Papers by Manuel R. Ciosici
Accelerated High-Quality Mutual-Information Based Word Clustering (2020.lrec-1)
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| Challenge: | Word clustering is a hard hierarchical clustering that uses short-range distributional information to construct clusters. |
| Approach: | They propose to use a hierarchical clustering algorithm with a fixed-width beam to build clusters that outperform other word representations. |
| Outcome: | The proposed method outperforms the original methods in the computation of hierarchical and flat clusters. |
CRAFT Shared Tasks 2019 Overview — Integrated Structure, Semantics, and Coreference (D19-57)
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William Baumgartner, Michael Bada, Sampo Pyysalo, Manuel R. Ciosici, Negacy Hailu, Harrison Pielke-Lombardo, Michael Regan, Lawrence Hunter
| Challenge: | CRAFT corpus provides a unique foundation for integrating natural language processing (NLP) tasks involving structure, semantics, and coreference. |
| Approach: | They propose to use the CRAFT corpus to evaluate three fundamental language processing tasks over full-text biomedical articles. |
| Outcome: | The CRAFT corpus provides a unique foundation for integrating natural language processing tasks involving structure, semantics, and coreference. |
A reproduction of Apple’s bi-directional LSTM models for language identification in short strings (2021.eacl-srw)
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| Challenge: | Language Identification is the task of identifying a document’s language. |
| Approach: | They propose to use bi-LSTMs to identify language on very short strings such as text message fragments to perform automatic spell check. |
| Outcome: | The proposed model outperforms open-source language identifiers and its language identification mistakes are due to confusion between related languages. |
Efficient Methods for Natural Language Processing: A Survey (2023.tacl-1)
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Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon Derczynski, Iryna Gurevych, Roy Schwartz
| Challenge: | Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data, but using only scale to improve performance means resource consumption also grows. |
| Approach: | They propose to use data, time, storage, or energy to improve model performance. |
| Outcome: | The proposed methods and findings provide guidance for conducting NLP under limited resources and point towards promising research directions for developing more efficient methods. |
Abbreviation Explorer - an interactive system for pre-evaluation of Unsupervised Abbreviation Disambiguation (N19-4)
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| Challenge: | Abbreviation Explorer helps to identify long-forms that are easily confused . it can also pinpoint likely causes such as limitations of normalization, language switching, or inconsistent typing. |
| Approach: | They propose a system that supports interactive exploration of abbreviations that are challenging for Unsupervised Abbreviation Disambiguation. |
| Outcome: | The proposed system can identify long-forms that are easily confused and pinpoint likely causes . it can also identify which long-terms would benefit from additional input text . the proposed rules can be easily applied to existing vector spaces to improve performance while avoiding the cost of retraining. |
Abbreviation Expander - a Web-based System for Easy Reading of Technical Documents (C18-2)
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| Challenge: | Existing abbreviation expansion systems or tools require technical knowledge to set up . existing systems require strong assumptions and are limited in their usefulness . |
| Approach: | They propose a web-based system that automatically expands abbreviations and acronyms in a user provided document. |
| Outcome: | The proposed system expands abbreviations and acronyms automatically in a user provided document. |
Quantifying the morphosyntactic content of Brown Clusters (N19-1)
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| Challenge: | Using corpora representing several language families, we show that word clusters are highly capable at distinguishing Parts of Speech. |
| Approach: | They propose to use Brown and Exchange word clusters to represent morphosyntactic information in NLP systems. |
| Outcome: | The proposed clusters are highly capable at distinguishing Parts of Speech and can be used to perform tasks dependent on morphosyntactic information. |