1,720,965 research outputs found
Towards Compositional Tree Kernels
No full textDistributional Compositional Semantics (DCS) methods combine lexical vectors according to algebraic operators or functions to model the meaning of complex linguistic phrases. On the other hand, several textual inference tasks rely on supervised kernel-based learning, whereas Tree Kernels (TK) have been shown suitable to the modeling of syntactic and semantic similarity between linguistic instances. While the modeling of DCS for complex phrases is still an open research issue, TKs do not account for compositionality. In this paper, a novel kernel called Compositionally Smoothed Partial Tree Kernel is proposed integrating DCS operators into the TK estimation. Empirical results over Semantic Text Similarity and Question Classification tasks show the contribution of semantic compositions with respect to traditional TKs
UNITOR: Combining semantic text similarity functions through SV Regression
No full textThis paper presents the UNITOR system that participated to the SemEval 2012 Task 6: Semantic Textual Similarity (STS). The task is here modeled as a Support Vector (SV) regression problem, where a similarity scoring function between text pairs is acquired from examples. The semantic relatedness between sentences is modeled in an unsupervised fashion through different similarity functions, each capturing a specific semantic aspect of the STS, e.g. syntactic vs. lexical or topical vs. paradigmatic similarity. The SV regressor effectively combines the different models, learning a scoring function that weights individual scores in a unique resulting STS. It provides a highly portable method as it does not depend on any manually built resource (e.g. WordNet) nor controlled, e.g. aligned, corpus
Semantic compositionality in tree kernels
No full textKernel-based learning has been largely applied to semantic textual inference tasks. In particular, Tree Kernels (TKs) are crucial in the modeling of syntactic similarity between linguistic instances in Question Answering or Information Extraction tasks. At the same time, lexical semantic information has been studied through the adoption of the so-called Distributional Semantics (DS) paradigm, where lexical vectors are acquired automatically from large corpora. Notice how methods to account for compositional linguistic structures (e.g. grammatically typed bi-grams or complex verb or noun phrases) have been proposed recently by defining algebras on lexical vectors. The result is an extended paradigm called Distributional Compositional Semantics (DCS). Although lexical extensions have been already proposed to generalize TKs towards semantic phenomena (e.g. the predicate argument structures as for role labeling), currently studied TKs do not account for compositionality, in general. In this paper, a novel kernel called Compositionally Smoothed Partial Tree Kernel is proposed to integrate DCS operators into the tree kernel evaluation, by acting both over lexical leaves and non-terminal, i.e. complex compositional, nodes. The empirical results obtained on a Question Classification and Paraphrase Identification tasks show that state-of-the-art performances can be achieved, without resorting to manual feature engineering, thus suggesting that a large set of Web and text mining tasks can be handled successfully by the kernel proposed here
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
Space projections as distributional models for semantic composition
Full text linkEmpirical distributional methods account for the meaning of syntactic structures by combining word vectors according to algebraic operators. In this paper, a novel approach for semantic composition based on space projection techniques over lexical vector representations is proposed. In line with the principle of compositionality, the meaning of a phrase is modeled in terms of the subset of properties shared by co-occurring words. Syntactic bi-grams are thus projected in the so called Support Subspace, corresponding to such properties. State-of-the-art results are achieved in a well known phrase similarity task, used as a benchmark for this class of methods. © 2012 Springer-Verlag
A compositional perspective in convolution kernels
No full textKernel-based learning has been largely adopted in many semantic textual inference tasks. In particular, Tree Kernels (TKs) have been successfully applied in the modeling of syntactic similarity between linguistic instances in Question Answering or Information Extraction tasks. At the same time, lexical semantic information has been studied through the adoption of the so-called Distributional Semantics (DS) paradigm, where lexical vectors are acquired automatically from large-scale corpora. Recently, Compositional Semantics phenomena arising in complex linguistic structures have been studied in an extended paradigm called Distributional Compositional Semantics (DCS), where, for example, algebraic operators on lexical vectors have been defined to account for grammatically typed bi-grams or complex verb or noun phrases. In this paper, a novel kernel called Compositionally Smoothed Partial Tree Kernel is presented to integrate DCS operators into the tree kernel evaluation by also considering complex compositional nodes. Empirical results on well-known NLP tasks show that state-of-the-art performances can be achieved, without resorting to manual feature engineering, thus suggesting that a large set of Web and text mining tasks can be handled successfully by this kernel
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
Algebraic compositional models for semantic similarity in ranking and clustering
No full textAlthough distributional models of word meaning have been widely used in Information Retrieval achieving an effective representation and generalization schema of words in isolation, the composition of words in phrases or sentences is still a challenging task. Different methods have been proposed to account on syntactic structures to combine words in term of algebraic operators (e.g. tensor product) among vectors that represent lexical constituents. In this paper, a novel approach for semantic composition based on space projection techniques over the basic geometric lexical representations is proposed. In the geometric perspective here pursued, syntactic bi-grams are projected in the so called Support Subspace, aimed at emphasizing the semantic features shared by the compound words and better capturing phrase-specific aspects of the involved lexical meanings. State-of-the-art results are achieved in a well known benchmark for phrase similarity task and the generalization capability of the proposed operators is investigated in a cross-linguistic scenario, i.e. in the English and Italian Language
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