A Semantic and Detection‐Based Approach to Speech and Language Processing

Sheu, Phillip C.-Y.; Yu, Heather; Ramamoorthy, C. V.; Joshi, Anil; Zadeh, Lotfi A.

doi:10.1002/9780470588222.ch4

Cited by 1 publication

(1 citation statement)

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“…SC addresses the derivation and matching of the semantics of computational content and that of naturally expressed user intentions and brings together those disciplines concerned with connecting the intentions of humans with computational content by retrieving, using and manipulating existing content according to user's goals, and by creating, rearranging and managing content that matches the author's intentions (cf., [42]). SC technologies have been classified into five classes, namely, Semantic Analysis, Semantic Integration, Semantic Services, Service Integration, Semantic Interface (cf., [17,42] for details) and shown useful in, Education [25], Business Intelligence [26], Healthcare [45], Internet of things [59], several branches of Computer Science and Biomedical System [17], Speech and Language Processing [10], spoken document summarization [15], Theoretical Computer Science [20,22,61], and in many more disciplines for details see references of these cited work.…”

Section: Introductionmentioning

confidence: 99%

Generalized rough and fuzzy rough automata for semantic computing

Yadav

Tiwari

Kumari

et al. 2022

Int. J. Mach. Learn. & Cyber.

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The classical automata, fuzzy finite automata, and rough finite state automata are some formal models of computing used to perform the task of computation and are considered to be the input device. These computational models are valid only for fixed input alphabets for which they are defined and, therefore, are less user-friendly and have limited applications. The semantic computing techniques provide a way to redefine them to improve their scope and applicability. In this paper, the concept of semantically equivalent concepts and semantically related concepts in information about real-world applications datasets are used to introduce and study two new formal models of computations with semantic computing (SC), namely, a rough finite-state automaton for SC and a fuzzy finite rough automaton for SC as extensions of rough finite-state automaton and fuzzy finite-state automaton, respectively, in two different ways. The traditional rough finite-state automata can not deal with situations when external alphabet or semantically equivalent concepts are given as inputs. The proposed rough finite-state automaton for SC can handle such situations and accept such inputs and is shown to have successful real-world applications. Similarly, a fuzzy finite rough automaton corresponding to a fuzzy automaton is also failed to process input alphabet different from their input alphabet, the proposed fuzzy finite rough automaton for SC corresponding to a given fuzzy finite automaton is capable of processing semantically related input, and external input alphabet information from the dataset obtained by real-world applications and provide better user experience and applicability as compared to classical fuzzy finite rough automaton.

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