97 research outputs found
Proceedings of the 17th Annual Conference of the European Association for Machine Translation
Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT
Multilingual sentiment analysis in social media.
252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations
Phraseology in Corpus-Based Translation Studies: A Stylistic Study of Two Contemporary Chinese Translations of Cervantes's Don Quijote
The present work sets out to investigate the stylistic profiles of two modern Chinese versions of
Cervantes’s Don Quijote (I): by Yang Jiang (1978), the first direct translation from Castilian to Chinese,
and by Liu Jingsheng (1995), which is one of the most commercially successful versions of the
Castilian literary classic. This thesis focuses on a detailed linguistic analysis carried out with the help
of the latest textual analytical tools, natural language processing applications and statistical packages.
The type of linguistic phenomenon singled out for study is four-character expressions (FCEXs), which
are a very typical category of Chinese phraseology. The work opens with the creation of a descriptive
framework for the annotation of linguistic data extracted from the parallel corpus of Don Quijote.
Subsequently, the classified and extracted data are put through several statistical tests. The results of
these tests prove to be very revealing regarding the different use of FCEXs in the two Chinese
translations. The computational modelling of the linguistic data would seem to indicate that among
other findings, while Liu’s use of archaic idioms has followed the general patterns of the original and
also of Yang’s work in the first half of Don Quijote I, noticeable variations begin to emerge in the
second half of Liu’s more recent version. Such an idiosyncratic use of archaisms by Liu, which may be
defined as style shifting or style variation, is then analyzed in quantitative terms through the application
of the proposed context-motivated theory (CMT). The results of applying the CMT-derived statistical
models show that the detected stylistic variation may well point to the internal consistency of the
translator in rendering the second half of Part I of the novel, which reflects his freer, more creative and
experimental style of translation. Through the introduction and testing of quantitative research methods
adapted from corpus linguistics and textual statistics, this thesis has made a major contribution to
methodological innovation in the study of style within the context of corpus-based translation studies
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