999 research outputs found
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
PRIME: A System for Multi-lingual Patent Retrieval
Given the growing number of patents filed in multiple countries, users are
interested in retrieving patents across languages. We propose a multi-lingual
patent retrieval system, which translates a user query into the target
language, searches a multilingual database for patents relevant to the query,
and improves the browsing efficiency by way of machine translation and
clustering. Our system also extracts new translations from patent families
consisting of comparable patents, to enhance the translation dictionary
A Comparison of Different Machine Transliteration Models
Machine transliteration is a method for automatically converting words in one
language into phonetically equivalent ones in another language. Machine
transliteration plays an important role in natural language applications such
as information retrieval and machine translation, especially for handling
proper nouns and technical terms. Four machine transliteration models --
grapheme-based transliteration model, phoneme-based transliteration model,
hybrid transliteration model, and correspondence-based transliteration model --
have been proposed by several researchers. To date, however, there has been
little research on a framework in which multiple transliteration models can
operate simultaneously. Furthermore, there has been no comparison of the four
models within the same framework and using the same data. We addressed these
problems by 1) modeling the four models within the same framework, 2) comparing
them under the same conditions, and 3) developing a way to improve machine
transliteration through this comparison. Our comparison showed that the hybrid
and correspondence-based models were the most effective and that the four
models can be used in a complementary manner to improve machine transliteration
performance
Applying Machine Translation to Two-Stage Cross-Language Information Retrieval
Cross-language information retrieval (CLIR), where queries and documents are
in different languages, needs a translation of queries and/or documents, so as
to standardize both of them into a common representation. For this purpose, the
use of machine translation is an effective approach. However, computational
cost is prohibitive in translating large-scale document collections. To resolve
this problem, we propose a two-stage CLIR method. First, we translate a given
query into the document language, and retrieve a limited number of foreign
documents. Second, we machine translate only those documents into the user
language, and re-rank them based on the translation result. We also show the
effectiveness of our method by way of experiments using Japanese queries and
English technical documents.Comment: 13 pages, 1 Postscript figur
An analysis of machine translation errors on the effectiveness of an Arabic-English QA system
The aim of this paper is to investigate
how much the effectiveness of a Question
Answering (QA) system was affected
by the performance of Machine
Translation (MT) based question translation.
Nearly 200 questions were selected
from TREC QA tracks and ran through a
question answering system. It was able to
answer 42.6% of the questions correctly
in a monolingual run. These questions
were then translated manually from English
into Arabic and back into English using
an MT system, and then re-applied to
the QA system. The system was able to
answer 10.2% of the translated questions.
An analysis of what sort of translation error
affected which questions was conducted,
concluding that factoid type
questions are less prone to translation error
than others
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