TITLE:
Semantic Sentence Similarity Using Finite State Machine
AUTHORS:
Chiranjibi Sitaula, Yadav Raj Ojha
KEYWORDS:
Artificial Intelligence; Natural Language Processing; Text Mining; Semantic Similarity; Finite State Machine
JOURNAL NAME:
Intelligent Information Management,
Vol.5 No.6,
November
28,
2013
ABSTRACT:
In this paper, a finite state
machine approach is followed in order to find the semantic similarity of two sentences.
The approach exploits the concept of bi-directional logic along with a semantic ordering approach. The
core part of this approach is bi-directional logic of artificial intelligence. The
bi-directional logic is implemented using Finite State Machine algorithm with slight
modification. For finding the semantic similarity, keyword has played climactic
importance. With the help of the keyword approach, it can be found easily at the sentence level according to this
algorithm. The algorithm is proposed especially for Nepali texts. With the polarity of the individual keywords,
the finite state machine is made and its final state determines its polarity.
If two sentences are negatively polarized, they are said to be coherent, otherwise not. Similarly, if two sentences are of a positive
nature, they are said to be coherence. For measuring the coherence (similarity), contextual
concept is taken into consideration. The semantic approach, in this research,
is a totally
contextual based method. Two sentences are said to be semantically similar if
they bear the same context. The total accuracy obtained in this algorithm
is 90.16%.