Classifying Travel-related Intents in Textual Data

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Intent classification refers to the process of identifying a set of intents of interest that appear in a given document. This work considers the task of annotating travel-related reviews with travel intents that best represent the reviewer's reason for visiting the place of interest (POI). A domain-tailored word embedding model is learned to construct intent-specific feature vectors, thereby improving classification accuracy. The feasibility of multiclass intent classification is explored using an intent corpus, consisting of 6,560 labelled reviews.
Publisher
Emirates Association of Computer, Electrical & Electronics Engineers (EACEEE)
Issue Date
2016-01-23
Language
English
Citation

the 2016 2nd International Conference on Data Mining, Electronics and Information Technology (DMEIT'16), pp.40 - 47

DOI
10.15242/ijccie.er01161004
URI
http://hdl.handle.net/10203/209424
Appears in Collection
CS-Conference Papers(학술회의논문)
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