2 research outputs found
Context-Sensitive Convolution Tree Kernel for Pronoun Resolution
This paper proposes a context-sensitive convolution tree kernel for pronoun resolution. It resolves two critical problems in previous researches in two ways. First, given a parse tree and a pair of an anaphor and an antecedent candidate, it implements a dynamic-expansion scheme to automatically determine a proper tree span for pronoun resolution by taking predicate- and antecedent competitor-related information into consideration. Second, it applies a context-sensitive convolution tree kernel, which enumerates both context-free and context-sensitive sub-trees by considering their ancestor node paths as their contexts. Evaluation on the ACE 2003 corpus shows that our dynamic-expansion tree span scheme can well cover necessary structured information in the parse tree for pronoun resolution and the context-sensitive tree kernel much outperforms previous tree kernels.
Natural Language Processing for Information Extraction
With rise of digital age, there is an explosion of information in the form of
news, articles, social media, and so on. Much of this data lies in unstructured
form and manually managing and effectively making use of it is tedious, boring
and labor intensive. This explosion of information and need for more
sophisticated and efficient information handling tools gives rise to
Information Extraction(IE) and Information Retrieval(IR) technology.
Information Extraction systems takes natural language text as input and
produces structured information specified by certain criteria, that is relevant
to a particular application. Various sub-tasks of IE such as Named Entity
Recognition, Coreference Resolution, Named Entity Linking, Relation Extraction,
Knowledge Base reasoning forms the building blocks of various high end Natural
Language Processing (NLP) tasks such as Machine Translation, Question-Answering
System, Natural Language Understanding, Text Summarization and Digital
Assistants like Siri, Cortana and Google Now. This paper introduces Information
Extraction technology, its various sub-tasks, highlights state-of-the-art
research in various IE subtasks, current challenges and future research
directions.Comment: 24 pages, 1 figur