation as the semantic composition of temporal operators like UNION, NEXT, and AFTER. We have applied the annotation scheme to 34 documents so far, producing 1104 annotations, and achieving inter-annotator agreement of 0.821.

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Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
Steven Bethard | Marine Carpuat | Daniel Cer | David Jurgens | Preslav Nakov | Torsten Zesch

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DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity
Md Arafat Sultan | Steven Bethard | Tamara Sumner

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SemEval-2016 Task 12: Clinical TempEval
Steven Bethard | Guergana Savova | Wei-Te Chen | Leon Derczynski | James Pustejovsky | Marc Verhagen

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Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training
Quynh Ngoc Thi Do | Steven Bethard | Marie-Francine Moens

We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the fact that co-training is a successful traditional semi-supervised method, its application in SRL is very limited especially when a huge amount of labeled data is available. In this work, co-training is used together with word embeddings to improve the performance of a system trained on a large training dataset. We also introduce a semantic role labeling system with a simple learning architecture and effective inference that is easily adaptable to semi-supervised settings with new training data and/or new features. On the out-of-domain testing set of the standard benchmark CoNLL 2009 data our simple approach achieves high performance and improves state-of-the-art results.

2015

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Not All Character N-grams Are Created Equal: A Study in Authorship Attribution
Upendra Sapkota | Steven Bethard | Manuel Montes | Thamar Solorio

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Developing Language-tagged Corpora for Code-switching Tweets
Suraj Maharjan | Elizabeth Blair | Steven Bethard | Thamar Solorio

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Predicting Continued Participation in Online Health Forums
Farig Sadeque | Thamar Solorio | Ted Pedersen | Prasha Shrestha | Steven Bethard

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Extracting Time Expressions from Clinical Text
Timothy Miller | Steven Bethard | Dmitriy Dligach | Chen Lin | Guergana Savova

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DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition
Md Arafat Sultan | Steven Bethard | Tamara Sumner

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CUAB: Supervised Learning of Disorders and their Attributes using Relations
James Gung | John Osborne | Steven Bethard

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SemEval-2015 Task 6: Clinical TempEval
Steven Bethard | Leon Derczynski | Guergana Savova | James Pustejovsky | Marc Verhagen

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Feature-Rich Two-Stage Logistic Regression for Monolingual Alignment
Md Arafat Sultan | Steven Bethard | Tamara Sumner

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Adapting Coreference Resolution for Narrative Processing
Quynh Ngoc Thi Do | Steven Bethard | Marie-Francine Moens

2014

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Descending-Path Convolution Kernel for Syntactic Structures
Chen Lin | Timothy Miller | Alvin Kho | Steven Bethard | Dmitriy Dligach | Sameer Pradhan | Guergana Savova

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An Annotation Framework for Dense Event Ordering
Taylor Cassidy | Bill McDowell | Nathanael Chambers | Steven Bethard

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The Stanford CoreNLP Natural Language Processing Toolkit
Christopher Manning | Mihai Surdeanu | John Bauer | Jenny Finkel | Steven Bethard | David McClosky

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ClearTK 2.0: Design Patterns for Machine Learning in UIMA
Steven Bethard | Philip Ogren | Lee Becker

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Proceedings of the EACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL)
Oleksandr Kolomiyets | Marie-Francine Moens | Martha Palmer | James Pustejovsky | Steven Bethard

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Overview for the First Shared Task on Language Identification in Code-Switched Data
Thamar Solorio | Elizabeth Blair | Suraj Maharjan | Steven Bethard | Mona Diab | Mahmoud Ghoneim | Abdelati Hawwari | Fahad AlGhamdi | Julia Hirschberg | Alison Chang | Pascale Fung

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DLS@CU: Sentence Similarity from Word Alignment
Md Arafat Sultan | Steven Bethard | Tamara Sumner

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Temporal Annotation in the Clinical Domain
William F. Styler IV | Steven Bethard | Sean Finan | Martha Palmer | Sameer Pradhan | Piet C de Groen | Brad Erickson | Timothy Miller | Chen Lin | Guergana Savova | James Pustejovsky

This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, “the THYME Guidelines to ISO-TimeML (THYME-TimeML)”. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.

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Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence
Md Arafat Sultan | Steven Bethard | Tamara Sumner

We present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that words with similar meanings represent potential pairs for alignment if located in similar contexts, we propose a system that operates by finding such pairs. In two intrinsic evaluations on alignment test data, our system achieves F1 scores of 88–92%, demonstrating 1–3% absolute improvement over the previous best system. Moreover, in two extrinsic evaluations our aligner outperforms existing aligners, and even a naive application of the aligner approaches state-of-the-art performance in each extrinsic task.

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Dense Event Ordering with a Multi-Pass Architecture
Nathanael Chambers | Taylor Cassidy | Bill McDowell | Steven Bethard

The past 10 years of event ordering research has focused on learning partial orderings over document events and time expressions. The most popular corpus, the TimeBank, contains a small subset of the possible ordering graph. Many evaluations follow suit by only testing certain pairs of events (e.g., only main verbs of neighboring sentences). This has led most research to focus on specific learners for partial labelings. This paper attempts to nudge the discussion from identifying some relations to all relations. We present new experiments on strongly connected event graphs that contain ∼10 times more relations per document than the TimeBank. We also describe a shift away from the single learner to a sieve-based architecture that naturally blends multiple learners into a precision-ranked cascade of sieves. Each sieve adds labels to the event graph one at a time, and earlier sieves inform later ones through transitive closure. This paper thus describes innovations in both approach and task. We experiment on the densest event graphs to date and show a 14% gain over state-of-the-art.

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Cross-Topic Authorship Attribution: Will Out-Of-Topic Data Help?
Upendra Sapkota | Thamar Solorio | Manuel Montes | Steven Bethard | Paolo Rosso

2013

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51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop
Anik Dey | Sebastian Krause | Ivelina Nikolova | Eva Vecchi | Steven Bethard | Preslav I. Nakov | Feiyu Xu

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DLS@CU-CORE: A Simple Machine Learning Model of Semantic Textual Similarity
Md. Sultan | Steven Bethard | Tamara Sumner

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ClearTK-TimeML: A minimalist approach to TempEval 2013
Steven Bethard

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SemEval-2013 Task 3: Spatial Role Labeling
Oleksandr Kolomiyets | Parisa Kordjamshidi | Marie-Francine Moens | Steven Bethard

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CU : Computational Assessment of Short Free Text Answers - A Tool for Evaluating Students’ Understanding
Ifeyinwa Okoye | Steven Bethard | Tamara Sumner

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Discovering Temporal Narrative Containers in Clinical Text
Timothy Miller | Steven Bethard | Dmitriy Dligach | Sameer Pradhan | Chen Lin | Guergana Savova

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Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing
David Yarowsky | Timothy Baldwin | Anna Korhonen | Karen Livescu | Steven Bethard

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A Synchronous Context Free Grammar for Time Normalization
Steven Bethard

2012

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Extracting Narrative Timelines as Temporal Dependency Structures
Oleksandr Kolomiyets | Steven Bethard | Marie-Francine Moens

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SemEval-2012 Task 3: Spatial Role Labeling
Parisa Kordjamshidi | Steven Bethard | Marie-Francine Moens

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Annotating Story Timelines as Temporal Dependency Structures
Steven Bethard | Oleksandr Kolomiyets | Marie-Francine Moens

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Identifying science concepts and student misconceptions in an interactive essay writing tutor
Steven Bethard | Ifeyinwa Okoye | Md. Arafat Sultan | Haojie Hang | James H. Martin | Tamara Sumner

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Skip N-grams and Ranking Functions for Predicting Script Events
Bram Jans | Steven Bethard | Ivan Vulić | Marie Francine Moens

2011

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Model-Portability Experiments for Textual Temporal Analysis
Oleksandr Kolomiyets | Steven Bethard | Marie-Francine Moens

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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

2010

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Crowdsourcing and language studies: the new generation of linguistic data
Robert Munro | Steven Bethard | Victor Kuperman | Vicky Tzuyin Lai | Robin Melnick | Christopher Potts | Tyler Schnoebelen | Harry Tily

2009

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Building Test Suites for UIMA Components
Philip Ogren | Steven Bethard

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Topic Model Analysis of Metaphor Frequency for Psycholinguistic Stimuli
Steven Bethard | Vicky Tzuyin Lai | James H. Martin

2008

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Building a Corpus of Temporal-Causal Structure
Steven Bethard | William Corvey | Sara Klingenstein | James H. Martin

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Learning Semantic Links from a Corpus of Parallel Temporal and Causal Relations
Steven Bethard | James H. Martin

2007

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CU-TMP: Temporal Relation Classification Using Syntactic and Semantic Features
Steven Bethard | James H. Martin

2006

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Identification of Event Mentions and their Semantic Class
Steven Bethard | James H. Martin

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