3,856 research outputs found
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding
We construct a multilingual common semantic space based on distributional
semantics, where words from multiple languages are projected into a shared
space to enable knowledge and resource transfer across languages. Beyond word
alignment, we introduce multiple cluster-level alignments and enforce the word
clusters to be consistently distributed across multiple languages. We exploit
three signals for clustering: (1) neighbor words in the monolingual word
embedding space; (2) character-level information; and (3) linguistic properties
(e.g., apposition, locative suffix) derived from linguistic structure knowledge
bases available for thousands of languages. We introduce a new
cluster-consistent correlational neural network to construct the common
semantic space by aligning words as well as clusters. Intrinsic evaluation on
monolingual and multilingual QVEC tasks shows our approach achieves
significantly higher correlation with linguistic features than state-of-the-art
multi-lingual embedding learning methods do. Using low-resource language name
tagging as a case study for extrinsic evaluation, our approach achieves up to
24.5\% absolute F-score gain over the state of the art.Comment: 10 page
Automatic Construction of Clean Broad-Coverage Translation Lexicons
Word-level translational equivalences can be extracted from parallel texts by
surprisingly simple statistical techniques. However, these techniques are
easily fooled by {\em indirect associations} --- pairs of unrelated words whose
statistical properties resemble those of mutual translations. Indirect
associations pollute the resulting translation lexicons, drastically reducing
their precision. This paper presents an iterative lexicon cleaning method. On
each iteration, most of the remaining incorrect lexicon entries are filtered
out, without significant degradation in recall. This lexicon cleaning technique
can produce translation lexicons with recall and precision both exceeding 90\%,
as well as dictionary-sized translation lexicons that are over 99\% correct.Comment: PostScript file, 10 pages. To appear in Proceedings of AMTA-9
Dublin City University at CLEF 2007: Cross-Language Speech Retrieval Experiments
The Dublin City University participation in the CLEF 2007 CL-SR English task concentrated primarily on issues of topic translation. Our retrieval system used the BM25F model and pseudo relevance feedback. Topics were translated into English using the Yahoo! BabelFish free online service combined with domain-specific translation lexicons gathered automatically from Wikipedia. We explored alternative topic translation methods using these resources. Our results indicate that extending machine translation tools using automatically generated domainspecific translation lexicons can provide improved CLIR effectiveness for this task
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
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