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Detecting conditional healthiness of food items from natural language text

  • In this article, we explore the feasibility of extracting suitable and unsuitable food items for particular health conditions from natural language text. We refer to this task as conditional healthiness classification. For that purpose, we annotate a corpus extracted from forum entries of a food-related website. We identify different relation types that hold between food items and health conditions going beyond a binary distinction of suitability and unsuitability and devise various supervised classifiers using different types of features. We examine the impact of different task-specific resources, such as a healthiness lexicon that lists the healthiness status of a food item and a sentiment lexicon. Moreover, we also consider task-specific linguistic features that disambiguate a context in which mentions of a food item and a health condition co-occur and compare them with standard features using bag of words, part-of-speech information and syntactic parses. We also investigate in how far individual food items and health conditions correlate with specific relation types and try to harness this information for classification.

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Author:Michael WiegandGND, Dietrich Klakow
Parent Title (English):Language Resources and Evaluation
Place of publication:Dordrecht
Document Type:Article
Year of first Publication:2015
Date of Publication (online):2019/03/06
Tag:Food domain; Linguistically informed feature engineering; Polarity classification; Social media; Text classification
GND Keyword:Computerlinguistik; Information Extraction; Lebensmittel; Natürliche Sprache; Polarität
First Page:777
Last Page:830
This is a post-peer-review, pre-copyedit version of an article published in Language Resources and Evaluation. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10579-015-9314-7
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Licence (German):License LogoUrheberrechtlich geschützt