Commonsense Reasoning in and Over Natural Language

Liu, Hugo; Singh, Push

doi:10.1007/978-3-540-30134-9_40

Cited by 71 publications

(27 citation statements)

References 9 publications

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“…The similarity calculation is done by using Divisi, an implementation of AnalogySpace, which is a way of representing ConceptNet's common-sense knowledge base in a multi-dimensional vector space. MontyLingua [6] is also a Python implemented tool that is used for natural language understanding. Given a sentence, it can extract verb/subject/object tuples, as well as other semantic information.…”

Section: Knowledge-based Semantic Similaritymentioning

confidence: 99%

Comparable Evaluation of Contemporary Corpus-Based and Knowledge-Based Semantic Similarity Measures of Short Texts

Furlan

Sivački

Jovanović

et al. 2011

JITA

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This paper presents methods for measuring the semantic similarity of texts, where we evaluated different approaches based on existing similarity measures. On one side word similarity was calculated by processing large text corpuses and on the other, commonsense knowledgebase was used. Given that a large fraction of the information available today, on the Web and elsewhere, consists of short text snippets (e.g. abstracts of scientifi c documents, image captions or product descriptions), where commonsense knowledge has an important role, in this paper we focus on computing the similarity between two sentences or two short paragraphs by extending existing measures with information from the ConceptNet knowledgebase. On the other hand, an extensive research has been done in the fi eld of corpus-based semantic similarity, so we also evaluated existing solutions by imposing some modifi cations. Through experiments performed on a paraphrase data set, we demonstrate that some of proposed approaches can improve the semantic similarity measurement of short text.

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Section: Knowledge-based Semantic Similaritymentioning

confidence: 99%

Comparable Evaluation of Contemporary Corpus-Based and Knowledge-Based Semantic Similarity Measures of Short Texts

Furlan

Sivački

Jovanović

et al. 2011

JITA

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“…Semantic Web and related approaches have contributed to a significant improvement in performance of search engines. However, for further progress it may be necessary to add to existing search engines knowledge-management systems such as the Web Ontology Language (OWL) [36], Cyc [40], WordNet [41], and ConceptNet [42].…”

Section: Prob(e) Ismentioning

confidence: 99%

Perception-based Data Mining and Decision Making in Economics and Finance

Batyrshin¹

2007

Studies in Computational Intelligence

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The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.Cover design: deblik, Berlin Typesetting by the editors and SPi using a Springer L A T E X macro package Printed on acid-free paper SPIN: 11793922 89/SPi 5 4 3 2 1 0 PrefaceThe primary goal of this book is to present to the scientific and management communities a selection of applications using more recent Soft Computing (SC) and Computing with Words and Perceptions (CWP) models and techniques meant to solve the economics and financial problems. The selected examples could also serve as a starting point or as an opening out, in the SC and CWP techniques application to a wider range of problems in economics and finance. Decision making in the present world is becoming more and more sophisticated, time consuming and difficult for human beings who require more and more computational support. This book addresses the significant increase on research and applications of Soft Computing and Computing with Words and Perceptions for decision making in Economics and Finance in recent years. Decision making is heavily based on information and knowledge usually extracted from the analysis of large amounts of data. Data mining techniques enabled with the capability to integrate human experience could be used for a more realistic business decision support. Computing with Words and Perceptions introduced by Lotfi Zadeh, can serve as a basis for such extension of traditional data mining and decision making systems. Fuzzy logic as a main constituent of CWP gives powerful tools for modeling and processing linguistic information defined on numerical domain.Decision making techniques based on fuzzy logics in many cases have demonstrated better performance than competing approaches. The reason is that traditional, bivalent-logic-based approaches, are not a good fit to reality -the reality of pervasive imprecision, uncertainty and partiality of truth. On the other hand, traditional probabilistic interpretation of uncertainties in practice does not always correspond to the nature of uncertainties that often appear as the effects of subjective estimations. The list of practical situations, when it seems better to avoid the traditional probabilistic interpretation of uncertainty is very long. The centrepiece of fuzzy logic that everything is, or is allowed to be, a matter of degree, makes it possible to better deal with perception-based information. Such information plays an essential role in economics, finance and, more generally in all domains in which human perceptions and emotions are in evidence. For instance, it is the case for the studies of the capital markets/financial engineering including financial time series modeling; price projections for stocks, volatility analysis and the pricing of options and derivatives; and risk management to mention few.The book consists o...

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“…To avoid incorrect inferences, our system disambiguates words robustly by utilizing two knowledge bases WordNet (Kipper et al 2006) and VerbNet (Liu and Singh 2004b). WordNet is an English lexical database that categorizes synonyms into groups, records word group relationships, and labels topic domains of some words; it can provide some word meanings for our system.…”

Section: Introductionmentioning

confidence: 99%

Memory reconsolidation for natural language processing

Cooper

Siegelmann

2009

Cogn Neurodyn

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We propose a model of memory reconsolidation that can output new sentences with additional meaning after refining information from input sentences and integrating them with related prior experience. Our model uses available technology to first disambiguate the meanings of words and extracts information from the sentences into a structure that is an extension to semantic networks. Within our long-term memory we introduce an action relationships database reminiscent of the way symbols are associated in brain, and propose an adaptive mechanism for linking these actions with the different scenarios. The model then fills in the implicit context of the input and predicts relevant activities that could occur in the context based on a statistical action relationship database. The new data both of the more complete scenario and of the statistical relationships of the activities are reconsolidated into memory. Experiments show that our model improves upon the existing reasoning tool suggested by MIT Media lab, known as ConceptNet.

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Commonsense Reasoning in and Over Natural Language

Cited by 71 publications

References 9 publications

Comparable Evaluation of Contemporary Corpus-Based and Knowledge-Based Semantic Similarity Measures of Short Texts

Comparable Evaluation of Contemporary Corpus-Based and Knowledge-Based Semantic Similarity Measures of Short Texts

Perception-based Data Mining and Decision Making in Economics and Finance

Memory reconsolidation for natural language processing

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