Countability and number in Japanese to English machine translation

Bond, Francis; Ogura, Kentaro; Ikehara, Satoru

doi:10.3115/991886.991889

Cited by 18 publications

(15 citation statements)

References 3 publications

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“…We looked up the countability from the transfer lexicon used in the Japanese-toEnglish machine translation system ALT-J/E (Ikehara et al, 1991). We used six values for the countability feature: FC (fully countable) for nouns that have both singular and plural forms and can be directly modified by numerals and modifiers such as many; UC (uncountable) for nouns that have no plural form and can be modified by much; SC (strongly countable) for nouns that are more often countable than uncountable; WC (weakly countable) for nouns that are more often uncountable than countable; and PT (pluralia tantum) for nouns that only have plural forms, such as for example, scissors (Bond et al, 1994). Finally, we used the value UNKNOWN if the lexicon did not provide countability information for a noun or if the head of the NP was not a noun.…”

Section: Features Determining Automatedmentioning

confidence: 99%

Memory-based learning for article generation

Minnen¹,

Bond²,

Copestake³

2000

Proceedings of the 2nd Workshop on Learning Language in Logic and the 4th Conference on Computational Natural Language Learning

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Article choice can pose difficult problems in applications such as machine translation and automated summarization. In this paper, we investigate the use of corpus data to collect statistical generalizations about article use in English in order to be able to generate articles automatically to supplement a symbolic generator. We use data from the Penn Treebank as input to a memory-based learner (TiMBL 3.0;Daelemans et al., 2000) which predicts whether to generate an article with respect to an English base noun phrase. We discuss competitive results obtained using a variety of lexical, syntactic and semantic features that play an important role in automated article generation.

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Section: Features Determining Automatedmentioning

confidence: 99%

Memory-based learning for article generation

Minnen¹,

Bond²,

Copestake³

2000

Proceedings of the 2nd Workshop on Learning Language in Logic and the 4th Conference on Computational Natural Language Learning

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“…These constructions are also problematic for handcrafted MT systems(Bond et al, 1994). 2 CLEC tagging is not comprehensive; some common mass noun errors (e.g., make a good progress) are not tagged in this corpus.…”

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confidence: 99%

Correcting ESL errors using phrasal SMT techniques

Brockett

Dolan

Gamon

2006

Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the ACL - ACL '06

135

123

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This paper presents a pilot study of the use of phrasal Statistical Machine Translation (SMT) techniques to identify and correct writing errors made by learners of English as a Second Language (ESL). Using examples of mass noun errors found in the Chinese Learner Error Corpus (CLEC) to guide creation of an engineered training set, we show that application of the SMT paradigm can capture errors not well addressed by widely-used proofing tools designed for native speakers. Our system was able to correct 61.81% of mistakes in a set of naturallyoccurring examples of mass noun errors found on the World Wide Web, suggesting that efforts to collect alignable corpora of pre-and post-editing ESL writing samples offer can enable the development of SMT-based writing assistance tools capable of repairing many of the complex syntactic and lexical problems found in the writing of ESL learners.

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“…Based on Allan's (1980) NCPs, Bond, Ogura, and Ikehara (1994) similarly divided nouns into five major categories. These are as follows:…”

Section: Countabilitymentioning

confidence: 99%

A Closer Look at the English Article System: Internal and External Sources of Difficulty Revisited

Harb

2014

IJL

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This paper aims to provide a short overview of the English article system along with a detailed account of the sources of difficulty that makes such a complicated system uneasily attainable for ESL learners. According to Hewson (1972), "the definite and the indefinite articles are among the ten most frequent words of English discourse" (p.131). As such, learners of English are likely to be heavily exposed to hundreds of instances that contain a wide range of uses of English articles, which will, presumably, provide non-native speakers with a perceptibly increased level of accuracy. However, the English article system has been reported to be one of the most difficult features of the English language, causing some students enormous difficulties; particularly, of course, those whose native languages do not use articles (Master, 1990; Mizuno, 1999; Park, 2006; inter alia). In this paper, I will discuss the factors that make the English article system difficult for speakers of other languages to use appropriately. The paper will be divided into three sections: (1) a short overview of the article system, (2) internal factors: those that pertain to the internal structure of English such as lexico-syntactic and discoursal factors, and (3) external factors: those that pertain to other languages such as presence or absence of the article system. Possibly unlike previously published research, this paper combines both factors and further works to the advantage of new instructors as it provides them with the rationale behind ESL learners' inappropriate usage of the article system.

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Countability and number in Japanese to English machine translation

Cited by 18 publications

References 3 publications

Memory-based learning for article generation

Memory-based learning for article generation

Correcting ESL errors using phrasal SMT techniques

A Closer Look at the English Article System: Internal and External Sources of Difficulty Revisited

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