國立虎尾科技大學 |

Robust Parsing for Ungrammatical Sentences.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Robust Parsing for Ungrammatical Sentences./
作者:	Hashemi, Homa Baradaran.
面頁冊數:	1 online resource (163 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
標題:	Artificial intelligence. -
電子資源:	click for full text (PQDT)
ISBN:	9780355886979

Robust Parsing for Ungrammatical Sentences.
Hashemi, Homa Baradaran.

Robust Parsing for Ungrammatical Sentences. - 1 online resource (163 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2017.

Includes bibliographical references

Natural Language Processing (NLP) is a research area that specializes in studying computational approaches to human language. However, not all of the natural language sentences are grammatically correct. Sentences that are ungrammatical, awkward, or too casual/colloquial tend to appear in a variety of NLP applications, from product reviews and social media analysis to intelligent language tutors or multilingual processing. In this thesis, we focus on syntactic parsing, an essential component of many NLP applications. We investigate the impact of ungrammatical sentences on statistical parsers. We also hypothesize that breaking up parse trees from problematic parts prevents NLP applications from degrading due to incorrect syntactic analysis.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355886979Subjects--Topical Terms:

559380
Artificial intelligence.
Index Terms--Genre/Form:

554714
Electronic books.

Robust Parsing for Ungrammatical Sentences.
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520 $a Natural Language Processing (NLP) is a research area that specializes in studying computational approaches to human language. However, not all of the natural language sentences are grammatically correct. Sentences that are ungrammatical, awkward, or too casual/colloquial tend to appear in a variety of NLP applications, from product reviews and social media analysis to intelligent language tutors or multilingual processing. In this thesis, we focus on syntactic parsing, an essential component of many NLP applications. We investigate the impact of ungrammatical sentences on statistical parsers. We also hypothesize that breaking up parse trees from problematic parts prevents NLP applications from degrading due to incorrect syntactic analysis.
520 $a A parser is robust if it can overlook problems such as grammar mistakes and produce a parse tree that closely resembles the correct analysis for the intended sentence. We develop a robustness evaluation metric and conduct a series of experiments to compare the performances of state-of-the-art parsers on the ungrammatical sentences. The evaluation results show that ungrammatical sentences present challenges for statistical parsers, because the well-formed syntactic trees they produce may not be appropriate for ungrammatical sentences. We also define a new framework for reviewing the parses of ungrammatical sentences and extracting the coherent parts whose syntactic analyses make sense. We call this task parse tree fragmentation. The experimental results suggest that the proposed overall fragmentation framework is a promising way to handle syntactically unusual sentences; they also validate the utility of parse tree fragmentation methods in two external tasks of sentential grammaticality judgment and semantic role labeling.
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