17,330 research outputs found
Manipulative impact of implicit communication: A comparative analysis of French, Italian and German political speeches
This paper presents the application of a quantitative model for measuring the impact of manipulative
implicit linguistic strategies on a small comparable corpus of Italian, German and
French political discourses. The aim is to show the cross-linguistic applicability of the model,
originally developed and put to test on Italian. Furthermore, the analysis allows a quantitative
and qualitative comparison of the three comparable corpora: some statistical correlations and
tendencies in the frequency and type of linguistic implicit strategies are presented and put in
relation not only with the language, but also with the political orientation of the speaker and
with other parameters (context, subject, rhetorical style). Results show that the model can be
applied to multiple languages and that inter- and intra-linguistic tendencies in the use of manipulative
implicit linguistic strategies can be appreciated
Noisy-parallel and comparable corpora filtering methodology for the extraction of bi-lingual equivalent data at sentence level
Text alignment and text quality are critical to the accuracy of Machine
Translation (MT) systems, some NLP tools, and any other text processing tasks
requiring bilingual data. This research proposes a language independent
bi-sentence filtering approach based on Polish (not a position-sensitive
language) to English experiments. This cleaning approach was developed on the
TED Talks corpus and also initially tested on the Wikipedia comparable corpus,
but it can be used for any text domain or language pair. The proposed approach
implements various heuristics for sentence comparison. Some of them leverage
synonyms and semantic and structural analysis of text as additional
information. Minimization of data loss was ensured. An improvement in MT system
score with text processed using the tool is discussed.Comment: arXiv admin note: text overlap with arXiv:1509.09093,
arXiv:1509.0888
In no uncertain terms : a dataset for monolingual and multilingual automatic term extraction from comparable corpora
Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in low inter-annotator agreement. There is a large need for well-documented, manually validated datasets, especially in the rising field of multilingual term extraction from comparable corpora, which presents a unique new set of challenges. In this paper, a new approach is presented for both monolingual and multilingual term annotation in comparable corpora. The detailed guidelines with different term labels, the domain- and language-independent methodology and the large volumes annotated in three different languages and four different domains make this a rich resource. The resulting datasets are not just suited for evaluation purposes but can also serve as a general source of information about terms and even as training data for supervised methods. Moreover, the gold standard for multilingual term extraction from comparable corpora contains information about term variants and translation equivalents, which allows an in-depth, nuanced evaluation
Termhood-based Comparability Metrics of Comparable Corpus in Special Domain
Cross-Language Information Retrieval (CLIR) and machine translation (MT)
resources, such as dictionaries and parallel corpora, are scarce and hard to
come by for special domains. Besides, these resources are just limited to a few
languages, such as English, French, and Spanish and so on. So, obtaining
comparable corpora automatically for such domains could be an answer to this
problem effectively. Comparable corpora, that the subcorpora are not
translations of each other, can be easily obtained from web. Therefore,
building and using comparable corpora is often a more feasible option in
multilingual information processing. Comparability metrics is one of key issues
in the field of building and using comparable corpus. Currently, there is no
widely accepted definition or metrics method of corpus comparability. In fact,
Different definitions or metrics methods of comparability might be given to
suit various tasks about natural language processing. A new comparability,
namely, termhood-based metrics, oriented to the task of bilingual terminology
extraction, is proposed in this paper. In this method, words are ranked by
termhood not frequency, and then the cosine similarities, calculated based on
the ranking lists of word termhood, is used as comparability. Experiments
results show that termhood-based metrics performs better than traditional
frequency-based metrics
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval
Although more and more language pairs are covered by machine translation
services, there are still many pairs that lack translation resources.
Cross-language information retrieval (CLIR) is an application which needs
translation functionality of a relatively low level of sophistication since
current models for information retrieval (IR) are still based on a
bag-of-words. The Web provides a vast resource for the automatic construction
of parallel corpora which can be used to train statistical translation models
automatically. The resulting translation models can be embedded in several ways
in a retrieval model. In this paper, we will investigate the problem of
automatically mining parallel texts from the Web and different ways of
integrating the translation models within the retrieval process. Our
experiments on standard test collections for CLIR show that the Web-based
translation models can surpass commercial MT systems in CLIR tasks. These
results open the perspective of constructing a fully automatic query
translation device for CLIR at a very low cost.Comment: 37 page
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