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Cross-document coreference occurs when the same person, place, event, or concept is discussed in more than one text source. Computer recognition of this phenomenon is important because it helps break …...

Cross-document coreference occurs when the same person, place, event, or concept is discussed in more than one text source. Computer recognition of this phenomenon is important because it helps break …...

Covering ambiguity is one of the two basic types of ambiguities in Chinese word segmentation. We regard its resolution as equivalent to word sense disambiguation, and make use of the …...

We propose a detection method for or- thographic variants caused by translit- eration in a large corpus. The method employs two similarities. One is string similarity based on edit distance. …...

This paper presents a novel algorithm for the acquisition of Information Extraction patterns. The approach makes the assump- tion that useful patterns will have simi- lar meanings to those already …...

New Event Detection (NED) involves monitoring chronologically-ordered news streams to automatically detect the stories that report on new events. We compare two stories by finding three cosine simi- larities based …...

. Based on large-scale bilingual corpora and the theories of vector space model and lexical mutual information, this paper explores the application of the traditional monolingual IR technology to converting …...

Psychiatric document retrieval attempts to help people to efficiently and effectively locate the consultation documents relevant to their depressive problems. Individuals can understand how to alleviate their symp- toms according …...

We explore the performance of the Vector Space Model (VSM) in finding verb synonyms in Por- tuguese by analyzing the impact of three oper- ating parameters: (i) the weighting function, …...

This paper proposes new algorithms to com- pute the sense similarity between two units (words, phrases, rules, etc.) from parallel cor- pora. The sense similarity scores are computed by using …...

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. …...

Noticing that different information sources of- ten provide complementary coverage of word sense and meaning, we propose a simple and yet effective strategy for measuring lexical se- mantics. Our model …...

Unsupervised Relation Extraction (URE) is the task of extracting relations of a priori unknown semantic types using clustering methods on a vector space model of entity pairs and patterns. In …...

We present a novel vector space model for se- mantic co-compositionality. Inspired by Gen- erative Lexicon Theory (Pustejovsky, 1995), our goal is a compositional model where both predicate and argument …...

This paper proposes a new approach to domain adaptation in statistical machine translation (SMT) based on a vector space model (VSM). The general idea is first to create a vector …...

AbstractIn this paper, we propose two extensions to the vector space model (VSM) adaptation technique (Chen et al., 2013b) for statistical machine translation (SMT), both of which result in significant …...

. In the course of the last two decades, numerous algorithms have sprouted up that successfully capture the semantics of single words by looking at their distribution in text, and …...

Most existing systems for subcategoriza- tion frame (SCF) acquisition rely on su- pervised parsing and infer SCF distribu- tions at type, rather than instance level. These systems suffer from poor …...

. Vector space models implement the distributional hypothesis relying on the repetition of information oc- curring in the contexts of words to associate. However, these models suffer from a high …...

A central challenge in semantic parsing is handling the myriad ways in which knowl- edge base predicates can be expressed. Traditionally, semantic parsers are trained primarily from text paired with …...

This work describes the participation of the University of Texas Health Science Center at Houston (UTHealth) team on the SemEval 2014 – Task 7 analysis of clinical text challenge. The …...

This paper describes the system developed by the University of Texas Health Science Center at Houston (UTHealth), for the 2015 SemEval shared task on “Analysis of Clinical Text” (Task 14). …...

Distributional semantics creates vector- space representations that capture many forms of semantic similarity, but their re- lation to semantic entailment has been less clear. We propose a vector-space model which …...

AbstractThe paper describes the participation of the team “TwiSE” in the SemEval-2017 challenge. Specifically, I participated at Task 4 entitled “Sentiment Analysis in Twitter” for which I implemented systems for …...