We investigate methods of generating additional bilingual phrase pairs for a phrase-based decoder by translating short sequences of source text. Because our translation task is more constrained, we can use a model tha...
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This paper addresses implicit opinions expressed via inference over explicit sentiments and events that positively/negatively affect entities (goodFor/badFor, gfbf events). We incorporate the inferences developed by i...
ISBN:
(纸本)9781941643266
This paper addresses implicit opinions expressed via inference over explicit sentiments and events that positively/negatively affect entities (goodFor/badFor, gfbf events). We incorporate the inferences developed by implicature rules into an optimization framework, to jointly improve sentiment detection toward entities and disambiguate components of gfbf events. The framework simultaneously beats the baselines by more than 10 points in F-measure on sentiment detection and more than 7 points in accuracy on gfbf polarity disambiguation.
For many NLP applications that require a parser, the sentences of interest may not be well-formed. If the parser can overlook problems such as grammar mistakes and produce a parse tree that closely resembles the corre...
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Opinion inference arises when opinions are expressed toward states and events which positive or negatively affect entities, i.e., benefactive and malefactive events. This paper addresses creating a lexicon of such eve...
Opinions may be expressed implicitly via inference over explicit sentiments and events that positively/negatively affect entities (goodFor/badFor events). We investigate how such inferences may be exploited to improve...
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There is a scarcity of multilingual vision-language models that properly account for the perceptual differences that are reflected in image captions across languages and cultures. In this work, through a multimodal, m...
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While previous sentiment analysis research has concentrated on the interpretation of explicitly stated opinions and attitudes, this work addresses a type of opinion implicature (i.e., opinion-oriented default inferenc...
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In this paper, we propose a new framework for constructing text metrics which can be used to compare and support inferences among terms and sets of terms. Our metric is derived from data-driven kernels on graphs that ...
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ISBN:
(纸本)9781577355120
In this paper, we propose a new framework for constructing text metrics which can be used to compare and support inferences among terms and sets of terms. Our metric is derived from data-driven kernels on graphs that let us capture global relations among terms and sets of terms, regardless of their complexity and size. To compute the metric efficiently for any two subsets of terms, we develop an approximation technique that relies on the precompiled term-term similarities. To scale-up the approach to problems with huge number of terms, we develop and experiment with a solution that subsamples the term space. We demonstrate the benefits of the whole framework on two text inference tasks: prediction of terms in the article from its abstract and query expansion in information retrieval.
Implicit opinions are commonly seen in opinion-oriented documents, such as political editorials. Previous work have utilized opinion inference rules to detect implicit opinions evoked by events that positively/negativ...
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We develop an algorithm for merging plans that are represented in a richly expressive language. Specifically, we are concerned with plans that have (i) quantitative temporal constraints, (ii) actions that are not inst...
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