37,827 research outputs found
Filling Knowledge Gaps in a Broad-Coverage Machine Translation System
Knowledge-based machine translation (KBMT) techniques yield high quality in
domains with detailed semantic models, limited vocabulary, and controlled input
grammar. Scaling up along these dimensions means acquiring large knowledge
resources. It also means behaving reasonably when definitive knowledge is not
yet available. This paper describes how we can fill various KBMT knowledge
gaps, often using robust statistical techniques. We describe quantitative and
qualitative results from JAPANGLOSS, a broad-coverage Japanese-English MT
system.Comment: 7 pages, Compressed and uuencoded postscript. To appear: IJCAI-9
A retrospective view on the promise on machine translation for Bahasa Melayu-English
Research and development activities for machine translation systems from English language to others are more progressive than vice versa. It has been more than 30 years since the machine translation was introduced and yet a Malay language or Bahasa Melayu (BM) to English machine translation engine is not available. Consequently, many translation systems have been developed for the world's top 10 languages in terms of native speakers, but none for BM, although the language is used by more than 200 million speakers around the world. This paper attempts to seek possible reasons as why such situation occurs. A summative overview to show progress, challenges as well as future works on MT is presented. Issues faced by researchers and system developers in modeling and developing a machine translation engine are also discussed. The study of the previous translation systems (from other languages to English) reveals that the accuracy level can be achieved up to 85 %. The figure suggests that the translation system is not reliable if it is to be utilized in a serious translation activity. The most prominent difficulties are the complexity of grammar rules and ambiguity problems of the source language. Thus, we hypothesize that the inclusion of ‘semantic’ property in the translation rules may produce a better quality BM-English MT engine
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
Interlingual Lexical Organisation for Multilingual Lexical Databases in NADIA
We propose a lexical organisation for multilingual lexical databases (MLDB).
This organisation is based on acceptions (word-senses). We detail this lexical
organisation and show a mock-up built to experiment with it. We also present
our current work in defining and prototyping a specialised system for the
management of acception-based MLDB. Keywords: multilingual lexical database,
acception, linguistic structure.Comment: 5 pages, Macintosh Postscript, published in COLING-94, pp. 278-28
Combining Multiple Methods for the Automatic Construction of Multilingual WordNets
This paper explores the automatic construction of a multilingual Lexical
Knowledge Base from preexisting lexical resources. First, a set of automatic
and complementary techniques for linking Spanish words collected from
monolingual and bilingual MRDs to English WordNet synsets are described.
Second, we show how resulting data provided by each method is then combined to
produce a preliminary version of a Spanish WordNet with an accuracy over 85%.
The application of these combinations results on an increment of the extracted
connexions of a 40% without losing accuracy. Both coarse-grained (class level)
and fine-grained (synset assignment level) confidence ratios are used and
evaluated. Finally, the results for the whole process are presented.Comment: 7 pages, 4 postscript figure
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
Pattern Matching and Discourse Processing in Information Extraction from Japanese Text
Information extraction is the task of automatically picking up information of
interest from an unconstrained text. Information of interest is usually
extracted in two steps. First, sentence level processing locates relevant
pieces of information scattered throughout the text; second, discourse
processing merges coreferential information to generate the output. In the
first step, pieces of information are locally identified without recognizing
any relationships among them. A key word search or simple pattern search can
achieve this purpose. The second step requires deeper knowledge in order to
understand relationships among separately identified pieces of information.
Previous information extraction systems focused on the first step, partly
because they were not required to link up each piece of information with other
pieces. To link the extracted pieces of information and map them onto a
structured output format, complex discourse processing is essential. This paper
reports on a Japanese information extraction system that merges information
using a pattern matcher and discourse processor. Evaluation results show a high
level of system performance which approaches human performance.Comment: See http://www.jair.org/ for any accompanying file
Acquiring Word-Meaning Mappings for Natural Language Interfaces
This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted
Examples), that acquires a semantic lexicon from a corpus of sentences paired
with semantic representations. The lexicon learned consists of phrases paired
with meaning representations. WOLFIE is part of an integrated system that
learns to transform sentences into representations such as logical database
queries. Experimental results are presented demonstrating WOLFIE's ability to
learn useful lexicons for a database interface in four different natural
languages. The usefulness of the lexicons learned by WOLFIE are compared to
those acquired by a similar system, with results favorable to WOLFIE. A second
set of experiments demonstrates WOLFIE's ability to scale to larger and more
difficult, albeit artificially generated, corpora. In natural language
acquisition, it is difficult to gather the annotated data needed for supervised
learning; however, unannotated data is fairly plentiful. Active learning
methods attempt to select for annotation and training only the most informative
examples, and therefore are potentially very useful in natural language
applications. However, most results to date for active learning have only
considered standard classification tasks. To reduce annotation effort while
maintaining accuracy, we apply active learning to semantic lexicons. We show
that active learning can significantly reduce the number of annotated examples
required to achieve a given level of performance
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