Robert E. Mercer

Univ. of Western Ontario

Also published as: Robert Mercer

Other people with similar names: Robert L. Mercer (IBM)


2020

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Use of Claim Graphing and Argumentation Schemes in Biomedical Literature: A Manual Approach to Analysis
Eli Moser | Robert E. Mercer
Proceedings of the 7th Workshop on Argument Mining

Argumentation in an experimental life science paper consists of a main claim being supported with reasoned argumentative steps based on the data garnered from the experiments that were carried out. In this paper we report on an investigation of the large scale argumentation structure found when examining five biochemistry journal publications. One outcome of this investigation of biochemistry articles suggests that argumentation schemes originally designed for genetic research articles may transfer to experimental biomedical literature in general. Our use of these argumentation schemes shows that claims depend not only on experimental data but also on other claims. The tendency for claims to use other claims as their supporting evidence in addition to the experimental data led to two novel models that have provided a better understanding of the large scale argumentation structure of a complete biochemistry paper. First, the claim graph displays the claims within a paper, their interactions, and their evidence. Second, another aspect of this argumentation network is further illustrated by the Model of Informational Hierarchy (MIH) which visualizes at a meta-level the flow of reasoning provided by the authors of the paper and also connects the main claim to the paper’s title. Together, these models, which have been produced by a manual examination of the biochemistry articles, would be likely candidates for a computational method that analyzes the large scale argumentation structure.

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A Lexicon-Based Approach for Detecting Hedges in Informal Text
Jumayel Islam | Lu Xiao | Robert E. Mercer
Proceedings of the 12th Language Resources and Evaluation Conference

Hedging is a commonly used strategy in conversational management to show the speaker’s lack of commitment to what they communicate, which may signal problems between the speakers. Our project is interested in examining the presence of hedging words and phrases in identifying the tension between an interviewer and interviewee during a survivor interview. While there have been studies on hedging detection in the natural language processing literature, all existing work has focused on structured texts and formal communications. Our project thus investigated a corpus of eight unstructured conversational interviews about the Rwanda Genocide and identified hedging patterns in the interviewees’ responses. Our work produced three manually constructed lists of hedge words, booster words, and hedging phrases. Leveraging these lexicons, we developed a rule-based algorithm that detects sentence-level hedges in informal conversations such as survivor interviews. Our work also produced a dataset of 3000 sentences having the categories Hedge and Non-hedge annotated by three researchers. With experiments on this annotated dataset, we verify the efficacy of our proposed algorithm. Our work contributes to the further development of tools that identify hedges from informal conversations and discussions.

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Multilingual Corpus Creation for Multilingual Semantic Similarity Task
Mahtab Ahmed | Chahna Dixit | Robert E. Mercer | Atif Khan | Muhammad Rifayat Samee | Felipe Urra
Proceedings of the 12th Language Resources and Evaluation Conference

In natural language processing, the performance of a semantic similarity task relies heavily on the availability of a large corpus. Various monolingual corpora are available (mainly English); but multilingual resources are very limited. In this work, we describe a semi-automated framework to create a multilingual corpus which can be used for the multilingual semantic similarity task. The similar sentence pairs are obtained by crawling bilingual websites, whereas the dissimilar sentence pairs are selected by applying topic modeling and an Open-AI GPT model on the similar sentence pairs. We focus on websites in the government, insurance, and banking domains to collect English-French and English-Spanish sentence pairs; however, this corpus creation approach can be applied to any other industry vertical provided that a bilingual website exists. We also show experimental results for multilingual semantic similarity to verify the quality of the corpus and demonstrate its usage.

2019

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Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition
Jumayel Islam | Robert E. Mercer | Lu Xiao
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)

The advent of micro-blogging sites has paved the way for researchers to collect and analyze huge volumes of data in recent years. Twitter, being one of the leading social networking sites worldwide, provides a great opportunity to its users for expressing their states of mind via short messages which are called tweets. The urgency of identifying emotions and sentiments conveyed through tweets has led to several research works. It provides a great way to understand human psychology and impose a challenge to researchers to analyze their content easily. In this paper, we propose a novel use of a multi-channel convolutional neural architecture which can effectively use different emotion and sentiment indicators such as hashtags, emoticons and emojis that are present in the tweets and improve the performance of emotion and sentiment identification. We also investigate the incorporation of different lexical features in the neural network model and its effect on the emotion and sentiment identification task. We analyze our model on some standard datasets and compare its effectiveness with existing techniques.

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Annotation of Rhetorical Moves in Biochemistry Articles
Mohammed Alliheedi | Robert E. Mercer | Robin Cohen
Proceedings of the 6th Workshop on Argument Mining

This paper focuses on the real world application of scientific writing and on determining rhetorical moves, an important step in establishing the argument structure of biomedical articles. Using the observation that the structure of scholarly writing in laboratory-based experimental sciences closely follows laboratory procedures, we examine most closely the Methods section of the texts and adopt an approach of identifying rhetorical moves that are procedure-oriented. We also propose a verb-centric frame semantics with an effective set of semantic roles in order to support the analysis. These components are designed to support a computational model that extends a promising proposal of appropriate rhetorical moves for this domain, but one which is merely descriptive. Our work also contributes to the understanding of argument-related annotation schemes. In particular, we conduct a detailed study with human annotators to confirm that our selection of semantic roles is effective in determining the underlying rhetorical structure of existing biomedical articles in an extensive dataset. The annotated dataset that we produce provides the important knowledge needed for our ultimate goal of analyzing biochemistry articles.

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Incorporating Figure Captions and Descriptive Text in MeSH Term Indexing
Xindi Wang | Robert E. Mercer
Proceedings of the 18th BioNLP Workshop and Shared Task

The goal of text classification is to automatically assign categories to documents. Deep learning automatically learns effective features from data instead of adopting human-designed features. In this paper, we focus specifically on biomedical document classification using a deep learning approach. We present a novel multichannel TextCNN model for MeSH term indexing. Beyond the normal use of the text from the abstract and title for model training, we also consider figure and table captions, as well as paragraphs associated with the figures and tables. We demonstrate that these latter text sources are important feature sources for our method. A new dataset consisting of these text segments curated from 257,590 full text articles together with the articles’ MEDLINE/PubMed MeSH terms is publicly available.

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You Only Need Attention to Traverse Trees
Mahtab Ahmed | Muhammad Rifayat Samee | Robert E. Mercer
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In recent NLP research, a topic of interest is universal sentence encoding, sentence representations that can be used in any supervised task. At the word sequence level, fully attention-based models suffer from two problems: a quadratic increase in memory consumption with respect to the sentence length and an inability to capture and use syntactic information. Recursive neural nets can extract very good syntactic information by traversing a tree structure. To this end, we propose Tree Transformer, a model that captures phrase level syntax for constituency trees as well as word-level dependencies for dependency trees by doing recursive traversal only with attention. Evaluation of this model on four tasks gets noteworthy results compared to the standard transformer and LSTM-based models as well as tree-structured LSTMs. Ablation studies to find whether positional information is inherently encoded in the trees and which type of attention is suitable for doing the recursive traversal are provided.

2015

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Identification and Disambiguation of Lexical Cues of Rhetorical Relations across Different Text Genres
Taraneh Khazaei | Lu Xiao | Robert Mercer
Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics

2014

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An automated method to build a corpus of rhetorically-classified sentences in biomedical texts
Hospice Houngbo | Robert Mercer
Proceedings of the First Workshop on Argumentation Mining

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Titles That Announce Argumentative Claims in Biomedical Research Articles
Heather Graves | Roger Graves | Robert Mercer | Mahzereen Akter
Proceedings of the First Workshop on Argumentation Mining

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Extracting Higher Order Relations From Biomedical Text
Syeed Ibn Faiz | Robert Mercer
Proceedings of the First Workshop on Argumentation Mining

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Extracting Imperatives from Wikipedia Article for Deletion Discussions
Fiona Mao | Robert Mercer | Lu Xiao
Proceedings of the First Workshop on Argumentation Mining

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The Use of Text Similarity and Sentiment Analysis to Examine Rationales in the Large-Scale Online Deliberations
Wanting Mao | Lu Xiao | Robert Mercer
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2012

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Method Mention Extraction from Scientific Research Papers
Hospice Houngbo | Robert E. Mercer
Proceedings of COLING 2012

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A Machine Learning Approach for Phenotype Name Recognition
Maryam Khordad | Robert E. Mercer | Peter Rogan
Proceedings of COLING 2012

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Book Review: The Structure of Scientific Articles: Applications to Citation Indexing and Summarization by Simone Teufel
Robert E. Mercer
Computational Linguistics, Volume 38, Issue 2 - June 2012

2004

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A Design Methodology for a Biomedical Literature Indexing Tool Using the Rhetoric of Science
Robert E. Mercer | Chrysanne Di Marco
HLT-NAACL 2004 Workshop: Linking Biological Literature, Ontologies and Databases

2001

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Book Reviews: Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language
Robert E. Mercer
Computational Linguistics, Volume 27, Number 2, June 2001

1991

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Presuppositions and Default Reasoning: A Study in Lexical Pragmatics
Robert E. Mercer
Lexical Semantics and Knowledge Representation

1988

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Solving Some Persistent Presupposition Problems
Robert E. Mercer
Coling Budapest 1988 Volume 2: International Conference on Computational Linguistics