Semantic Representation for Understanding Meaning Based on Correspondence Between Meanings

2006 ◽  
Vol 10 (6) ◽  
pp. 876-912
Author(s):  
Akira Takagi ◽  
◽  
Hideki Asoh ◽  
Yukihiro Itoh ◽  
Makoto Kondo ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Representation ◽  
Semantic Content ◽  
Semantic Interpretation ◽  
Basic Unit ◽  
Dependency Relation ◽  
Dependency Structures ◽  
Attribute Value

One of the biggest problems in natural language processing is that its processing target (i.e. the surface expressions of sentences) has a great deal of diversity. In order to reduce the difficulty, it is desirable to extract the semantic content denoted by a sentence in such a way that it does not depend on the surface expressions as much as possible. This paper proposes a new semantic representation and general interpretive procedures that enable us to obtain the result of semantic interpretation from a variety of surface expressions of the input independently of their dependency structures. In the semantic representation to be proposed, a variety of surface dependency relations are compressed into attribute nouns, and the meaning expressed by dependency relation is represented in a uniform style (i.e. attribute = value). This approach enables us to establish correspondence between meanings by using the attribute-value pair as a basic unit. With this semantic representation and the general interpretive procedures, the same interpretive result can be obtained from sentences with different dependency structures. We will further demonstrate that semantic contents of multiple sentences can be integrated by interpreting them based on the correspondence between meanings.

The Applications of Description Logics in Natural Language Processing

2011 ◽  
Vol 181-182 ◽  
pp. 236-241
Author(s):  
Xian Yi Cheng ◽  
Chen Cheng ◽  
Qian Zhu
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Description Logic ◽  
Description Logics ◽  
Semantic Interpretation ◽  
Natural Language Semantic ◽  
Ontology Language ◽  
Basic Ideas

As a sort of formalizing tool of knowledge representation, Description Logics have been successfully applied in Information System, Software Engineering and Natural Language processing and so on. Description Logics also play a key role in text representation, Natural Language semantic interpretation and language ontology description. Description Logics have been logical basis of OWL which is an ontology language that is recommended by W3C. This paper discusses the description logic basic ideas under vocabulary semantic, context meaning, domain knowledge and background knowledge.

Taking genre into account when analysing conceptual relation patterns

Corpora ◽  
2008 ◽  
Vol 3 (2) ◽  
pp. 115-140 ◽  
Author(s):  
Anne Condamines
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Subject Matter ◽  
Natural Sciences ◽  
Semantic Interpretation ◽  
Corpus Study ◽  
Text Genre ◽  
Conceptual Relation ◽  
Didactic Texts

This paper uses a corpus study to investigate the influence of text genre on the frequency and semantic interpretation of certain pattern/concept relations. In linking pattern/concept relations to text genre, this study identifies three types of dependency: weak dependency, where the relation appears in almost any kind of text; complete dependency, where it is strongly linked to a particular text or group of related texts; and dependency in terms of text genre. The particular examples that form the basis of the study are meronymic chez, which is found to have a significant dependency in didactic texts in the natural sciences; comme as a marker of hypernymy and co-hyponymy, which has a weaker, but observable dependency in technical and didactic genres; nominal anaphora involving hypernyms, where no consistent conclusions can be reached; and meronymic avec, where the significant factor is shown to be communicative objective rather than domain (subject matter). I discuss the relevance of such studies to Natural Language Processing, and indicate the potential for further research.

Reusing a syntactic generator for CALL purposes

ReCALL ◽  
2000 ◽  
Vol 12 (1) ◽  
pp. 79-91 ◽  
Author(s):  
ANNE VANDEVENTER ◽  
MARIE-JOSÉE HAMEL
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Deep Structure ◽  
Semantic Representation ◽  
Generation Process ◽  
Generation System ◽  
Sentence Generation ◽  
Advantages And Disadvantages ◽  
Grammatical Sentence

This article presents briefly the advantages and disadvantages of reusing natural language processing (NLP) tools in the CALL context. The issue is addressed through the description of GBGen, a sentence generation system. The abstract semantic representation used as input for the generator is described, as well as the actual generation process, from a deep structure to a grammatical sentence through transformations and the application of morphology. The possible didactic value of such a tool is then evaluated and the outline of a CALL scenario given. Finally, proposed adaptations of the generator for the CALL context are discussed.

scholarly journals State of Art for Semantic Analysis of Natural Language Processing

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Dastan Hussen Maulud ◽  
Subhi R. M. Zeebaree ◽  
Karwan Jacksi ◽  
Mohammed A. Mohammed Sadeeq ◽  
Karzan Hussein Sharif
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Prediction Error ◽  
Semantic Analysis ◽  
Essential Feature ◽  
Semantic Interpretation ◽  
The Subject ◽  
State Of Art

Semantic analysis is an essential feature of the NLP approach. It indicates, in the appropriate format, the context of a sentence or paragraph. Semantics is about language significance study. The vocabulary used conveys the importance of the subject because of the interrelationship between linguistic classes. In this article, semantic interpretation is carried out in the area of Natural Language Processing. The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.

State of Art for Semantic Analysis of Natural Language Processing

2021 ◽  
Vol 1 (2) ◽  
pp. 21-28
Author(s):  
Dastan Hussen Maulud ◽  
Subhi R. M. Zeebaree ◽  
Karwan Jacksi ◽  
Mohammed Mohammed Sadeeq ◽  
Karzan Hussein Sharif
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Prediction Error ◽  
Semantic Analysis ◽  
Essential Feature ◽  
Semantic Interpretation ◽  
The Subject ◽  
State Of Art

Semantic analysis is an essential feature of the NLP approach. It indicates, in the appropriate format, the context of a sentence or paragraph. Semantics is about language significance study. The vocabulary used conveys the importance of the subject because of the interrelationship between linguistic classes. In this article, semantic interpretation is carried out in the area of Natural Language Processing. The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.

The Applications of Description Logics in Natural Language Processing

2011 ◽  
Vol 204-210 ◽  
pp. 381-386 ◽  
Author(s):  
Xian Yi Cheng ◽  
Chen Cheng ◽  
Qian Zhu
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Description Logic ◽  
Description Logics ◽  
Semantic Interpretation ◽  
Natural Language Semantic ◽  
Ontology Language ◽  
Basic Ideas

As a sort of formalizing tool of knowledge representation, Description Logics have been successfully applied in Information System, Software Engineering and Natural Language processing and so on. Description Logics also play a key role in text representation, Natural Language semantic interpretation and language ontology description. Description Logics have been logical basis of OWL which is an ontology language that is recommended by W3C. This paper discusses the description logic basic ideas under vocabulary semantic, context meaning, domain knowledge and background knowledge.

NLP and the Representation of Data on the Semantic Web

2021 ◽  
pp. 393-426
Author(s):  
Jose L. Martinez-Rodriguez ◽  
Ivan Lopez-Arevalo ◽  
Jaime I. Lopez-Veyna ◽  
Ana B. Rios-Alvarado ◽  
Edwin Aldana-Bobadilla
Keyword(s):  
Natural Language Processing ◽  
Semantic Web ◽  
Natural Language ◽  
Language Processing ◽  
Resource Description Framework ◽  
Basic Unit ◽  
Starting Point ◽  
Description Framework ◽  
Resource Description

One of the goals of data scientists and curators is to get information (contained in text) organized and integrated in a way that can be easily consumed by people and machines. A starting point for such a goal is to get a model to represent the information. This model should ease to obtain knowledge semantically (e.g., using reasoners and inferencing rules). In this sense, the Semantic Web is focused on representing the information through the Resource Description Framework (RDF) model, in which the triple (subject, predicate, object) is the basic unit of information. In this context, the natural language processing (NLP) field has been a cornerstone in the identification of elements that can be represented by triples of the Semantic Web. However, existing approaches for the representation of RDF triples from texts use diverse techniques and tasks for such purpose, which complicate the understanding of the process by non-expert users. This chapter aims to discuss the main concepts involved in the representation of the information through the Semantic Web and the NLP fields.

Application of Domain Ontologies to Natural Language Processing

2015 ◽  
Vol 5 (3) ◽  
pp. 19-38 ◽  
Author(s):  
María Herrero-Zazo ◽  
Isabel Segura-Bedmar ◽  
Janna Hastings ◽  
Paloma Martínez
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Semantic Representation ◽  
Relation Extraction ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Knowledge Domain ◽  
Domain Ontologies

Natural Language Processing (NLP) techniques can provide an interesting way to mine the growing biomedical literature, and a promising approach for new knowledge discovery. However, the major bottleneck in this area is that these systems rely on specific resources providing the domain knowledge. Domain ontologies provide a contextual framework and a semantic representation of the domain, and they can contribute to a better performance of current NLP systems. However, their contribution to information extraction has not been well studied yet. The aim of this paper is to provide insights into the potential role that domain ontologies can play in NLP. To do this, the authors apply the drug-drug interactions ontology (DINTO) to named entity recognition and relation extraction from pharmacological texts. The authors use the DDI corpus, a gold-standard for the development and evaluation of IE systems in this domain, and evaluate their results in the framework of the last SemEval-2013 DDI Extraction task.

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