Papers by Robert E. Mercer
Enhancing Scientific Document Summarization with Research Community Perspective and Background Knowledge (2024.lrec-main)
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| Challenge: | Scientific paper summarization is the focus of recent research . prevailing summarizing methods involve selective extraction of content from abstract, introduction, and conclusion segments within the target articles. |
| Approach: | They propose a model that incorporates references and citations to capture the impact of the document on the research community. |
| Outcome: | The proposed model generates extractive and abstractive summaries in parallel and improves their performance when considering the standard metrics. |
You Only Need Attention to Traverse Trees (P19-1)
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| Challenge: | Recent research has focused on sentence representations. |
| Approach: | They propose a tree-based model that captures phrase-level syntax and word-level dependencies by doing recursive traversal with attention. |
| Outcome: | a new model captures phrase-level syntax and word-level dependencies with attention. |
Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification (2024.lrec-main)
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| Challenge: | Existing methods for ICD indexing use machine learning to assign subset of codes to medical records . experimental results show proposed method achieves state-of-the-art performance on a number of measures. |
| Approach: | They propose a method that uses a deep dilated residual convolution encoder to learn document representations across different lengths of the texts. |
| Outcome: | The proposed method achieves state-of-the-art performance on a number of measures. |
A Lexicon-Based Approach for Detecting Hedges in Informal Text (2020.lrec-1)
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| Challenge: | Existing studies on hedging detection have focused on structured texts and formal communications. |
| Approach: | They propose to use hedging words and phrases to identify tensions between interviewees during a survivor interview to help researchers understand the dynamics of the interview. |
| Outcome: | The proposed algorithm detects sentence-level hedges in informal conversations such as survivor interviews. |
Evaluation Benchmarks for Spanish Sentence Representations (2022.lrec-1)
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Vladimir Araujo, Andrés Carvallo, Souvik Kundu, José Cañete, Marcelo Mendoza, Robert E. Mercer, Felipe Bravo-Marquez, Marie-Francine Moens, Alvaro Soto
| Challenge: | Existing and newly constructed datasets address different tasks from various domains. |
| Approach: | They propose to use Spanish SentEval and Spanish DiscoEval to evaluate stand-alone and discourse-aware sentence representations. |
| Outcome: | The proposed benchmarks evaluate the capabilities of stand-alone and discourse-aware sentence representations in Spanish and show that they are more robust and comparable than previous benchmarks. |
Method Entity Extraction from Biomedical Texts (2022.coling-1)
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| Challenge: | Scientific research papers consist of complex keywords and domain-specific terminologies, and new terminologie erupt. |
| Approach: | They find method terminologies in biomedical text using rule-based and machine learning techniques . authors propose to use a silver standard corpus to extract method entities from biomedically text . |
| Outcome: | The proposed method entities can be extracted from biomedical text with reasonable accuracy . the proposed method entity extraction method is based on a rule-based method and a machine learning technique. |
Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition (N19-1)
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| Challenge: | Existing methods to analyze tweets are based on lexical features and a multi-channel convolutional neural architecture. |
| Approach: | They propose a neural network which can use different emotion and sentiment indicators such as hashtags, emoticons and emojis present in tweets to improve the performance of emotion and feelings identification. |
| Outcome: | The proposed model can use hashtags, emoticons and emojis present in tweets and improves emotion and sentiment identification. |
MeSHup: Corpus for Full Text Biomedical Document Indexing (2022.lrec-1)
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| Challenge: | Medical Subject Heading (MeSH) indexing is a problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms. |
| Approach: | They train an end-to-end model that combines features from documents and associated labels on MEDLINE corpus and report the new baseline. |
| Outcome: | The proposed system can be used to assign a biomedical document with the most relevant labels from an extremely large set of MeSH terms. |
Multilingual Corpus Creation for Multilingual Semantic Similarity Task (2020.lrec-1)
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| Challenge: | Existing monolingual corpora are limited for semantic similarity tasks . a major factor affecting the success of deep neural networks is the availability of large and good quality corpors. |
| Approach: | They propose a semi-automated framework to create a multilingual corpus for a semantic similarity task. |
| Outcome: | The proposed framework can be applied to government, insurance, banking domains provided a bilingual website exists. |
Building a Synthetic Biomedical Research Article Citation Linkage Corpus (2022.lrec-1)
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| Challenge: | citations are used in scientific publications to support the presented results and to demonstrate the previous discoveries. |
| Approach: | They propose a silver standard corpus and a method to find citation linkages in biomedical research papers using deep learning. |
| Outcome: | The proposed model can locate the text spans in a reference article, given a citing statement, based on semantic similarity. |