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Re-ranking for Information Retrieval aims to elevate relevant feedbacks and depress negative ones in initial retrieval result list. Compared to relevance feedback-based re-ranking...
Yu Hong, Qing-qing Cai, Song Hua, Jian-Min Yao, Qi...
A large body of prior research on coreference resolution recasts the problem as a two-class classification problem. However, standard supervised machine learning algorithms that m...
This paper proposes an approach to improve graph-based dependency parsing by using decision history. We introduce a mechanism that considers short dependencies computed in the ear...
In this paper, we propose an unsupervised approach for identifying bipolar person names in a set of topic documents. We employ principal component analysis (PCA) to discover bipol...
In this paper, we consider sentence simplification as a special form of translation with the complex sentence as the source and the simple sentence as the target. We propose a Tre...
Text summarization solves the problem of extracting important information from huge amount of text data. There are various methods in the literature that aim to find out well-form...
The interpretation of a multiple-domain text corpus as a single ontology leads to misconceptions. This is because some concepts may be syntactically equal; though, they are semant...
We study correlation of rankings of text summarization systems using evaluation methods with and without human models. We apply our comparison framework to various well-establishe...
Horacio Saggion, Juan Manuel Torres Moreno, Iria d...