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Media type:
Doctoral Thesis;
Electronic Thesis;
E-Book
Title:
Strategies to Address Data Sparseness in Implicit Semantic Role Labeling
Contributor:
Feizabadi, Parvin Sadat
[Author]
imprint:
Heidelberg University: HeiDok, 2019
Language:
English
DOI:
https://doi.org/10.11588/heidok.00025842
Origination:
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
Natural language texts frequently contain predicates whose complete understanding re- quires access to other parts of the discourse. Human readers can retrieve such infor- mation across sentence boundaries and infer the implicit piece of information. This capability enables us to understand complicated texts without needing to repeat the same information in every single sentence. However, for computational systems, resolv- ing such information is problematic because computational approaches traditionally rely on sentence-level processing and rarely take into account the extra-sentential context. In this dissertation, we investigate this omission phenomena, called implicit semantic role labeling. Implicit semantic role labeling involves identification of predicate argu- ments that are not locally realized but are resolvable from the context. For example, in ”What’s the matter, Walters? asked Baynes sharply.”, the ADDRESSEE of the predicate ask, Walters, is not mentioned as one of its syntactic arguments, but can be recoverable from the previous sentence. In this thesis, we try to improve methods for the automatic processing of such predicate instances to improve natural language pro- cessing applications. Our main contribution is introducing approaches to solve the data sparseness problem of the task. We improve automatic identification of implicit roles by increasing the amount of training set without needing to annotate new instances. For this purpose, we propose two approaches. As the first one, we use crowdsourcing to annotate instances of implicit semantic roles and show that with an appropriate task de- sign, reliable annotation of implicit semantic roles can be obtained from the non-experts without the need to present precise and linguistic definition of the roles to them. As the second approach, we combine seemingly incompatible corpora to solve the problem of data sparseness of ISRL by applying a domain adaptation technique. We show that out of domain data from a different genre can be successfully used ...