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54
Vector-based models of semantic composition
- In Proceedings of ACL-08: HLT
, 2008
"... This paper proposes a framework for representing the meaning of phrases and sentences in vector space. Central to our approach is vector composition which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models which ..."
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Cited by 220 (5 self)
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This paper proposes a framework for representing the meaning of phrases and sentences in vector space. Central to our approach is vector composition which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models which we evaluate empirically on a sentence similarity task. Experimental results demonstrate that the multiplicative models are superior to the additive alternatives when compared against human judgments.
Summarizing Scientific Articles - Experiments with Relevance and Rhetorical Status
- Computational Linguistics
, 2002
"... this paper we argue that scientific articles require a different summarization strategy than, for instance, news articles. We propose a strategy which concentrates on the rhetorical status of statements in the article: Material for summaries is selected in such a way that summaries can highlight the ..."
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Cited by 199 (3 self)
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this paper we argue that scientific articles require a different summarization strategy than, for instance, news articles. We propose a strategy which concentrates on the rhetorical status of statements in the article: Material for summaries is selected in such a way that summaries can highlight the new contribution of the source paper and situate it with respect to earlier work. We provide a gold standard for summaries of this kind consisting of a substantial corpus of conference articles in computational linguistics with human judgements of rhetorical status and relevance. We present several experiments measuring our judges' agreement on these annotations. We also present an algorithm which, on the basis of the annotated training material, selects content and classifies it into a fixed set of seven rhetorical categories. The output of this extraction and classification system can be viewed as a single-document summary in its own right; alternatively, it can be used to generate task-oriented and user-tailored summaries designed to give users an overview of a scientific field.
Using the web to obtain frequencies for unseen bigrams
- COMPUT. LINGUIST
, 2003
"... This paper shows that the web can be employed to obtain frequencies for bigrams that are unseen in a given corpus. We describe a method for retrieving counts for adjective-noun, noun-noun, and verb-object bigrams from the web by querying a search engine. We evaluate this method by demonstrating: (a) ..."
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Cited by 171 (2 self)
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This paper shows that the web can be employed to obtain frequencies for bigrams that are unseen in a given corpus. We describe a method for retrieving counts for adjective-noun, noun-noun, and verb-object bigrams from the web by querying a search engine. We evaluate this method by demonstrating: (a) a high correlation between web frequencies and corpus frequencies; (b) a reliable correlation between web frequencies and plausibility judgments; (c) a reliable correlation between web frequencies and frequencies recreated using class-based smoothing; (d) a good performance of web frequencies in a pseudo-disambiguation task.
Composition in distributional models of semantics
, 2010
"... Distributional models of semantics have proven themselves invaluable both in cog-nitive modelling of semantic phenomena and also in practical applications. For ex-ample, they have been used to model judgments of semantic similarity (McDonald, 2000) and association (Denhire and Lemaire, 2004; Griffit ..."
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Cited by 148 (3 self)
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Distributional models of semantics have proven themselves invaluable both in cog-nitive modelling of semantic phenomena and also in practical applications. For ex-ample, they have been used to model judgments of semantic similarity (McDonald, 2000) and association (Denhire and Lemaire, 2004; Griffiths et al., 2007) and have been shown to achieve human level performance on synonymy tests (Landuaer and Dumais, 1997; Griffiths et al., 2007) such as those included in the Test of English as Foreign Language (TOEFL). This ability has been put to practical use in automatic the-saurus extraction (Grefenstette, 1994). However, while there has been a considerable amount of research directed at the most effective ways of constructing representations for individual words, the representation of larger constructions, e.g., phrases and sen-tences, has received relatively little attention. In this thesis we examine this issue of how to compose meanings within distributional models of semantics to form represen-tations of multi-word structures. Natural language data typically consists of such complex structures, rather than
A Probabilistic Account of Logical Metonymy
, 2003
"... In this article we investigate logical metonymy, that is, constructions in which the argument of a word in syntax appears to be different from that argument in logical form (e.g., enjoy the book means enjoy reading the book, and easy problem means a problem that is easy to solve). The systematic var ..."
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Cited by 43 (4 self)
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In this article we investigate logical metonymy, that is, constructions in which the argument of a word in syntax appears to be different from that argument in logical form (e.g., enjoy the book means enjoy reading the book, and easy problem means a problem that is easy to solve). The systematic variation in the interpretation of such constructions suggests a rich and complex theory of composition on the syntax/semantics interface. Linguistic accounts of logical metonymy typically fail to describe exhaustively all the possible interpretations, or they don't rank those interpretations in terms of their likelihood. In view of this, we acquire the meanings of metonymic verbs and adjectives from a large corpus and propose a probabilistic model that provides a ranking on the set of possible interpretations. We identify the interpretations automatically by exploiting the consistent correspondences between surface syntactic cues and meaning. We evaluate our results against paraphrase judgments elicited experimentally from humans and show that the model's ranking of meanings correlates reliably with human intuitions.
Automatic Evaluation of Information Ordering: Kendall's Tau
- Computational Linguistics
, 2006
"... This article considers the automatic evaluation of information ordering, a task underlying many text-based applications such as concept-to-text generation and multidocument summarization. We propose an evaluation method based on Kendall’s τ, a metric of rank correlation. The method is inexpensive, r ..."
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Cited by 42 (0 self)
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This article considers the automatic evaluation of information ordering, a task underlying many text-based applications such as concept-to-text generation and multidocument summarization. We propose an evaluation method based on Kendall’s τ, a metric of rank correlation. The method is inexpensive, robust, and representation independent. We show that Kendall’s τ correlates reliably with human ratings and reading times. 1.
Summarising Scientific Articles - Experiments with Relevance and Rhetorical Status
- Computational Linguistics
"... Machine (COLING94), S.Tojo 28 9411023 Abstract Generation Based on Rhetorical Structure Extraction (COLING94), K.Ono et al. ..."
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Cited by 25 (5 self)
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Machine (COLING94), S.Tojo 28 9411023 Abstract Generation Based on Rhetorical Structure Extraction (COLING94), K.Ono et al.
Rules for Syntax, Vectors for Semantics
- In Proceedings of the Twenty-third Annual Conference of the Cognitive Science Society
, 2001
"... Latent Semantic Analysis (LSA) has been shown to perform many linguistic tasks as well as humans do, and has been put forward as a model of human linguistic competence. But LSA pays no attention to word order, much less sentence structure. Researchers in Natural Language Processing have made si ..."
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Cited by 24 (2 self)
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Latent Semantic Analysis (LSA) has been shown to perform many linguistic tasks as well as humans do, and has been put forward as a model of human linguistic competence. But LSA pays no attention to word order, much less sentence structure. Researchers in Natural Language Processing have made significant progress in quickly and accurately deriving the syntactic structure of texts. But there is little agreement on how best to represent meaning, and the representations are brittle and difficult to build. This paper evaluates a model of language understanding that combines information from rule-based syntactic processing with a vector-based semantic representation which is learned from a corpus. The model is evaluated as a cognitive model, and as a potential technique for natural language understanding. Motivations Latent Semantic Analysis (LSA) was originally developed for the task of information retrieval, selecting a text which matches a query from a large database (Deerw...
Verb Similarity on the Taxonomy of Wordnet
- In the 3rd International WordNet Conference (GWC-06), Jeju Island, Korea
, 2006
"... In this paper, we introduce two kinds of word similarity algorithms, SHE and RHE, to investigate the capability of WordNet in measuring verb similarity. In the absence of a standard verb set we have proposed two new verb similarity ..."
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Cited by 22 (4 self)
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In this paper, we introduce two kinds of word similarity algorithms, SHE and RHE, to investigate the capability of WordNet in measuring verb similarity. In the absence of a standard verb set we have proposed two new verb similarity
An Ontology-driven Similarity Algorithm
- Knowledge Media Institute, Walton
, 2004
"... Abstract. This paper presents our similarity algorithm between relations in a user query written in FOL ( first order logic) and ontological relations. Our similarity algorithm takes two graphs and produces a mapping between elements of the two graphs (i.e. graphs associated to the query, a subsecti ..."
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Cited by 17 (9 self)
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Abstract. This paper presents our similarity algorithm between relations in a user query written in FOL ( first order logic) and ontological relations. Our similarity algorithm takes two graphs and produces a mapping between elements of the two graphs (i.e. graphs associated to the query, a subsection of ontology relevant to the query). The algorithm assesses structural similarity and concept similarity. An evaluation of our algorithm using the KMi Planet ontology 1 is presented. We also carried out an experiment to test the human judgment about similarity using context and without context. Our similarity algorithm has been manly used in AQUA, our question answering, in the query reformulation process.
