Authors:
Sylvester Olubolu Orimaye
;
Saadat M. Alhashmi
and
Siew Eu-Gene
Affiliation:
Monash University, Malaysia
Keyword(s):
Sentential, Frequency, Context, Query terms, Grammar-based, Opinion retrieval.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Soft Computing
;
Symbolic Systems
;
Web Mining
Abstract:
Many opinion retrieval techniques use frequency of query terms as a measurement for detecting documents that contain opinion. However, using frequency of query terms leads to bias in context-dependent opinion retrieval such that all documents containing query terms are retrieved, regardless of contextual relevance to the intent of the human seeking the opinion. This can be described as non-contextual relevance problem in opinion retrieval systems such as Google Blogs Search and Technorati Blog Directory. Sentence-level contextual understanding and grammatical dependencies need be considered to ensure documents retrieved contain large proportion of textual contents that have the same underlying meaning with the given query instead of using frequency of individual query terms. Thus, we present specific challenges with state-of-the-art opinion retrieval techniques that rely on frequency of query terms and we propose a grammar-based technique for efficient context-dependent opinion retri
eval. We believe our proposed technique can solve the non-contextual relevance problem common to opinion retrieval systems, and can be used for context-dependent retrieval such as expert search systems, faceted-opinion retrieval, opinion trend analytic, and personalized web search.
(More)