| Author: | Wang, Lu |
| Title: | Disambiguating the ambiguities in natural language processing by using UML models |
| Degree: | M.A. |
| Year: | 2013 |
| Subject: | English language -- Semantics. Natural language processing (Computer science) Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of English |
| Pages: | vii, 68 pages |
| Language: | English |
| Abstract: | This research is an analysis of wrong POS tags in Natural Language Processing (NLP) as well as a resolution for some of these wrong tags. It serves three objectives: to identify the wrong tags in the output of the parsing, to analyze the causes underlying these wrong taggings and the characteristics of distribution of wrong tagging in terms of genre and word, and to attempt to provide some resolutions for the wrong tagging by using UML models as well. This study is valuable in that it might help to improve the accuracy of the POS tagging for NLP tool. A Corpus of Travel and Tourism Texts (TnT) is used to obtain data from authentic texts of natural language in English. The TnT corpus contains a collection of eight hundred thousand words and includes four different genres: academic papers, promotional literature, travelogue and online discussion. Each genre contains about two hundred thousand words. After all the texts are tagged with POS tags, a selection of several key words in the texts of each genre are examined closely and analyzed in order to determine their causes before the connection between each genre’s linguistic features and the causes of the incorrect tags is established. The results show that there are three major causes of the wrong tagging: nominal group, preposition ‘to’ and omission. Analysis of the relationship between the genre and these causes reveals that the nominal group is the major contributor in the genre of academic papers, and the omission is the central cause of online discussion. Such characteristics are evidently related to the linguistic features of each genre. Further study of the relationship between the individual key words and the causes of wrong tagging shows that those words which have larger numbers of wrong tagging are lexically ambiguous. On the other hand, words which are not prone to lexical ambiguity are incorrectly tagged mainly because of the statistical approach of the NLP tool. |
| Rights: | All rights reserved |
| Access: | restricted access |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| b26876188.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 441.92 kB | Adobe PDF | View/Open |
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