6,951 research outputs found
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
Japanese/English Cross-Language Information Retrieval: Exploration of Query Translation and Transliteration
Cross-language information retrieval (CLIR), where queries and documents are
in different languages, has of late become one of the major topics within the
information retrieval community. This paper proposes a Japanese/English CLIR
system, where we combine a query translation and retrieval modules. We
currently target the retrieval of technical documents, and therefore the
performance of our system is highly dependent on the quality of the translation
of technical terms. However, the technical term translation is still
problematic in that technical terms are often compound words, and thus new
terms are progressively created by combining existing base words. In addition,
Japanese often represents loanwords based on its special phonogram.
Consequently, existing dictionaries find it difficult to achieve sufficient
coverage. To counter the first problem, we produce a Japanese/English
dictionary for base words, and translate compound words on a word-by-word
basis. We also use a probabilistic method to resolve translation ambiguity. For
the second problem, we use a transliteration method, which corresponds words
unlisted in the base word dictionary to their phonetic equivalents in the
target language. We evaluate our system using a test collection for CLIR, and
show that both the compound word translation and transliteration methods
improve the system performance
Automated Building of Sentence-Level Parallel Corpus and Chinese-Hungarian Dictionary
Decades of work have been conducted on automated building of parallel corpus and automatic dictionary in the field of natural language processing. However, rarely have any studies been done between high-density character-based languages and medium-density word-based languages due to the lack of resources and fundamental linguistic differences. In this paper, we describe a methodology for creating a sentence-level paralleled corpus and an automatic bilingual dictionary between Chinese (a high-density character-based language) and Hungarian (a medium-density word-based language). This method will possibly be applied to create Chinese-Hungarian bilingual dictionary for the Sztaki Dictionary project [http://szotar.sztaki.hu/]
The Role of Text Pre-processing in Sentiment Analysis
It is challenging to understand the latest trends and summarise the state or general opinions about products due to the big diversity and size of social media data, and this creates the need of automated and real time opinion extraction and mining. Mining online opinion is a form of sentiment analysis that is treated as a difficult text classification task. In this paper, we explore the role of text pre-processing in sentiment analysis, and report on experimental results that demonstrate that with appropriate feature selection and representation, sentiment analysis accuracies using support vector machines (SVM) in this area may be significantly improved. The level of accuracy achieved is shown to be comparable to the ones achieved in topic categorisation although sentiment analysis is considered to be a much harder problem in the literature
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