Resolving Ambiguity/Uncertainty
in Fact Extraction
Hejab Ma’azer Al Fawareh1,
Shaidah Jusoh2,
Graduate Dept. of Computer Science,
e-mail: 1alfawareh@gmail.com, 2shaidah@uum.edu.my,
3nmn@uum.edu.my
Most of the valuable and crucial information is stored in texts. Extracting
information from the texts requiring a person to read them. This is very time
consuming. It can become a challenging task if the person does not have enough
background related to the texts. Having an automated system that can extract
required information from the texts is becoming an urgent need. Information
extraction is one of the application research in the field of knowledge mining.
In information extraction, there are two levels of extractions; entity
extraction and fact extractions. Fact extraction is a process of spreading out
the facts from entities and topics. The major challenging issue in extracting
facts from texts is natural language words and structures are always ambiguous.
In an
automated information extraction system, the fact should be correct and
relevant to a user’s needs. Lets us
consider a sentence “The robber shot a police in the Giant mall”. The sentence can be parsed using a grammar rule
[Sentence -> Noun Phrase, Verb Phrase] or [Sentence -> Noun Phrase, Verb
Phrase, Preposition Phrase]. Thus, the sentence can be interpreted as “The robber who is inside the Giant mall shot
the police” or “The robber shot a police
who is inside the Giant mall”. Up to now, not much research has been conducted in resolving ambiguity
and uncertainty problems for fact extraction. The ambiguity problem occurs when
a sentence structure could be interpreted into more than one meaning and uncertainty problems
occur when there are more than one fact could be extracted. In this paper, we
propose a new technique to resolve ambiguity and uncertainty in fact
extraction. The approach is developed
by utilizing natural language processing, fuzzy sets and context knowledge-base
approaches.