This paper presents SUCRE, a new software tool for coreference resolution and its feature engineering. It is able to separately do noun, pronoun and full coreference resolution. SUCRE introduces a new approach to the ...
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Several statistical methods have already been proposed to detect and correct the real-word errors of a context. However, to the best of our knowledge, none of them has been applied on Persian language yet. In this pap...
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Syntax-based translation models should in principle be efficient with polynomially-sized search space, but in practice they are often embarassingly slow, partly due to the cost of language model integration. In this p...
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The proceedings contain 13 papers. The topics discussed include: using NLG and sensors to support personal narrative for children with complex communication needs;automatic generation of conversational utterances and ...
The proceedings contain 13 papers. The topics discussed include: using NLG and sensors to support personal narrative for children with complex communication needs;automatic generation of conversational utterances and narrative for augmentative and alternative communication: a prototype system;implications of pragmatic and cognitive theories on the design of utterance-based AAC systems;scanning methods and language modeling for binary switch typing;a platform for automated acoustic analysis for assistive technology;an approach for anonymous spelling for voter write-ins using speech interaction;and using reinforcement learning to create communication channel management strategies for diverse users.
This paper presents a new framework integrating different relevance feedback scenarios (pseudo relevance feedback and user relevance feedback in short- and long-term context) and different approaches (model- and examp...
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This paper proposes three methods for combining various probabilistic models for retrieving answers from community-based question answering (cQA) archives. We adopt four probabilistic models for these combinations, i....
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ISBN:
(纸本)9780769542638
This paper proposes three methods for combining various probabilistic models for retrieving answers from community-based question answering (cQA) archives. We adopt four probabilistic models for these combinations, i.e., (1) the language model measuring similarity between a query and a question stored in the cQA archive, (2) two translation models for measuring the similarity between a query and an answer stored in the cQA archive, and a background language model for smoothing. Then, we developed three parameter estimation methods. Two of them are mixture models of the language models. The remaining model exploits the difference between the models. We apply the proposed methods to a cQA archive and show that they significantly outperform a widely used language model and Okapi BM25. We also show that they achieve a better performance than the recently proposed cQA retrieval method.
This paper proposes a unified framework for zero anaphora resolution, which can be divided into three sub-tasks: zero anaphor detection, anaphoricity determination and antecedent identification. In particular, all the...
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This paper presents ongoing efforts on developing Word Sense Disambiguation (WSD) resources for the German language, using GermaNet as a basis. We bootstrap two WSD systems for German. (i) We enrich GermaNet with pred...
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ISBN:
(纸本)9783862230051
This paper presents ongoing efforts on developing Word Sense Disambiguation (WSD) resources for the German language, using GermaNet as a basis. We bootstrap two WSD systems for German. (i) We enrich GermaNet with predominant sense in- formation, following previous unsupervised methods to acquire predominant senses of words. The acquired predominant sense information is used as a type-based first sense heuristics for token-level WSD. (ii) As an alternative, we adapt a state-of-theart knowledge-based WSD system to the GermaNet lexical resource. We finally investigate the hypothesis of whether the two systems are complementary by combining their output within a voting architecture. The results show that we are able to bootstrap two robust baseline systems for word sense annotation of German words.
This paper addresses the issue of sentiment word identification given an opinionated sentence, which is very important in sentiment analysis tasks. The most common way to tackle this problem is to utilize a readily av...
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Named Entity Recognition and Classification (NERC) is a well-studied NLP task typically focused on coarse-grained named entity (NE) classes. NERC for more fine-grained semantic NE classes has not been systematically s...
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