The types of semantic information that are automatically retrieved from the mental lexicon on hearing a word were investigated in 3 semantic priming experiments. The authors probed for activation of information about a word's category membership by using prime-target pairs that were members of a common semantic category (e.g., pig-horse) and 2 types of functional semantic properties: instrument...

Relation extraction is the task of extracting semantic relations— such as synonymy or hypernymy—between word pairs from corpus data. Past work in relation extraction has concentrated on manually creating templates to use in directly extracting word pairs for a given semantic relation from corpus text. Recently, there has been a move towards using machine learning to automatically learn these pa...

This paper describes our study on identifying semantic relations that exist between diseases and treatments in biomedical sentences. We focus on three semantic relations: Cure, Prevent, and Side Effect. The contributions of this paper consists in the fact that better results are obtained compared to previous studies and the fact that our research settings allow the integration of biomedical and...

Open Relation Extraction (ORE) overcomes the limitations of traditional IE techniques, which train individual extractors for every single relation type. Systems such as ReVerb, PATTY, OLLIE, and Exemplar have attracted much attention on English ORE. However, few studies have been reported on ORE for languages beyond English. This paper presents a syntax-based Chinese (Zh) ORE system, ZORE, for ...

Explicit continuous vector representation such as vector representation of words, phrases, etc. has been proven effective for various NLP tasks. This paper proposes a novel method of constructing such vector representation for both entity-pairs and relation expressions which link them in text. Based on the insight of the duality of relations, the representation is constructed by embedding of tw...

Semantic relations between text concepts denote the core elements of lexical semantics. This paper presents a model for the automatic detection of INTENTION semantic relation. Our approach first identifies the syntactic patterns that encode intentions, then we select syntactic and semantic features for a SVM learning classifier. In conclusion, we discuss the application of INTENTION relations t...

Semantic dictionaries are widely used in natural language processing studies. Broad range of methods have been proposed to construct semantic dictionaries until this time. In this study, a brief summary for semantic relation pair extraction is introduced. An overview of selected approaches is given and results are compared to each other for hyponymy, holonymy, and antonymy relations.

This paper describes methods using Natural Language Processing approaches to extract and validate induced syntactic relations (here restricted to the Verb-Object relation). These methods use a syntactic parser and a semantic closeness measure to extract such relations. Then, their validation is based on two different techniques: A Web Validation system on one part, then a Semantic-Vectorbased a...

How do we formally specify the relation between a base class and a derived class? This question has two parts, a syntactic one, and a semantic one. The syntactic part is of course the easier of the two and the answer to that part is the standard contra/covariance requirement on the arguments and result of any base class method redefined in the derived class. Our concern in the current paper is ...

First, Similarity Of Fuzzy Ontology Generation For Semantic Web, Fuzzy Formal Concept Analysis incorporates fuzzy logic into Formal Concept Analysis (FCA) to form a fuzzy concept lattice. The fuzzy ontology is an extension of the domain ontology for solving the uncertainty problems. Fuzzy Conceptual Clustering then constructs the concept hierarchy from the fuzzy concept lattice. Finally, Fuzzy ...