Authors:
Pikakshi Manchanda
;
Elisabetta Fersini
and
Matteo Palmonari
Affiliation:
University of Milano-Bicocca, Italy
Keyword(s):
Web of Data, Information Extraction, Named Entity Recognition, Named Entity Linking, Knowledge Base, Microblogs.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining Text and Semi-Structured Data
;
Symbolic Systems
Abstract:
The Web of Data provides abundant knowledge wherein objects or entities are described by means of properties
and their relationships with other objects or entities. This knowledge is used extensively by the research
community for Information Extraction tasks such as Named Entity Recognition (NER) and Linking (NEL)
to make sense of data. Named entities can be identified from a variety of textual formats which are further
linked to corresponding resources in the Web of Data. These tasks of entity recognition and linking are, however,
cast as distinct problems in the state-of-the-art, thereby, overlooking the fact that performance of entity
recognition affects the performance of entity linking. The focus of this paper is to improve the performance
of entity recognition on a particular textual format, viz, microblog posts by disambiguating the named entities
with resources in a Knowledge Base (KB). We propose an unsupervised learning approach to jointly improve
the performance o
f entity recognition and, thus, the whole system by leveraging the results of disambiguated
entities.
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