D6.1: Technologies and Tools for Lexical Acquisition

This report describes the technologies and tools to be used for Lexical Acquisition in PANACEA. It includes descriptions of existing technologies and tools which can be built on and improved within PANACEA, as well as of new technologies and tools to be developed and integrated in PANACEA platform. The report also specifies the Lexical Resources to be produced. Four main areas of lexical acquisition are included: Subcategorization frames (SCFs), Selectional Preferences (SPs), Lexical-semantic Classes (LCs), for both nouns and verbs, and Multi-Word Expressions (MWEs)

The challenge of olfactory ideophones : Reconsidering ineffability from the Totonac-Tepehua perspective

Olfactory impressions are said to be ineffable, but little systematic exploration has been done to substantiate this. We explored olfactory language in Huehuetla Tepehua—a Totonac-Tepehua language spoken in Hidalgo, Mexico—which has a large inventory of ideophones, words with sound-symbolic properties used to describe perceptuomotor experiences. A multi-method study found Huehuetla Tepehua has 45 olfactory ideophones, illustrating intriguing sound-symbolic alternation patterns. Elaboration in the olfactory domain is not unique to this language; related Totonac-Tepehua languages also have impressive smell lexicons. Comparison across these languages shows olfactory and gustatory terms overlap in interesting ways, mirroring the physiology of smelling and tasting. However, although cognate taste terms are formally similar, olfactory terms are less so. We suggest the relative instability of smell vocabulary in comparison with those of taste likely results from the more varied olfactory experiences caused by the mutability of smells in different environments

Automatic domain-specific learning: towards a methodology for ontology enrichment

[EN] At the current rate of technological development, in a world where enormous amount of data are constantly created and in which the Internet is used as the primary means for information exchange, there exists a need for tools that help processing, analyzing and using that information. However, while the growth of information poses many opportunities for social and scientific advance, it has also highlighted the difficulties of extracting meaningful patterns from massive data. Ontologies have been claimed to play a major role in the processing of large-scale data, as they serve as universal models of knowledge representation, and are being studied as possible solutions to this. This paper presents a method for the automatic expansion of ontologies based on corpus and terminological data exploitation. The proposed ¿ontology enrichment method¿ (OEM) consists of a sequence of tasks aimed at classifying an input keyword automatically under its corresponding node within a target ontology. Results prove that the method can be successfully applied for the automatic classification of specialized units into a reference ontology.Financial support for this research has been provided by the DGI, Spanish Ministry of Education and Science, grant FFI2011-29798-C0201.Ureña Gómez-Moreno, P.; Mestre-Mestre, EM. (2017). Automatic domain-specific learning: towards a methodology for ontology enrichment. LFE. Revista de Lenguas para Fines Específicos. 23(2):63-85. http://hdl.handle.net/10251/148357S638523