15,269 research outputs found
Structural Stability of Lexical Semantic Spaces: Nouns in Chinese and French
Many studies in the neurosciences have dealt with the semantic processing of
words or categories, but few have looked into the semantic organization of the
lexicon thought as a system. The present study was designed to try to move
towards this goal, using both electrophysiological and corpus-based data, and
to compare two languages from different families: French and Mandarin Chinese.
We conducted an EEG-based semantic-decision experiment using 240 words from
eight categories (clothing, parts of a house, tools, vehicles,
fruits/vegetables, animals, body parts, and people) as the material. A
data-analysis method (correspondence analysis) commonly used in computational
linguistics was applied to the electrophysiological signals.
The present cross-language comparison indicated stability for the following
aspects of the languages' lexical semantic organizations: (1) the
living/nonliving distinction, which showed up as a main factor for both
languages; (2) greater dispersion of the living categories as compared to the
nonliving ones; (3) prototypicality of the \emph{animals} category within the
living categories, and with respect to the living/nonliving distinction; and
(4) the existence of a person-centered reference gradient. Our
electrophysiological analysis indicated stability of the networks at play in
each of these processes. Stability was also observed in the data taken from
word usage in the languages (synonyms and associated words obtained from
textual corpora).Comment: 17 pages, 4 figure
Translation and the creation of a new genre : a corpus-based study of interaction in English and Chinese popular science writings
Abstract unavailable please refer to PD
Testing for a Cultural Influence on Reading for Meaning in the Developing Brain: The Neural Basis of Semantic Processing in Chinese Children
Functional magnetic resonance imaging was used to explore the neural correlates of semantic judgments in a group of 8- to 15-year-old Chinese children. Participants were asked to indicate if pairs of Chinese characters presented visually were related in meaning. The related pairs were arranged in a continuous variable according to association strength. Pairs of characters with weaker semantic association elicited greater activation in the mid ventral region (BA 45) of left inferior frontal gyrus, suggesting increased demands on the process of selecting appropriate semantic features. By contrast, characters with stronger semantic association elicited greater activation in left inferior parietal lobule (BA 39), suggesting stronger integration of highly related features. In addition, there was a developmental increase, similar to previously reported findings in English, in left posterior middle temporal gyrus (BA 21), suggesting that older children have more elaborated semantic representations. There were additional age-related increases in the posterior region of left inferior parietal lobule and in the ventral regions of left inferior frontal gyrus, suggesting that reading acquisition relies more on the mapping from orthography to semantics in Chinese children as compared to previously reported findings in English
PersoNER: Persian named-entity recognition
© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network
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