arXiv subjects

Atsushi Fujii

Publications and source records attributed to Atsushi Fujii.

22 records · Page 2Linked to original sources

arXiv1999

Selective Sampling for Example-based Word Sense Disambiguation

This paper proposes an efficient example sampling method for example-based word sense disambiguation systems. To construct a database of practical size, a considerable overhead for manual sense disambiguation (overhead for supervision) is required. In addition, the time complexity of searching a large-sized database poses a considerable problem (overhead for search). To counter these problems, our method selectively samples a smaller-sized effective subset from a given example set for use in word sense disambiguation. Our method is characterized by the reliance on the notion of training utility: the degree to which each example is informative for future example sampling when used for the training of the system. The system progressively collects examples by selecting those with greatest utility. The paper reports the effectiveness of our method through experiments on about one thousand sentences. Compared to experiments with other example sampling methods, our method reduced both the overhead for supervision and the overhead for search, without the degeneration of the performance of the system.

cs.CL↗

arXiv1999

Cross-Language Information Retrieval for Technical Documents

This paper proposes a Japanese/English cross-language information retrieval (CLIR) system targeting technical documents. Our system first translates a given query containing technical terms into the target language, and then retrieves documents relevant to the translated query. The translation of technical terms is still problematic in that technical terms are often compound words, and thus new terms can be progressively created simply by combining existing base words. In addition, Japanese often represents loanwords based on its phonogram. Consequently, existing dictionaries find it difficult to achieve sufficient coverage. To counter the first problem, we use a compound word translation method, which uses a bilingual dictionary for base words and collocational statistics to resolve translation ambiguity. For the second problem, we propose a transliteration method, which identifies phonetic equivalents in the target language. We also show the effectiveness of our system using a test collection for CLIR.

cs.CL↗

arXiv1998

Corpus-Based Word Sense Disambiguation

Resolution of lexical ambiguity, commonly termed ``word sense disambiguation'', is expected to improve the analytical accuracy for tasks which are sensitive to lexical semantics. Such tasks include machine translation, information retrieval, parsing, natural language understanding and lexicography. Reflecting the growth in utilization of machine readable texts, word sense disambiguation techniques have been explored variously in the context of corpus-based approaches. Within one corpus-based framework, that is the similarity-based method, systems use a database, in which example sentences are manually annotated with correct word senses. Given an input, systems search the database for the most similar example to the input. The lexical ambiguity of a word contained in the input is resolved by selecting the sense annotation of the retrieved example. In this research, we apply this method of resolution of verbal polysemy, in which the similarity between two examples is computed as the weighted average of the similarity between complements governed by a target polysemous verb. We explore similarity-based verb sense disambiguation focusing on the following three methods. First, we propose a weighting schema for each verb complement in the similarity computation. Second, in similarity-based techniques, the overhead for manual supervision and searching the large-sized database can be prohibitive. To resolve this problem, we propose a method to select a small number of effective examples, for system usage. Finally, the efficiency of our system is highly dependent on the similarity computation used. To maximize efficiency, we propose a method which integrates the advantages of previous methods for similarity computation.

cmp-lg↗

arXiv1997

Selective Sampling of Effective Example Sentence Sets for Word Sense Disambiguation

This paper proposes an efficient example selection method for example-based word sense disambiguation systems. To construct a practical size database, a considerable overhead for manual sense disambiguation is required. Our method is characterized by the reliance on the notion of the training utility: the degree to which each example is informative for future example selection when used for the training of the system. The system progressively collects examples by selecting those with greatest utility. The paper reports the effectivity of our method through experiments on about one thousand sentences. Compared to experiments with random example selection, our method reduced the overhead without the degeneration of the performance of the system.

cmp-lg↗