Michelle Gregory

Also published as: M. L. Gregory, Michelle L. Gregory


Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
Zenan Zhai | Dat Quoc Nguyen | Saber Akhondi | Camilo Thorne | Christian Druckenbrodt | Trevor Cohn | Michelle Gregory | Karin Verspoor
Proceedings of the 18th BioNLP Workshop and Shared Task

Chemical patents are an important resource for chemical information. However, few chemical Named Entity Recognition (NER) systems have been evaluated on patent documents, due in part to their structural and linguistic complexity. In this paper, we explore the NER performance of a BiLSTM-CRF model utilising pre-trained word embeddings, character-level word representations and contextualized ELMo word representations for chemical patents. We compare word embeddings pre-trained on biomedical and chemical patent corpora. The effect of tokenizers optimized for the chemical domain on NER performance in chemical patents is also explored. The results on two patent corpora show that contextualized word representations generated from ELMo substantially improve chemical NER performance w.r.t. the current state-of-the-art. We also show that domain-specific resources such as word embeddings trained on chemical patents and chemical-specific tokenizers, have a positive impact on NER performance.


Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models
Subhradeep Kayal | Zubair Afzal | George Tsatsaronis | Sophia Katrenko | Pascal Coupet | Marius Doornenbal | Michelle Gregory
BioNLP 2017

In this paper we present a solution for tagging funding bodies and grants in scientific articles using a combination of trained sequential learning models, namely conditional random fields (CRF), hidden markov models (HMM) and maximum entropy models (MaxEnt), on a benchmark set created in-house. We apply the trained models to address the BioASQ challenge 5c, which is a newly introduced task that aims to solve the problem of funding information extraction from scientific articles. Results in the dry-run data set of BioASQ task 5c show that the suggested approach can achieve a micro-recall of more than 85% in tagging both funding bodies and grants.


PNNL: A Supervised Maximum Entropy Approach to Word Sense Disambiguation
Stephen Tratz | Antonio Sanfilippo | Michelle Gregory | Alan Chappell | Christian Posse | Paul Whitney
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)


User-directed Sentiment Analysis: Visualizing the Affective Content of Documents
Michelle L. Gregory | Nancy Chinchor | Paul Whitney | Richard Carter | Elizabeth Hetzler | Alan Turner
Proceedings of the Workshop on Sentiment and Subjectivity in Text

Integrating Ontological Knowledge and Textual Evidence in Estimating Gene and Gene Product Similarity
Antonio Sanfilippo | Christian Posse | Banu Gopalan | Stephen Tratz | Michelle Gregory
Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology

ChAT: A Time-Linked System for Conversational Analysis
Michelle L. Gregory | Douglas Love | Stuart Rose | Anne Schur
Proceedings of the Analyzing Conversations in Text and Speech

Word Domain Disambiguation via Word Sense Disambiguation
Antonio Sanfilippo | Stephen Tratz | Michelle Gregory
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers


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Bridging the Gap between Technology and Users: Leveraging Machine
Thomas Hoeft | Nick Cramer | M. L. Gregory | Elizabeth Hetzler
Proceedings of HLT/EMNLP 2005 Interactive Demonstrations


Sentence-Internal Prosody Does not Help Parsing the Way Punctuation Does
Michelle Gregory | Mark Johnson | Eugene Charniak
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004

Using Conditional Random Fields to Predict Pitch Accents in Conversational Speech
Michelle Gregory | Yasemin Altun
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)