fuzzy set natural language
3361773
Interpreting the Fuzzy Semantics of Natural Language Spatial Relation Terms with the Fuzzy Random Forest Algorithm
Naive Geography, intelligent geographical information systems (GIS) and spatial data mining especially from social media all rely on natural language spatial relations (NLSR) terms to incorporate commonsense spatial knowledge into conventional GIS and to enhance the semantic interoperability of spatial information in social media data. Yet, the inherent fuzziness of NLSR terms makes them challenging to interpret. This study proposes to interpret the fuzzy semantics of NLSR terms using the fuzzy random forest (FRF) algorithm. Based on a large number of fuzzy samples acquired by transforming a set of crisp samples with the random forest algorithm, two FRF models with different membership assembling strategies are trained to obtain the fuzzy interpretation of three line region geometric representations using 69 NLSR terms. Experimental results demonstrate that the two FRF models achieve good accuracy in interpreting line region geometric representations using fuzzy NLSR terms. In addition, fuzzy classification of FRF can interpret the fuzzy semantics of NLSR terms more fully than their crisp counterparts.
ISPRS Int. J. Geo Inf.
Xiaonan Wang Shihong Du Chen-Chieh Feng Xueying Zhang Xiuyuan Zhang
1504259
Clarification on linguistic applications of Fuzzy Set Theory to natural language analysis
Within the linguistic field, the phenomenon found by Fuzzy Set Theory application is called "fuzziness" Linguistic study on fuzziness has lagged behind the multitudinous literature on vagueness and ambiguity. However, as an unconventional form of semantics and the entity of imprecision and rationality, the study of fuzziness remains a valuable area to explore. As fuzziness is a phenomenon existing in real world, as well as in natural language, the fuzzy set theory lighted up by linguistic method can serve as a tool to study language in turn. The close kinship created by linguistics and the birth of fuzzy sets in turn help to fetch the essence of fuzziness and resume the study of linguistics along the same line. Since there hasn't been generally agreed approach to Fuzzy Linguistics up till now, the linguistic application of Fuzzy Theory to natural language is clarified in this paper, and a working definition of linguistic fuzziness is provided.
2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)
Li Ma
85509510
Set Theory and Fuzzy Sets Their Relationship to Natural Language An Interview with
Background: A recurring argument of scholars who advocate set theory and fuzzy sets for social science is that this framework is valuable and appropriate in part because it reflects the structure of meaning in natural language.2 George Lakoff has written extensively on these topics and is cited by these scholars as an authority. In this interview, Lakoff synthesizes a large body of research in linguistics and cognitive science, which contends that natural language is not set theoretic in structure. He also explores Lotfi Zadeh's fuzzy logic, emphasizing both its creative applications in engineering and its poor fit with most features of natural language. Finally, Lakoff discusses the basic contrast between Zadeh's fuzzy logic and Charles Ragin's fuzzy set scoring. Lakoff emphasizes that he is not in a position to judge the substantive contribution of Ragin's method. However, it does not rely on an empirically adequate account of natural language; and because the scoring is based on fixed numerical values, rather than fuzzy distributions, Ragin's scoring does not qualify as a fuzzy method.
George Lakoff
1033018
Using Natural Language Processing techniques and fuzzy semantic similarity for automatic external plagiarism detection
Plagiarism is one of the most serious crimes in academia and research fields. In this modern era, where access to information has become much easier, the act of plagiarism is rapidly increasing. This paper aligns on external plagiarism detection method, where the source collection of documents is available against which the suspicious documents are compared. Primary focus is to detect intelligent plagiarism cases where semantics and linguistic variations play an important role. The paper explores the different preprocessing methods based on Natural Language Processing (NLP) techniques. It further explores fuzzy semantic similarity measures for document comparisons. The system is finally evaluated using PAN 20121 data set and performances of different methods are compared.
2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Deepa Gupta K Vani Charan Kamal Singh
212527415
A New Method for Semantic Similarity Assessment using Fuzzy Formal Concept Analysis Fuzzy Set Similarity Measure
Measuring the accurate semantic similarity between the words is a major issue in various applications of artificial intelligence and computational linguistics areas such as natural language processing, text mining, information retrieval and for development of semantic web. In the past, many approaches have been proposed and adopted to evaluate similarity by using the knowledge based systems such as WordNet and MeSH ontology. In this paper we have proposed a new method; based on hybridization approach in knowledge based system. In this we have used feature based method and fuzzy Set theory. In feature based approach, properties or features are used for measuring the similarity as compare to edge and content information approaches. Our approach is investigated on standard dataset like R&G, M&C and 353 TC, which shows prominent improvement in the judgment of semantic similarity score between the words. This approach can be further used among cross ontology and fuzzy ontology as it is based on the feature based measure and fuzzy set theory.
Shivani Jain Rajni Jindal
19909336
Grounding spatial relations in natural language by fuzzy representation for human robot interaction
This paper addresses the issue of grounding spatial relations in natural language for human robot interaction and robot control. The problem is approached by identifying two set of spatial relations, the image space based and object centered, and expressing them as fuzzy sets to capture the ambiguity inherent to the linguistic expressions for the relations. The sizes and shades of the scene objects have also been modeled as fuzzy sets for conditioning the spatial relations. To verify the validity of our approach and test its feasibility in a natural language based interface, we have considered the typical scenarios of using the spatial relations in simple declarative and imperative sentences and designed simple grammars for parsing such sentences. Our experiment has shown that fuzzy spatial relation analysis provides a useful way for modeling the ambiguity or imprecision of the natural language in describing spatial relations and that it is possible to use the spatial relation models to support robot control and human robot interaction in a natural language based interface.
2014 IEEE International Conference on Fuzzy Systems (FUZZ IEEE)
Jiacheng Tan Zhaojie Ju Honghai Liu
125144917
Fuzzy Set Theory
Fuzzy sets as defined by Zadeh (Inf. Con. 8(3):338 353, 1965 [1] provide a useful, effective and operative tool for handling imprecise data. It not only provides a powerful representation of measurement uncertainty, but also gives a meaningful representation of vague concepts expressed in natural language. The purpose of this chapter is to review the basic definitions and results on fuzzy sets, fuzzy numbers, fuzzy arithmetic and fuzzy ranking methods.
Ali Ebrahimnejad Jose L Verdegay
46939556
Fuzzy Set Approach to Study Appositives and Its Impact Due to Positional Alterations
Abstract Computing with Words (CWW) and Possibilistic Relational Universal Fuzzy (PRUF) are the two concepts which widely represent and measure the vaguely defined natural phenomenon. In this paper, we study the positional alteration of the phrases by which the impact of a natural language proposition gets affected and/or modified. We observe the gradations due to sensitivity/feeling of a statement towards the positional alterations. We derive the classification and modification of the meaning of words due to the positional alteration. We present the results with reference to set theoretic interpretations.
E Mike Dison T Pathinathan
61383258
Natural Languauge Fuzzy Set Based CBIR
Not much is understood in terms of semantics of natural language applicable in CBIR. There are many linguistic systems, often based on set theory and logic, attempting to grasp (at least some phenomena and circumstances) of the natural language based on which a image might understood or interpreted and thereby be annotated with and it must useful in such way that it reduces the cost of accessing the relevant information and remains refresh all together with open ended structure to incorporate the validity of ground truths. In this research work we have tried to come all these issues and results shows that our method used in building natural language sets for images in question is highly relevant thus enriching results to provide high recall and precision value of range of 67% IndexTerms:fuzzydataset,linguistic,raw symbolism,semantic. I. NTRODUCTION As there is movement towards building systems that works and behave like humans ,this paper explores the possibility of developing a content based system that takes input from human subjects specific to their domain knowledge (4) which is expressible in local dialects therefore, stands to reduce the man machine gap while it is in use However the problem remains ,How to reason and compute with information described in natural language and associate it with each image's content which is of course numerical in nature. The basic importance of this problem derives from the fact that much of human knowledge and especially world knowledge is expressed in natural language, there a incremental progress in the field of CBIR can only be done if the system incorporates these natural language terms phrases and sentences and associate with image in such a manner that it is correct to ground truth depicted in the image and user of the system gets a real useful information Since an image content is contained by a multitude of concrete and abstract objects, which may be seen in terms of recurring, constant conjunction of properties based on which the object segmentation algorithm works. In linguistic terms such conjunctions are expressed as nouns, non recurring conjunctions are symbolised as phrases or clauses. Such objects come into being, fade out of existence or, if already exist, can change their properties either as a result of a spontaneous process, chance or in accordance with a purpose i.e. as per specifications due to interpretation of objects by human subjects depicted in image content Here the major question is how such an temporal and inferential structure of the image objects are which are inferred from highly subject i nference of the use and how it can be created by converting aspects of natural language not just into theraw symbolism' (3) but into a formal language which is capable of being expressed asannotated Information with content of the image thus carrying the adjectives, adverbs and verbs that can be written as fuzzy sets .Therefore the natural language fuzzy sets, based on this logical inference The problem formulation took its shape which is explained in the next section
Gurdeep Kaur Gaganpreet Kaur Sushil Garg
60877323
An assessment of natural language robustness and how it relates to natural language sustainability: a fuzzy ecological approach to the lives of natural languages
All living natural languages may be characterized in terms of their overall 'robustness' The concept may be defined as relating directly to the health/vitality /wellbeing (i.e. sustainability) of these languages. In the paper, an attempt is made to further define the concept in terms of a set of parameters as well as to show that natural languages differ as to the degree of their overall robustness.
Stanislaw Puppel