Information access and exchange. Cognitively Salient Relations for Multilingual Lexicography. Work in Cognitive Sciences. Transcription and Normalization. Mapping to Relation Types. Clustering by Property Types. Information about synonyms and antonyms.
При низкой оригинальности работы "Cognitive aspects of lexicon in the light of the language picture of the world", Вы можете повысить уникальность этой работы до 80-100%
In particular, we focus here on those related words that can be seen as systematic properties of the target entry, i. e., the basic concepts that would be used to de?ne the entry in relation to its super ordinate category and coordinate concepts. Sets of relevant and salient properties allow the user to collocate a word within its so-called “word ?eld” and to distinguish it more clearly from neighbour concepts, since the meaning of a word is not de?ned in isolation, but in contrast to related words in its word ?eld (Geckeler, 2002). Finally, this knowledge might be used as a basis to populate lexical networks by building models of concepts in terms of “relation sketches” based on salient typed properties (when an animal is added to our lexicon, we know that we will have to search a corpus to extract its parts, behaviour, etc., whereas for a tool the function would be the most important property to mine). Cognitive scientists focus on “concepts”, glossing over the fact that what subjects will produce are (strings of) words, and as such they will be, at least to a certain extent, language-dependent. The stimuli for the experiment consisted of 50 concrete concepts from 10 different classes (i. e., 5 concepts for each of the classes): mammal (dog, horse, rabbit, bear, monkey), bird (seagull, sparrow, woodpecker, owl, goose), fruit (apple, orange, pear, pineapple, cherry), vegetable (corn, onion, spinach, peas, potato), body part (eye, ?nger, head, leg, hand), clothing (chemise, jacket, sweater, shoes, socks), manipulability tool (comb, broom, sword, paintbrush, tongs), vehicle (bus, ship, airplane, train, truck), furniture (table, bed, chair, closet, armchair), and building (garage, bridge, skyscraper, church, tower).The resulting most salient relations are to be used for revising and adding to the word ?eld entries of a multilingual electronic dictionary in a language learning environment. Moreover, these patterns were robust across the two native languages studied in the experiment - even though a closer look at the data suggested that linguistic constraints might affect (verbalisations of) conceptual representations (and thus, to a certain extent, which properties are produced). This is a promising result to be used for automatically harvesting semantically related words for a given lexical entry of a concept class. Moreover, the stimuli set will have to be expanded to include, e. g., abstract concepts - although we hope to mine some abstract concept classes on the basis of the properties of our concept set (colors, for example, could be characterized by the concrete objects of which they are typical).
Вывод
This research is part of a project that aims to investigate the cognitive salience of semantic relations for (pedagogical) lexicographic purposes. The resulting most salient relations are to be used for revising and adding to the word ?eld entries of a multilingual electronic dictionary in a language learning environment.
We presented a multilingual concept description experiment. Participants produced different semantic relation type patterns across concept classes. Moreover, these patterns were robust across the two native languages studied in the experiment - even though a closer look at the data suggested that linguistic constraints might affect (verbalisations of) conceptual representations (and thus, to a certain extent, which properties are produced). This is a promising result to be used for automatically harvesting semantically related words for a given lexical entry of a concept class.
However, the granularity of concept classes has to be de?ned. In addition, to yield a larger number of usable data for the analysis, a re-mapping of the rare semantic relation types occurring in the actual data set should be conducted. Moreover, the stimuli set will have to be expanded to include, e. g., abstract concepts - although we hope to mine some abstract concept classes on the basis of the properties of our concept set (colors, for example, could be characterized by the concrete objects of which they are typical).
To complement the production experiment results, we aim to conduct an experiment which investigates the perceptual salience of the produced semantic relations (and possibly additional ones), in order to detect inconsistencies between generation and retrieval of salient properties. If, as we hope, we will ?nd that essentially the same properties are salient for each class across languages and both in production and perception, we will then have a pretty strong argument to suggest that these are the relations one should focus on when populating multilingual dictionaries.
Of course, the ultimate test of our approach will come from empirical evidence of the usefulness of our relation links to the language learner. This is, however, beyond the scope of the current project.
Размещено на
Вы можете ЗАГРУЗИТЬ и ПОВЫСИТЬ уникальность своей работы
