Esmeralda Manandise

Also published as: Esme Manandise


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Mitigating Silence in Compliance Terminology during Parsing of Utterances
Esme Manandise | Conrad de Peuter
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

This paper reports on an approach to increase multi-token-term recall in a parsing task. We use a compliance-domain parser to extract, during the process of parsing raw text, terms that are unlisted in the terminology. The parser uses a similarity measure (Generalized Dice Coefficient) between listed terms and unlisted term candidates to (i) determine term status, (ii) serve putative terms to the parser, (iii) decrease parsing complexity by glomming multi-tokens as lexical singletons, and (iv) automatically augment the terminology after parsing of an utterance completes. We illustrate a small experiment with examples from the tax-and-regulations domain. Bootstrapping the parsing process to detect out- of-vocabulary terms at runtime increases parsing accuracy in addition to producing other benefits to a natural-language-processing pipeline, which translates arithmetic calculations written in English into computer-executable operations.


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Towards Unlocking the Narrative of the United States Income Tax Forms
Esme Manandise
Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)


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The Bare Necessities: Increasing Lexical Coverage for Multi-Word Domain Terms with Less Lexical Data
Branimir Boguraev | Esme Manandise | Benjamin Segal
Proceedings of the 11th Workshop on Multiword Expressions


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Book Reviews: New Directions in Machine Translation (Proceedings of the Conference, Budapest, August 1988)
Esmeralda Manandise
Computational Linguistics, Volume 15, Number 4, December 1989