A4 Artikkeli konferenssijulkaisussa

An Unsupervised Query Rewriting Approach Using N-gram Co-occurrence Statistics to Find Similar Phrases in Large Text Corpora

Julkaisun tekijät: Hans Moen, Laura-Maria Peltonen, Henry Suhonen, Hanna-Maria Matinolli, Riitta Mieronkoski, Kirsi Telen, Kirsi Terho, Tapio Salakoski, Sanna Salanterä

Konferenssin vakiintunut nimi: Nordic Conference on Computational Linguistics

Julkaisuvuosi: 2019

Journal: Linköping Electronic Conference Proceedings

Kirjan nimi *: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa)

Sarjan nimi: NEALT Proceedings Series

Numero sarjassa: 42

ISBN: 978-91-7929-995-8

ISSN: 1650-3686

Verkko-osoite: https://www.aclweb.org/anthology/W19-6114/


We present our work towards developing a system that should find, in a large text corpus, contiguous phrases expressing similar meaning as a query phrase of arbitrary length. Depending on the use case, this task can be seen as a form of (phraselevel) query rewriting. The suggested approach works in a generative manner, is unsupervised and uses a combination of a semantic word n-gram model, a statistical language model and a document search engine. A central component is a distributional semantic model containing word n-grams vectors (or embeddings) which models semantic similarities between ngrams of different order. As data we use a large corpus of PubMed abstracts. The presented experiment is based on manual evaluation of extracted phrases for arbitrary queries provided by a group of evaluators. The results indicate that the proposed approach is promising and that the use of distributional semantic models trained with uni-, bi-and trigrams seems to work better than a more traditional unigram model.

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Last updated on 2021-24-06 at 09:04