NDLI: Non-Local Dictionary Based Japanese Dish Names Recognition Using Multi-Feature CRF from Online Reviews

Content Provider	ACM Digital Library
Author	Kaji, Katsuhiko Kawaguchi, Nobuo Chen, Weichang Hiroi, Kei
Abstract	In cuisine recommender service, online user review is an important data source avoiding a cold-start problem. Cuisine-domain named entity recognition(NER) can be used as an entrance to comprehend the semantic information of reviews. This paper describes a supervised approach recognizing Japanese dish name entity (DNE) from online reviews of Japanese cuisine website. In the first stage, this work adopts tweets as the data source to construct the dictionary of dish name elements through semantic rules and use Bayesian posterior to remove noise. Next stage, we maps first-stage dictionary as a non-local feature into Conditional Random Field (CRF) to recognize the dish name. This method can automatically add new dish name elements into the non-local dictionary by iteration during the recognition proceeding. By using 10-fold validation, experimental results show our method can reach 84.38% in F1 score and outperform the two baselines using the dictionary or CRF with term feature separately.
Starting Page	1
Ending Page	9
Page Count	9
File Format	PDF
ISBN	9781450325387
DOI	10.1145/2611040.2611063
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-06-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Text extraction Conditional random field Non-local dictionary Twitter
Content Type	Text
Resource Type	Article

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Non-Local Dictionary Based Japanese Dish Names Recognition Using Multi-Feature CRF from Online Reviews