A fine fuzzy spatial partitioning model for line objects based on computing with words and application in natural language spatial query

Guo, Jifa; Shao, Xiaodong

doi:10.3233/jifs-161616

Cited by 5 publications

(8 citation statements)

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“…Representation of the polarity in a natural language utterance, more precisely of its positivity, neutrality and negativity scores, has been a long-standing problem in NLP [1], the solving of which was attempted by various knowledge representation techniques including frames [2], conceptual dependency or semantic nets [3]. An extension of semantic nets was proposed under the name of fuzzy semantic nets [4][5][6] in order to include inexactitude and imprecision.…”

Section: Introductionmentioning

confidence: 99%

How Objective a Neutral Word Is? A Neutrosophic Approach for the Objectivity Degrees of Neutral Words

2017

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Abstract:In the latest studies concerning the sentiment polarity of words, the authors mostly consider the positive and negative constructions, without paying too much attention to the neutral words, which can have, in fact, significant sentiment degrees. More precisely, not all the neutral words have zero positivity or negativity scores, some of them having quite important nonzero scores for these polarities. At this moment, in the literature, a word is considered neutral if its positive and negative scores are equal, which implies two possibilities: (1) zero positive and negative scores; (2) nonzero, but equal positive and negative scores. It is obvious that these cases represent two different categories of neutral words that must be treated separately by a sentiment analysis task. In this paper, we present a comprehensive study about the neutral words applied to English as is developed with the aid of SentiWordNet 3.0: the publicly available lexical resource for opinion mining. We designed our study in order to provide an accurate classification of the so-called "neutral words" described in terms of sentiment scores and using measures from neutrosophy theory. The intended scope is to fill the gap concerning the neutrality aspect by giving precise measurements for the words' objectivity.

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Section: Introductionmentioning

confidence: 99%

How Objective a Neutral Word Is? A Neutrosophic Approach for the Objectivity Degrees of Neutral Words

2017

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“…The IT2 FS is better than the T2 FS in complexity and computation, so it is adopted in this study. Many authors refer to interval type-2 fuzzy sets as type-2 fuzzy sets and add the qualifying term 'generalized' only when discussing non-interval type-2 fuzzy sets [33,34]. Fuzzifying the spatial data that contain certain types of errors is necessary and can achieve error containment.…”

Section: It2 Fsmentioning

confidence: 99%

“…As a result, it is useful to determine which primary school is a medium distance from (not too near) the upstream section of the Haihe River, and the results can provide advice for parents' guidance to choose primary schools. In Reference [33], we divided the river into upstream, midstream and downstream sections by using the fuzzy partition method as shown in Figure 9. In Reference [33], we divided the river into upstream, midstream and downstream sections by using the fuzzy partition method as shown in Figure 9.…”

Section: Practical Applicationmentioning

confidence: 99%

“…In Reference [33], we divided the river into upstream, midstream and downstream sections by using the fuzzy partition method as shown in Figure 9. In Reference [33], we divided the river into upstream, midstream and downstream sections by using the fuzzy partition method as shown in Figure 9. In Section 4.1, the IT2 FS corresponding to "medium" distance by cycle is = [(0.74,2.0,2.2,3.07), (1.26,2.0,2.2,2.83) .…”

Section: Practical Applicationmentioning

confidence: 99%

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Modeling Words for Qualitative Distance Based on Interval Type-2 Fuzzy Sets

Guo

2018

IJGI

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Modeling qualitative distance words is important for natural language understanding, scene reconstruction and many decision support systems (DSSs) based on a geographic information system (GIS). However, it is difficult to establish the relationship between qualitative distance words and quantitative distance for special applications since the meanings of these words are influenced by both subjective and objective factors. Some existing methods are reviewed, and the Hao–Mendel approach (HMA) is improved to model qualitative distance words for four travel modes by using interval type-2 fuzzy sets (IT2 FSs), aiming at addressing the individual and interpersonal uncertainty among qualitative distance words. The area of the footprint of uncertainty (FOU), fuzziness (entropy), and variance are adopted to measure the uncertainties of qualitative distance words. The experimental results show that the improved HMA algorithm is better than the original HMA algorithm and can be used in spatial information retrieval and GIS-based DSSs.

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“…Wang et al [17] and Wang [18] developed fuzzy models to improve the representation, analysis, and query of non-spatial information in natural languages, such as low humidity and high elevation. Modeling fuzzy spatiotemporal objects [19][20][21] was studied to facilitate fuzzy spatial queries. Models for fuzzifying the 9-intersection model [22,23] and RCC [24,25] have been also proposed.…”

Section: Related Workmentioning

confidence: 99%

Interpreting the Fuzzy Semantics of Natural-Language Spatial Relation Terms with the Fuzzy Random Forest Algorithm

Wang

Feng

et al. 2018

IJGI

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Naïve 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.

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A fine fuzzy spatial partitioning model for line objects based on computing with words and application in natural language spatial query

Cited by 5 publications

References 20 publications

How Objective a Neutral Word Is? A Neutrosophic Approach for the Objectivity Degrees of Neutral Words

How Objective a Neutral Word Is? A Neutrosophic Approach for the Objectivity Degrees of Neutral Words

Modeling Words for Qualitative Distance Based on Interval Type-2 Fuzzy Sets

Interpreting the Fuzzy Semantics of Natural-Language Spatial Relation Terms with the Fuzzy Random Forest Algorithm

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