James H. Martin

Also published as: James Martin


2020

pdf bib
Defining and Learning Refined Temporal Relations in the Clinical Narrative
Kristin Wright-Bettner | Chen Lin | Timothy Miller | Steven Bethard | Dmitriy Dligach | Martha Palmer | James H. Martin | Guergana Savova
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative. We refined the THYME corpus annotations to more faithfully represent nuanced temporality and nuanced temporal-coreferential relations. The main contributions are in re-defining CONTAINS and OVERLAP relations into CONTAINS, CONTAINS-SUBEVENT, OVERLAP and NOTED-ON. We demonstrate that these refinements lead to substantial gains in learnability for state-of-the-art transformer models as compared to previously reported results on the original THYME corpus. We thus establish a baseline for the automatic extraction of these refined temporal relations. Although our study is done on clinical narrative, we believe it addresses far-reaching challenges that are corpus- and domain- agnostic.

2017

pdf bib
Abstract Meaning Representation Parsing using LSTM Recurrent Neural Networks
William Foland | James H. Martin
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a system which parses sentences into Abstract Meaning Representations, improving state-of-the-art results for this task by more than 5%. AMR graphs represent semantic content using linguistic properties such as semantic roles, coreference, negation, and more. The AMR parser does not rely on a syntactic pre-parse, or heavily engineered features, and uses five recurrent neural networks as the key architectural components for inferring AMR graphs.

2016

pdf bib
A Tangled Web: The Faint Signals of Deception in Text - Boulder Lies and Truth Corpus (BLT-C)
Franco Salvetti | John B. Lowe | James H. Martin
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present an approach to creating corpora for use in detecting deception in text, including a discussion of the challenges peculiar to this task. Our approach is based on soliciting several types of reviews from writers and was implemented using Amazon Mechanical Turk. We describe the multi-dimensional corpus of reviews built using this approach, available free of charge from LDC as the Boulder Lies and Truth Corpus (BLT-C). Challenges for both corpus creation and the deception detection include the fact that human performance on the task is typically at chance, that the signal is faint, that paid writers such as turkers are sometimes deceptive, and that deception is a complex human behavior; manifestations of deception depend on details of domain, intrinsic properties of the deceiver (such as education, linguistic competence, and the nature of the intention), and specifics of the deceptive act (e.g., lying vs. fabricating.) To overcome the inherent lack of ground truth, we have developed a set of semi-automatic techniques to ensure corpus validity. We present some preliminary results on the task of deception detection which suggest that the BLT-C is an improvement in the quality of resources available for this task.

pdf bib
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks
William Foland | James H. Martin
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

pdf bib
SGRank: Combining Statistical and Graphical Methods to Improve the State of the Art in Unsupervised Keyphrase Extraction
Soheil Danesh | Tamara Sumner | James H. Martin
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

pdf bib
Dependency-Based Semantic Role Labeling using Convolutional Neural Networks
William Foland | James Martin
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2012

pdf bib
Foundations of a Multilayer Annotation Framework for Twitter Communications During Crisis Events
William J. Corvey | Sudha Verma | Sarah Vieweg | Martha Palmer | James H. Martin
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually collect and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed efficiently and effectively. We describe a natural language processing component of the EPIC (Empowering the Public with Information in Crisis) Project infrastructure, designed to extract linguistic and behavioral information from tweet text to aid in the task of information integration. The system incorporates linguistic annotation, in the form of Named Entity Tagging, as well as behavioral annotations to capture tweets contributing to situational awareness and analyze the information type of the tweet content. We show classification results and describe future integration of these classifiers in the larger EPIC infrastructure.

pdf bib
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
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

2009

pdf bib
Topic Model Analysis of Metaphor Frequency for Psycholinguistic Stimuli
Steven Bethard | Vicky Tzuyin Lai | James H. Martin
Proceedings of the Workshop on Computational Approaches to Linguistic Creativity

2008

pdf bib
Extractive Summaries for Educational Science Content
Sebastian de la Chica | Faisal Ahmad | James H. Martin | Tamara Sumner
Proceedings of ACL-08: HLT, Short Papers

pdf bib
Learning Semantic Links from a Corpus of Parallel Temporal and Causal Relations
Steven Bethard | James H. Martin
Proceedings of ACL-08: HLT, Short Papers

pdf bib
Extracting a Representation from Text for Semantic Analysis
Rodney D. Nielsen | Wayne Ward | James H. Martin | Martha Palmer
Proceedings of ACL-08: HLT, Short Papers

pdf bib
Building a Corpus of Temporal-Causal Structure
Steven Bethard | William Corvey | Sara Klingenstein | James H. Martin
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

While recent corpus annotation efforts cover a wide variety of semantic structures, work on temporal and causal relations is still in its early stages. Annotation efforts have typically considered either temporal relations or causal relations, but not both, and no corpora currently exist that allow the relation between temporals and causals to be examined empirically. We have annotated a corpus of 1000 event pairs for both temporal and causal relations, focusing on a relatively frequent construction in which the events are conjoined by the word “and”. Temporal relations were annotated using an extension of the BEFORE and AFTER scheme used in the TempEval competition, and causal relations were annotated using a scheme based on connective phrases like “and as a result”. The annotators achieved 81.2% agreement on temporal relations and 77.8% agreement on causal relations. Analysis of the resulting corpus revealed some interesting findings, for example, that over 30% of CAUSAL relations do not have an underlying BEFORE relation. The corpus was also explored using machine learning methods, and while model performance exceeded all baselines, the results suggested that simple grammatical cues may be insufficient for identifying the more difficult temporal and causal relations.

pdf bib
Annotating Students’ Understanding of Science Concepts
Rodney D. Nielsen | Wayne Ward | James Martin | Martha Palmer
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper summarizes the annotation of fine-grained entailment relationships in the context of student answers to science assessment questions. We annotated a corpus of 15,357 answer pairs with 145,911 fine-grained entailment relationships. We provide the rationale for such fine-grained analysis and discuss its perceived benefits to an Intelligent Tutoring System. The corpus also has potential applications in other areas, such as question answering and multi-document summarization. Annotators achieved 86.2% inter-annotator agreement (Kappa=0.728, corresponding to substantial agreement) annotating the fine-grained facets of reference answers with regard to understanding expressed in student answers and labeling from one of five possible detailed relationship categories. The corpus described in this paper, which is the only one providing such detailed entailment annotations, is available as a public resource for the research community. The corpus is expected to enable application development, not only for intelligent tutoring systems, but also for general textual entailment applications, that is currently not practical.

pdf bib
Towards Robust Semantic Role Labeling
Sameer S. Pradhan | Wayne Ward | James H. Martin
Computational Linguistics, Volume 34, Number 2, June 2008 - Special Issue on Semantic Role Labeling

pdf bib
Classification Errors in a Domain-Independent Assessment System
Rodney D. Nielsen | Wayne Ward | James H. Martin
Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications

pdf bib
Pedagogically Useful Extractive Summaries for Science Education
Sebastian de la Chica | Faisal Ahmad | James H. Martin | Tamara Sumner
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

pdf bib
Towards Robust Semantic Role Labeling
Sameer Pradhan | Wayne Ward | James Martin
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

pdf bib
CU-COMSEM: Exploring Rich Features for Unsupervised Web Personal Name Disambiguation
Ying Chen | James H. Martin
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

pdf bib
CU-TMP: Temporal Relation Classification Using Syntactic and Semantic Features
Steven Bethard | James H. Martin
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

pdf bib
Towards Robust Unsupervised Personal Name Disambiguation
Ying Chen | James Martin
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

pdf bib
Identification of Event Mentions and their Semantic Class
Steven Bethard | James H. Martin
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

2005

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

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

2004

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

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

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

1997

pdf bib
Contextual Spelling Correction Using Latent Semantic Analysis
Michael P. Jones | James H. Martin
Fifth Conference on Applied Natural Language Processing

1995

pdf bib
Expressing Rhetorical Relations in Instructional Text: a case study of the purpose relation
Keith Vander Linden | James Martin
Computational Linguistics, Volume 21, Number 1, March 1995

1992

pdf bib
Knowledge Representation and Metaphor
James Martin
Computational Linguistics, Volume 18, Number 1, March 1992

1991

pdf bib
Conventional Metaphor and the Lexicon
James H. Martin
Lexical Semantics and Knowledge Representation

1988

pdf bib
The Berkeley Unix Consultant Project
Robert Wilensky | David N. Chin | Marc Luria | James Martin | James Mayfield | Dekai Wu
Computational Linguistics, Volume 14, Number 4, December 1988, LFP: A Logic for Linguistic Descriptions and an Analysis of its Complexity

pdf bib
Representing Regularities in the Metaphoric Lexicon
James H. Martin
Coling Budapest 1988 Volume 1: International Conference on Computational Linguistics