An Unsupervised Query Rewriting Approach Using N-gram Co-occurrence Statistics to Find Similar Phrases in Large Text Corpora
Laura-Maria Peltonen; Hans Moen; Kirsi Terho; Hanna-Maria Matinolli; Kirsi Telen; Riitta Mieronkoski; Henry Suhonen; Tapio Salakoski; Sanna Salanterä
https://urn.fi/URN:NBN:fi-fe2021042827307
Tiivistelmä
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.
Kokoelmat
- Rinnakkaistallenteet [19207]