Распределительное сходство

Accurately representing synonymy using distribu- tional similarity requires large volumes of data to reliably represent infrequent words. However, the na¨ıve nearest-neighbour approach to compare con- text vectors extracted from large …...

Processing discourse connectives is im- portant for tasks such as discourse parsing and generation. For these tasks, it is use- ful to know which connectives can signal the same coherence …...

A query speller is crucial to search en- gine in improving web search relevance. This paper describes novel methods for use of distributional similarity estimated from query logs in learning …...

Word clustering is a conventional and im- portant NLP task, and the literature has suggested two kinds of approaches to this problem. One is based on the distribu- tional similarity …...

Topic segmentation was addressed by a large amount of work from which it is not easy to draw conclusions, especially about the need for knowl- edge. In this article, we …...

Main approaches to corpus-based seman- tic class mining include distributional similarity (DS) and pattern-based (PB). In this paper, we perform an empirical comparison of them, based on a publicly available …...

A variety of information extraction techniques rely on the fact that instances of the same relation are “distributionally similar,” in that they tend to appear in similar textual con- texts. …...

An A-C bilingual dictionary can be inferred by merging A-B and B-C dictionaries using B as pivot. However, polysemous pivot words often produce wrong translation candidates. This paper analyzes two …...

This paper develops a minimally supervised approach, based on focused distributional sim- ilarity methods and discourse connectives, for identifying of causality relations between events in context. While it has been …...

AbstractRetrieving research papers and patents is important for any researcher assessing the scope of a field with high industrial relevance. However, the terms used in patents are often more abstract …...

AbstractWe present several experiments aiming at measuring the semantic compositionality of NV expressions in Basque. Our approach is based on the hypothesis that compositionality can be related to distributional similarity. …...

Understanding the connotation of words plays an important role in interpreting sub- tle shades of sentiment beyond denotative or surface meaning of text, as seemingly objective statements often allude nuanced …...

Semantic relation clustering for unsupervised information extraction Most studies in unsupervised information extraction concentrate on the relation extraction and few work has been proposed on the organization of the extracted …...

AbstractThe identification of various types of relations is a necessary step to allow computers to understand natural language text. In particular, the clarification of relations between predicates and their arguments …...

AbstractWe present a method for the extraction of synonyms for German particle verbs based on a word-aligned German-English parallel corpus: by translating the particle verb to a pivot, which is …...

In this paper, we address the role of syntactic parsing for distributional similarity. On the one hand, we are exploring distributional similarities as an extrinsic test bed for unsupervised parsers. …...

AbstractWe propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least …...

This work is concerned with distinguishing different semantic relations which exist between distributionally similar words. We compare a novel approach based on training a linear Support Vector Machine on pairs …...

Statistical Machine Translation (SMT) usually utilizes contextual information to disambiguate translation candidates. However, it is often limited to contexts within sentence boundaries, hence broader topical information cannot be leveraged. In …...

AbstractIn the work presented here we assess the degree of compositionality of German Particle Verbs with a Distributional Semantics Model which only relies on word window information and has no …...

Most traditional distributional similarity models fail to capture syntagmatic patterns that group together multiple word features within the same joint context. In this work we introduce a novel generic distributional …...

We compare the multinomial i-vector framework from the speech community with LDA, SAGE, and LSA as fea- ture learners for topic ID on multinomial speech and text data. We also …...

AbstractWe describe a method for identifying and performing functional analysis of structured regions that are embedded in natural language documents, such as tables or key-value lists. Such regions often encode …...

AbstractInference rule discovery aims to identify entailment relations between predicates, e.g., ‘X acquire Y –> X purchase Y’ and ‘X is author of Y –> X write Y’. Traditional methods …...

AbstractThe current state of the art for First Story Detection (FSD) are nearest neighbour-based models with traditional term vector representations; however, one challenge faced by FSD models is that the …...

AbstractPredicting the degree of compositionality of noun compounds such as “snowball” and “butterfly” is a crucial ingredient for lexicography and Natural Language Processing applications, to know whether the compound should …...