Standardization of the formal representation of lexical information for NLP

A New Method for Improving Computational Cost of Open Information Extraction Systems Using Log-Linear Model

Information extraction (IE) is a process of automatically providing a structured representation from an unstructured or semi-structured text. It is a long-standing challenge in natural language processing (NLP) which has been intensified by the increased volume of information and heterogeneity, and non-structured form of it. One of the core information extraction tasks is relation extraction wh...

Deep Semantics in an NLP Knowledge Base

In most natural language processing systems there is no representation of the semantic knowledge of lexical units, but just subcategorization frames, selection restrictions and links to other paradigmatically-related lexical units. Some NLP systems, e.g. machine translation or dialogue-based systems, attempt to “understand” the input text by translating it into some kind of formal language-inde...

The Effect of Semantic Transfer on Iranian EFL Learners’ Lexical Representation and Processing

Mental Representation of Cognates/Noncognates in Persian-Speaking EFL Learners

The purpose of this study was to investigate the mental representation of cognate and noncognate translation pairs in languages with different scripts to test the prediction of dual lexicon model (Gollan, Forster, & Frost, 1997). Two groups of Persian-speaking English language learners were tested on cognate and noncognate translation pairs in Persian-English and English-Persian directions with...

Conversion of Lexicon - Grammar tables to LMF : application to French 1

In this chapter, we describe the first experiment of conversion of Lexicon-Grammar tables for French verbs into the LMF format. The Lexicon-Grammar of the French language is currently one of the major sources of lexical and syntactic information for French. Its conversion into an interoperable representation format according to the LMF standard makes it usable in different contexts, thus contri...