Search Results for author: Robert E. Mercer

Found 21 papers, 5 papers with code

Use of Claim Graphing and Argumentation Schemes in Biomedical Literature: A Manual Approach to Analysis

no code implementations COLING (ArgMining) 2020 Eli Moser, Robert E. Mercer

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.

Building a Synthetic Biomedical Research Article Citation Linkage Corpus

no code implementations LREC 2022 Sudipta Singha Roy, Robert E. Mercer

In this paper, to find these citation linkages in biomedical research publications using deep learning, we provide a synthetic silver standard corpus as well as the method to build this corpus.

Semantic Similarity Semantic Textual Similarity +2

MeSHup: Corpus for Full Text Biomedical Document Indexing

1 code implementation LREC 2022 Xindi Wang, Robert E. Mercer, Frank Rudzicz

Medical Subject Heading (MeSH) indexing refers to the problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms.

BioCite: A Deep Learning-based Citation Linkage Framework for Biomedical Research Articles

no code implementations BioNLP (ACL) 2022 Sudipta Singha Roy, Robert E. Mercer

This paper suggests a mechanism for linking citing sentences in a publication with cited sentences in referenced sources.

Building a Biomedical Full-Text Part-of-Speech Corpus Semi-Automatically

1 code implementation LREC (LAW) 2022 Nicholas Elder, Robert E. Mercer, Sudipta Singha Roy

This paper presents a method for semi-automatically building a corpus of full-text English-language biomedical articles annotated with part-of-speech tags.

TAG

MeSHup: A Corpus for Full Text Biomedical Document Indexing

no code implementations28 Apr 2022 Xindi Wang, Robert E. Mercer, Frank Rudzicz

Medical Subject Heading (MeSH) indexing refers to the problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms.

KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling

1 code implementation ACL 2022 Xindi Wang, Robert E. Mercer, Frank Rudzicz

Currently, Medical Subject Headings (MeSH) are manually assigned to every biomedical article published and subsequently recorded in the PubMed database to facilitate retrieving relevant information.

A Lexicon-Based Approach for Detecting Hedges in Informal Text

no code implementations LREC 2020 Jumayel Islam, Lu Xiao, Robert E. Mercer

Our work produced three manually constructed lists of hedge words, booster words, and hedging phrases.

Management

Annotation of Rhetorical Moves in Biochemistry Articles

no code implementations WS 2019 Mohammed Alliheedi, Robert E. Mercer, Robin Cohen

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.

You Only Need Attention to Traverse Trees

no code implementations ACL 2019 Mahtab Ahmed, Muhammad Rifayat Samee, Robert E. Mercer

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.

Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition

no code implementations NAACL 2019 Jumayel Islam, Robert E. Mercer, Lu Xiao

It provides a great way to understand human psychology and impose a challenge to researchers to analyze their content easily.

Improving Tree-LSTM with Tree Attention

no code implementations1 Jan 2019 Mahtab Ahmed, Muhammad Rifayat Samee, Robert E. Mercer

In Natural Language Processing (NLP), we often need to extract information from tree topology.

Extracting Connected Concepts from Biomedical Texts using Fog Index

no code implementations30 Jul 2013 Rushdi Shams, Robert E. Mercer

We rank sentences of a text according to their FI and select 30 percent of the most difficult sentences.

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