1,721,089 research outputs found
String Processing and Information Retrieval: 18th International Symposium, SPIRE 2011, Pisa, Italy, October 17-21, 2011, Proceedings
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An Experimental Comparison of Term Representations for Term Management Applications
No full textA number of content management tasks, including term clustering, term categorization, and automated thesaurus generation, see natural language terms (e.g. words, noun phrases) as first-class objects, i.e. as objects endowed with an internal representation which makes them suitable for being explicitly manipulated by the corresponding algorithms. The information retrieval (IR) literature has traditionally used and extensional representation for terms according to which a term is represented by the `bag of documents` in which the term occurs. The computational linguistics (CL) literature has independently developed an alternative extensional representation for terms, according to which a term is represented by the `bag of terms` that co-occur with it in some documents. This paper aims agt discovering which of the two representations is most effective, i.e. brings about higher effectiveness once used in tasks that require terms to be explicitly represented and manipulated. In order to discover this we carry out experiments on a term categorization task, which allows us to compare the two different representations in closely controlled experimental conditions. We report the results of a large scale experimentation carried out by classifying under 42 different classes the terms extracted from a corpus of more than 60,000 documents. Our results show a substantial difference in effectiveness between the two representation styles; we give both an intuitive explanation and an information-theoretic justification for these different behaviours
Distributional Term Representations: An Experimental Comparison
No full textA number of content management tasks, including term categorization, term clustering, and automated thesaurus generation, view natural language terms (e.g. words, noun phrases) as first-class objects, i.e. as objects endowed with an internal representation which makes them suitable for explicit manipulation by the corresponding algorithms. The information retrieval (IR) literature has traditionally used an extensional (aka distributional) representation for terms according to which a term is represented by the ``bag of documents`` in which the term occurs. The computational linguistics (CL) literature has independently developed an alternative distributional representation for terms, according to which a term is represented by the ``bag of terms`` that co-occur with it in some document. This paper aims at discovering which of the two representations is most effective, i.e. brings about higher effectiveness once used in tasks that require terms to be explicitly represented and manipulated. We carry out experiments on (i) a term categorization task, and (ii) a term clustering task; this allows us to compare the two different representations in closely controlled experimental conditions. We report the results of experiments in which we categorize/cluster under 42 different classes the terms extracted from a corpus of more than 65,000 documents. Our results show a substantial difference in effectiveness between the two representation styles; we give both an intuitive explanation and an information-theoretic justification for these different behaviours
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Unravelling interlanguage facts via explainable machine learning
No full textNative language identification (NLI) is the task of training (via supervised machine learning) a classifier that guesses the native language of the author of a text. This task has been extensively researched in the last decade, and the performance of NLI systems has steadily improved over the years. We focus on a different facet of the NLI task, i.e. that of analysing the internals of an NLI classifier trained by an explainable machine learning (EML) algorithm, in order to obtain explanations of its classification decisions, with the ultimate goal of gaining insight into which linguistic phenomena 'give a speaker's native language away'. We use this perspective in order to tackle both NLI and a (much less researched) companion task, i.e. guessing whether a text has been written by a native or a non-native speaker. Using three datasets of different provenance (two datasets of English learners' essays and a dataset of social media posts), we investigate which kind of linguistic traits (lexical, morphological, syntactic, and statistical) are most effective for solving our two tasks, namely, are most indicative of a speaker's L1; our experiments indicate that the most discriminative features are the lexical ones, followed by the morphological, syntactic, and statistical features, in this order. We also present two case studies, one on Italian and one on Spanish learners of English, in which we analyse individual linguistic traits that the classifiers have singled out as most important for spotting these L1s; we show that the traits identified as most discriminative well align with our intuition, i.e. represent typical patterns of language misuse, underuse, or overuse, by speakers of the given L1. Overall, our study shows that the use of EML can be a valuable tool for the scholar who investigates interlanguage facts and language transfer
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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