%0 Conference Proceedings %T An Unsupervised Query Rewriting Approach Using N-gram Co-occurrence Statistics to Find Similar Phrases in Large Text Corpora %A Moen, Hans %A Peltonen, Laura-Maria %A Suhonen, Henry %A Matinolli, Hanna-Maria %A Mieronkoski, Riitta %A Telen, Kirsi %A Terho, Kirsi %A Salakoski, Tapio %A Salanterä, Sanna %Y Hartmann, Mareike %Y Plank, Barbara %S Proceedings of the 22nd Nordic Conference on Computational Linguistics %D 2019 %8 sep–oct %I Linköping University Electronic Press %C Turku, Finland %F moen-etal-2019-unsupervised %X 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 (phrase-level) 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 n-grams 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. %U https://aclanthology.org/W19-6114 %P 131-139