90 research outputs found

    Augmenting concept definition in gloss vector semantic relatedness measure using Wikipedia articles

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    Semantic relatedness measures are widely used in text mining and information retrieval applications. Considering these automated measures, in this research paper we attempt to improve Gloss Vector relatedness measure for more accurate estimation of relatedness between two given concepts. Generally, this measure, by constructing concepts definitions (Glosses) from a thesaurus, tries to find the angle between the concepts’ gloss vectors for the calculation of relatedness. Nonetheless, this definition construction task is challenging as thesauruses do not provide full coverage of expressive definitions for the particularly specialized concepts. By employing Wikipedia articles and other external resources, we aim at augmenting these concepts’ definitions. Applying both definition types to the biomedical domain, using MEDLINE as corpus, UMLS as the default thesaurus, and a reference standard of 68 concept pairs manually rated for relatedness, we show exploiting available resources on the Web would have positive impact on final measurement of semantic relatedness

    Automatically Acquiring A Semantic Network Of Related Concepts

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    We describe the automatic acquisition of a semantic network in which over 7,500 of the most frequently occurring nouns in the English language are linked to their semantically related concepts in the WordNet noun ontology. Relatedness between nouns is discovered automatically from lexical co-occurrence in Wikipedia texts using a novel adaptation of an information theoretic inspired measure. Our algorithm then capitalizes on salient sense clustering among these semantic associates to automatically disambiguate them to their corresponding WordNet noun senses (i.e., concepts). The resultant concept-to-concept associations, stemming from 7,593 target nouns, with 17,104 distinct senses among them, constitute a large-scale semantic network with 208,832 undirected edges between related concepts. Our work can thus be conceived of as augmenting the WordNet noun ontology with RelatedTo links. The network, which we refer to as the Szumlanski-Gomez Network (SGN), has been subjected to a variety of evaluative measures, including manual inspection by human judges and quantitative comparison to gold standard data for semantic relatedness measurements. We have also evaluated the network’s performance in an applied setting on a word sense disambiguation (WSD) task in which the network served as a knowledge source for established graph-based spreading activation algorithms, and have shown: a) the network is competitive with WordNet when used as a stand-alone knowledge source for WSD, b) combining our network with WordNet achieves disambiguation results that exceed the performance of either resource individually, and c) our network outperforms a similar resource, WordNet++ (Ponzetto & Navigli, 2010), that has been automatically derived from annotations in the Wikipedia corpus. iii Finally, we present a study on human perceptions of relatedness. In our study, we elicited quantitative evaluations of semantic relatedness from human subjects using a variation of the classical methodology that Rubenstein and Goodenough (1965) employed to investigate human perceptions of semantic similarity. Judgments from individual subjects in our study exhibit high average correlation to the elicited relatedness means using leave-one-out sampling (r = 0.77, σ = 0.09, N = 73), although not as high as average human correlation in previous studies of similarity judgments, for which Resnik (1995) established an upper bound of r = 0.90 (σ = 0.07, N = 10). These results suggest that human perceptions of relatedness are less strictly constrained than evaluations of similarity, and establish a clearer expectation for what constitutes human-like performance by a computational measure of semantic relatedness. We also contrast the performance of a variety of similarity and relatedness measures on our dataset to their performance on similarity norms and introduce our own dataset as a supplementary evaluative standard for relatedness measures

    Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods

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    Measuring the similarity of short written contexts is a fundamental problem in Natural Language Processing. This article provides a unifying framework by which short context problems can be categorized both by their intended application and proposed solution. The goal is to show that various problems and methodologies that appear quite different on the surface are in fact very closely related. The axes by which these categorizations are made include the format of the contexts (headed versus headless), the way in which the contexts are to be measured (first-order versus second-order similarity), and the information used to represent the features in the contexts (micro versus macro views). The unifying thread that binds together many short context applications and methods is the fact that similarity decisions must be made between contexts that share few (if any) words in common.Comment: 23 page

    Mining Meaning from Wikipedia

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    Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.Comment: An extensive survey of re-using information in Wikipedia in natural language processing, information retrieval and extraction and ontology building. Accepted for publication in International Journal of Human-Computer Studie

    Exploiting Wikipedia Semantics for Computing Word Associations

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    Semantic association computation is the process of automatically quantifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to computing semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the implicit semantic relations. In the past few years, semantic association computation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of humans' associations than the approaches based on other structured and unstructured knowledge sources. There are two key challenges to achieve this goal: first, to exploit various semantic association models based on different aspects of Wikipedia in developing new measures of semantic associations; and second, to evaluate these measures compared to human performance in a range of tasks. The focus of the thesis is on exploring two aspects of Wikipedia: as a formal knowledge source, and as an informal text corpus. The first contribution of the work included in the thesis is that it effectively exploited the knowledge source aspect of Wikipedia by developing new measures of semantic associations based on Wikipedia hyperlink structure, informative-content of articles and combinations of both elements. It was found that Wikipedia can be effectively used for computing noun-noun similarity. It was also found that a model based on hybrid combinations of Wikipedia structure and informative-content based features performs better than those based on individual features. It was also found that the structure based measures outperformed the informative content based measures on both semantic similarity and semantic relatedness computation tasks. The second contribution of the research work in the thesis is that it effectively exploited the corpus aspect of Wikipedia by developing a new measure of semantic association based on asymmetric word associations. The thesis introduced the concept of asymmetric associations based measure using the idea of directional context inspired by the free word association task. The underlying assumption was that the association strength can change with the changing context. It was found that the asymmetric association based measure performed better than the symmetric measures on semantic association computation, relatedness based word choice and causality detection tasks. However, asymmetric-associations based measures have no advantage for synonymy-based word choice tasks. It was also found that Wikipedia is not a good knowledge source for capturing verb-relations due to its focus on encyclopedic concepts specially nouns. It is hoped that future research will build on the experiments and discussions presented in this thesis to explore new avenues using Wikipedia for finding deeper and semantically more meaningful associations in a wide range of application areas based on humans' estimates of word associations

    Applying Wikipedia to Interactive Information Retrieval

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    There are many opportunities to improve the interactivity of information retrieval systems beyond the ubiquitous search box. One idea is to use knowledge bases—e.g. controlled vocabularies, classification schemes, thesauri and ontologies—to organize, describe and navigate the information space. These resources are popular in libraries and specialist collections, but have proven too expensive and narrow to be applied to everyday webscale search. Wikipedia has the potential to bring structured knowledge into more widespread use. This online, collaboratively generated encyclopaedia is one of the largest and most consulted reference works in existence. It is broader, deeper and more agile than the knowledge bases put forward to assist retrieval in the past. Rendering this resource machine-readable is a challenging task that has captured the interest of many researchers. Many see it as a key step required to break the knowledge acquisition bottleneck that crippled previous efforts. This thesis claims that the roadblock can be sidestepped: Wikipedia can be applied effectively to open-domain information retrieval with minimal natural language processing or information extraction. The key is to focus on gathering and applying human-readable rather than machine-readable knowledge. To demonstrate this claim, the thesis tackles three separate problems: extracting knowledge from Wikipedia; connecting it to textual documents; and applying it to the retrieval process. First, we demonstrate that a large thesaurus-like structure can be obtained directly from Wikipedia, and that accurate measures of semantic relatedness can be efficiently mined from it. Second, we show that Wikipedia provides the necessary features and training data for existing data mining techniques to accurately detect and disambiguate topics when they are mentioned in plain text. Third, we provide two systems and user studies that demonstrate the utility of the Wikipedia-derived knowledge base for interactive information retrieval

    Measuring associational thinking through word embeddings

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    [EN] The development of a model to quantify semantic similarity and relatedness between words has been the major focus of many studies in various fields, e.g. psychology, linguistics, and natural language processing. Unlike the measures proposed by most previous research, this article is aimed at estimating automatically the strength of associative words that can be semantically related or not. We demonstrate that the performance of the model depends not only on the combination of independently constructed word embeddings (namely, corpus- and network-based embeddings) but also on the way these word vectors interact. The research concludes that the weighted average of the cosine-similarity coefficients derived from independent word embeddings in a double vector space tends to yield high correlations with human judgements. Moreover, we demonstrate that evaluating word associations through a measure that relies on not only the rank ordering of word pairs but also the strength of associations can reveal some findings that go unnoticed by traditional measures such as Spearman's and Pearson's correlation coefficients.s Financial support for this research has been provided by the Spanish Ministry of Science, Innovation and Universities [grant number RTC 2017-6389-5], the Spanish ¿Agencia Estatal de Investigación¿ [grant number PID2020-112827GB-I00 / AEI / 10.13039/501100011033], and the European Union¿s Horizon 2020 research and innovation program [grant number 101017861: project SMARTLAGOON]. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Periñán-Pascual, C. (2022). Measuring associational thinking through word embeddings. Artificial Intelligence Review. 55(3):2065-2102. https://doi.org/10.1007/s10462-021-10056-62065210255
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