TU Darmstadt / ULB / TUbiblio

Exploring Automatic Theme Identification: A Rule-Based Approach.

Schwarz, Lara and Bartsch, Sabine and Eckart de Castilho, Richard and Teich, Elke (2008):
Exploring Automatic Theme Identification: A Rule-Based Approach.
In: Text Resources and Lexical Knowledge. Selected Papers from the 9th Conference on Natural Language Processing KONVENS 2008, Mouton de Gruyter, [Online-Edition: https://www.degruyter.com/view/product/39088],
[Conference or Workshop Item]

Abstract

Knowledge about Theme-Rheme serves the interpretation of a text in terms of its thematic progression and provides a window into the topicality of a text as well as text type (genre). This is potentially relevant for NLP tasks such as information extraction and text clas- sification. To explore this potential, large corpora annotated for Theme-Rheme organization are needed. We report on a rule-based system for the automatic identification of Theme to be employed for corpus annotation. The rules are manually derived from a set of sentences parsed syntactically with the Stanford parser and analyzed in terms of Theme on the basis of Systemic Functional Grammar (SFG). We describe the development of the rule set and the automatic procedure of Theme identification and assess the validity of the approach by application to some authentic text data.

Item Type: Conference or Workshop Item
Erschienen: 2008
Creators: Schwarz, Lara and Bartsch, Sabine and Eckart de Castilho, Richard and Teich, Elke
Title: Exploring Automatic Theme Identification: A Rule-Based Approach.
Language: English
Abstract:

Knowledge about Theme-Rheme serves the interpretation of a text in terms of its thematic progression and provides a window into the topicality of a text as well as text type (genre). This is potentially relevant for NLP tasks such as information extraction and text clas- sification. To explore this potential, large corpora annotated for Theme-Rheme organization are needed. We report on a rule-based system for the automatic identification of Theme to be employed for corpus annotation. The rules are manually derived from a set of sentences parsed syntactically with the Stanford parser and analyzed in terms of Theme on the basis of Systemic Functional Grammar (SFG). We describe the development of the rule set and the automatic procedure of Theme identification and assess the validity of the approach by application to some authentic text data.

Title of Book: Text Resources and Lexical Knowledge. Selected Papers from the 9th Conference on Natural Language Processing KONVENS 2008
Publisher: Mouton de Gruyter
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 31 Dec 2016 14:29
Official URL: https://www.degruyter.com/view/product/39088
Identification Number: TUD-CS-2008-11511
Export:
Suche nach Titel in: TUfind oder in Google

Optionen (nur für Redakteure)

View Item View Item