Title

Using An Automatically Generated Dictionary And A Classifier To Identify A Person'S Profession In Tweets

Abstract

Algorithms for classifying pre-tagged person entities in tweets into one of 8 profession categories are presented. A classifier using a semi-supervised learning algorithm that takes into consideration the local context surrounding the entity in the tweet, hash tag information, and topic signature scores is described. A method that uses data from the Web to dynamically create a reference file called a person dictionary, which contains person/profession relationships, is described, as is an algorithm to use the dictionary to assign a person into one of the 8 profession categories. Results show that classifications made with the automated person dictionary compare favorably to classifications made using a manually compiled dictionary. Results also show that classifications made using either the dictionary or the classifier are moderately successful and that a hybrid method using both offers significant improvement. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

Publication Date

12-13-2013

Publication Title

FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference

Number of Pages

263-266

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84889776108 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84889776108

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