Achieving Semantic Consistency for Multilingual Sentence Representation Using an Explainable Machine Natural Language Parser (MParser)

2021 ◽  
Vol 11 (24) ◽  
pp. 11699
Author(s):  
Peng Qin ◽  
Weiming Tan ◽  
Jingzhi Guo ◽  
Bingqing Shen ◽  
Qian Tang
Keyword(s):  
Natural Language ◽  
Semantic Representation ◽  
Word Sense ◽  
English Sentence ◽  
Heterogeneous Information ◽  
Input Method ◽  
Case Grammar ◽  
Sentence Level ◽  
Semantic Shift ◽  
Consistent Information

In multilingual semantic representation, the interaction between humans and computers faces the challenge of understanding meaning or semantics, which causes ambiguity and inconsistency in heterogeneous information. This paper proposes a Machine Natural Language Parser (MParser) to address the semantic interoperability problem between users and computers. By leveraging a semantic input method for sharing common atomic concepts, MParser represents any simple English sentence as a bag of unique and universal concepts via case grammar of an explainable machine natural language. In addition, it provides a human and computer-readable and -understandable interaction concept to resolve the semantic shift problems and guarantees consistent information understanding among heterogeneous sentence-level contexts. To evaluate the annotator agreement of MParser outputs that generates a list of English sentences under a common multilingual word sense, three expert participants manually and semantically annotated 75 sentences (505 words in total) in English. In addition, 154 non-expert participants evaluated the sentences’ semantic expressiveness. The evaluation results demonstrate that the proposed MParser shows higher compatibility with human intuitions.

Use of a lexical knowledge base for information access systems

Terminology ◽  
1998 ◽  
Vol 5 (2) ◽  
pp. 203-228 ◽  
Author(s):  
Bernardo Magnini
Keyword(s):  
Natural Language ◽  
Semantic Representation ◽  
Information Access ◽  
Dialogue Systems ◽  
Complex Sentence ◽  
Lexical Resources ◽  
Lexical Knowledge ◽  
Word Level ◽  
Sentence Level

The role of generic lexical resources as well as specialized terminology is crucial in the design of complex dialogue systems, where a human interacts with the computer using Natural Language. Lexicon and terminology are supposed to store information for several purposes, including the discrimination of semantic-ally inconsistent interpretations, the use of lexical variations, the compositional construction of a semantic representation for a complex sentence and the ability to access equivalencies across different languages. For these purposes it is necessary to rely on representational tools that are both theoretically motivated and operationally well defined. In this paper we propose a solution to lexical and terminology representation which is based on the combination of a linguistically motivated upper model and a multilingual WordNet. The upper model accounts for the linguistic analysis at the sentence level, while the multilingual WordNet accounts for lexical and conceptual relations at the word level.

scholarly journals Supervised Word Sense Disambiguation using Decision Tree

2019 ◽  
Vol 8 (2) ◽  
pp. 4043-4047
Keyword(s):  
Natural Language Processing ◽  
Decision Tree ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Processing ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
English Sentence ◽  
Sense Disambiguation ◽  
Almost All

Semantic processing is an essential task in natural language processing. In semantic processing it has observed that some words have more than one meaning. Multiple meanings of a word create serious problems to linguists which produces ambiguity in sentence. Word Sense Disambiguation is one of the main challenges in natural language processing which is present in almost all the languages. By existing knowledge and experience human can certainly disambiguate the words but for machine it is difficult task. In the proposed work, we are resolving the ambiguity of all open class word in English sentence and translating it to the Hindi sentence. We have used decision tree as a classifier. For improving the speed of translation we have used the concept of translation memory.

PRINCIPAL PROBLEMS OF NATURAL LANGUAGE PROCESSING SYSTEMS

2018 ◽  
pp. 35-38
Author(s):  
O. Hyryn
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Syntactic Analysis ◽  
Syntactic Ambiguity ◽  
Grammatical Structure ◽  
English Sentence ◽  
Analysis Methods ◽  
The Way

The article deals with natural language processing, namely that of an English sentence. The article describes the problems, which might arise during the process and which are connected with graphic, semantic, and syntactic ambiguity. The article provides the description of how the problems had been solved before the automatic syntactic analysis was applied and the way, such analysis methods could be helpful in developing new analysis algorithms. The analysis focuses on the issues, blocking the basis for the natural language processing — parsing — the process of sentence analysis according to their structure, content and meaning, which aims to analyze the grammatical structure of the sentence, the division of sentences into constituent components and defining links between them.

scholarly journals Constructing Patent Maps Using Text Mining to Sustainably Detect Potential Technological Opportunities

2018 ◽  
Vol 10 (10) ◽  
pp. 3729 ◽  
Author(s):  
Hei Wang ◽  
Yung Chi ◽  
Ping Hsin
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Rapid Development ◽  
Word Sense Disambiguation ◽  
Current Method ◽  
Word Sense ◽  
Competitive Advantages ◽  
Patent Documents ◽  
Technological Opportunities

With the advent of the knowledge economy, firms often compete for intellectual property rights. Being the first to acquire high-potential patents can assist firms in achieving future competitive advantages. To identify patents capable of being developed, firms often search for a focus by using existing patent documents. Because of the rapid development of technology, the number of patent documents is immense. A prominent topic among current firms is how to use this large number of patent documents to discover new business opportunities while avoiding conflicts with existing patents. In the search for technological opportunities, a crucial task is to present results in the form of an easily understood visualization. Currently, natural language processing can help in achieving this goal. In natural language processing, word sense disambiguation (WSD) is the problem of determining which “sense” (meaning) of a word is activated in a given context. Given a word and its possible senses, as defined by a dictionary, we classify the occurrence of a word in context into one or more of its sense classes. The features of the context (such as neighboring words) provide evidence for these classifications. The current method for patent document analysis warrants improvement in areas, such as the analysis of many dimensions and the development of recommendation methods. This study proposes a visualization method that supports semantics, reduces the number of dimensions formed by terms, and can easily be understood by users. Since polysemous words occur frequently in patent documents, we also propose a WSD method to decrease the calculated degrees of distortion between terms. An analysis of outlier distributions is used to construct a patent map capable of distinguishing similar patents. During the development of new strategies, the constructed patent map can assist firms in understanding patent distributions in commercial areas, thereby preventing patent infringement caused by the development of similar technologies. Subsequently, technological opportunities can be recommended according to the patent map, aiding firms in assessing relevant patents in commercial areas early and sustainably achieving future competitive advantages.

scholarly journals Towards an Automated Model-Based Test Generation Approach from Controlled Natural Language Specifications Written in Arabic

2021 ◽  
Author(s):  
Moez Krichen ◽  
Seifeddine Mechti
Keyword(s):  
Natural Language ◽  
Test Generation ◽  
Arabic Language ◽  
Test Cases ◽  
New Model ◽  
Controlled Natural Language ◽  
Model Based ◽  
Case Grammar ◽  
Model Based Testing ◽  
Testing Approach

<div>We propose a new model-based testing approach which takes as input a set of requirements described in Arabic Controlled Natural Language (CNL) which is a subset of the Arabic language generated by a specific grammar. The semantics of the considered requirements is defined using the Case Grammar Theory (CTG). The requirements are translated into Transition Relations which serve as an input for test cases generation tools.</div>

scholarly journals Towards an Automated Model-Based Test Generation Approach from Controlled Natural Language Specifications Written in Arabic

2021 ◽  
Author(s):  
Moez Krichen ◽  
Seifeddine Mechti
Keyword(s):  
Natural Language ◽  
Test Generation ◽  
Arabic Language ◽  
Test Cases ◽  
New Model ◽  
Controlled Natural Language ◽  
Model Based ◽  
Case Grammar ◽  
Model Based Testing ◽  
Testing Approach

<div>We propose a new model-based testing approach which takes as input a set of requirements described in Arabic Controlled Natural Language (CNL) which is a subset of the Arabic language generated by a specific grammar. The semantics of the considered requirements is defined using the Case Grammar Theory (CTG). The requirements are translated into Transition Relations which serve as an input for test cases generation tools.</div>

Machine Learning in Natural Language Processing

2010 ◽  
pp. 302-324
Author(s):  
Marina Sokolova ◽  
Stan Szpakowicz
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Processing ◽  
Word Sense Disambiguation ◽  
Machine Learning Techniques ◽  
Word Sense ◽  
Part Of Speech ◽  
Applications Of Machine Learning

This chapter presents applications of machine learning techniques to traditional problems in natural language processing, including part-of-speech tagging, entity recognition and word-sense disambiguation. People usually solve such problems without difficulty or at least do a very good job. Linguistics may suggest labour-intensive ways of manually constructing rule-based systems. It is, however, the easy availability of large collections of texts that has made machine learning a method of choice for processing volumes of data well above the human capacity. One of the main purposes of text processing is all manner of information extraction and knowledge extraction from such large text. Machine learning methods discussed in this chapter have stimulated wide-ranging research in natural language processing and helped build applications with serious deployment potential.

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