1 research outputs found
Fuzzy natural language similarity measures through computing with words
A vibrant area of research is the understanding of human language by machines to engage in
conversation with humans to achieve set goals. Human language is naturally fuzzy by nature,
with words meaning different things to different people, depending on the context. Fuzzy
words are words with a subjective meaning, typically used in everyday human natural
language dialogue and often ambiguous and vague in meaning and dependent on an
individual’s perception. Fuzzy Sentence Similarity Measures (FSSM) are algorithms that can
compare two or more short texts which contain fuzzy words and return a numeric measure
of similarity of meaning between them.
The motivation for this research is to create a new FSSM called FUSE (FUzzy Similarity
mEasure). FUSE is an ontology-based similarity measure that uses Interval Type-2 Fuzzy Sets
to model relationships between categories of human perception-based words. Four versions
of FUSE (FUSE_1.0 – FUSE_4.0) have been developed, investigating the presence of linguistic
hedges, the expansion of fuzzy categories and their use in natural language, incorporating
logical operators such as ‘not’ and the introduction of the fuzzy influence factor.
FUSE has been compared to several state-of-the-art, traditional semantic similarity measures
(SSM’s) which do not consider the presence of fuzzy words. FUSE has also been compared to
the only published FSSM, FAST (Fuzzy Algorithm for Similarity Testing), which has a limited
dictionary of fuzzy words and uses Type-1 Fuzzy Sets to model relationships between
categories of human perception-based words. Results have shown FUSE is able to improve on
the limitations of traditional SSM’s and the FAST algorithm by achieving a higher correlation
with the average human rating (AHR) compared to traditional SSM’s and FAST using several
published and gold-standard datasets.
To validate FUSE, in the context of a real-world application, versions of the algorithm were
incorporated into a simple Question & Answer (Q&A) dialogue system (DS), referred to as
FUSION, to evaluate the improvement of natural language understanding. FUSION was tested
on two different scenarios using human participants and results compared to a traditional
SSM known as STASIS. Results of the DS experiments showed a True rating of 88.65%
compared to STASIS with an average True rating of 61.36%. Results showed that the FUSE
algorithm can be used within real world applications and evaluation of the DS showed an
improvement of natural language understanding, allowing semantic similarity to be
calculated more accurately from natural user responses.
The key contributions of this work can be summarised as follows: The development of a new
methodology to model fuzzy words using Interval Type-2 fuzzy sets; leading to the creation of
a fuzzy dictionary for nine fuzzy categories, a useful resource which can be used by other
researchers in the field of natural language processing and Computing with Words with other
fuzzy applications such as semantic clustering. The development of a FSSM known as FUSE,
which was expanded over four versions, investigating the incorporation of linguistic hedges,
the expansion of fuzzy categories and their use in natural language, inclusion of logical
operators such as ‘not’ and the introduction of the fuzzy influence factor. Integration of the
FUSE algorithm into a simple Q&A DS referred to as FUSION demonstrated that FSSM can be
used in a real-world practical implementation, therefore making FUSE and its fuzzy dictionary
generalisable to other applications