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Knowledge of Word Alignment Position Related To Parallel English-Hindi Sentences

Tarun Dhar Diwan, Rohit Miri, Amrita Verma

Abstract


This paper is based on knowledge of word alignment position related to parallel English Hindi sentences. This methodology is base to develop the parallel English Hindi word dictionary, after syntactically and semantically analysis of the English Hindi source text. Proposed system is using for aligning the English and Hindi sentences the methodology can be used for other languages. Large parallel corpus of english-hindi pair language is not frequently available. Solve this problem used two strategies. First is normalization of tagged English sentences and Hindi sentences. Second technique is mapping english-hindi sentence using parallel english-hindi word dictionary. Hence proposed system is desirable to encourage English and Hindi parallel sentences. A more detailed explanation is done for rule-based (constraint-based) part-of-speech tagging and morphological disambiguation. Some rule-based part-of-speech tagging studies on Turkish language are presented. Besides, there is an attached to the paper. English language recommend monolingual dictionaries as a source of important information concerning grammar information, collocations, spelling, pronunciation, context and etymology of words. There are a large number of materials which help students and teachers to work with dictionaries. However, not all of these materials can be used at lower secondary schools, because the situation at many primary and secondary schools is different. Results indicate that by combining these hand-crafted, statistical and learned information sources, a recall of 96 to 97% with a corresponding precision of 93 to 94% and ambiguity of 1.02 to 1.03 parses per token, on test texts is attained. However the impact of the rules that are learned is not significant as handcrafted rules do most of the easy work at the initial stages.

Keywords


Handcrafted Rules, Monolingual, Parser, Automatically, Speech Tagging, Parallel Sentences.

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References


Ahlswede, T. (1985, June 8-12). A tool kit for lexicon building. 24th Annual Meeting of the Association for Computational Linguistics. Chicago, Illinois: Association for Computational Linguistics.

Amsler, R. A. (1980). The structure of the Merriam-Webster pocket dictionary [Diss], Austin: University of Texas.

Amsler, R. A. (1982). Computational lexicology: A research program. In American Federated Information Processing Societies Conference Proceedings. National Computer Conference.

Atkins, B. T. S. (1991). Building a lexicon: The contribution of lexicography. International Journal of Lexicography, 4(3), 167-204.

Boguraev, B., & Briscoe, T. (1987). Large lexicons for natural language processing: Utilising the grammar coding system of LDOCE. Computational Linguistics, 13(3-4), 203-18.

Chodorow, M., Byrd, R., & Heidorn, G. (1985). Extracting semantic hierarchies from a large on-line dictionary. 23rd Annual Meeting of the Association for Computational Linguistics. Chicago, IL: Association for Computational Linguistics.

Dolan, W., Vanderwende, L., & Richardson, S. (2000). Polysemy in a broad-coverage natural language processing system. In Y. Ravin & C. Leacock (Eds.), Polysemy: Theoretical and Computational Approaches (pp. 178-204). Oxford: Oxford University Press.

Evens, M., & Smith, R. (1978). A lexicon for a computer question-answering system. American Journal of Computational Linguistics, Mf.81.

Evens, M. (ed.) (1988). Relational models of the lexicon: Representing knowledge in semantic networks. Studies in Natural Language Processing. Cambridge: Cambridge University Press.

Fellbaum, C. (ed.) (1998). WordNet: An electronic lexical database. Cambridge, Massachusetts: MIT Press.Firth, J. R. (1957). Modes of Meaning. In Papers in linguistics 1934-1951. Oxford: Oxford University Press.

Gove, P. (Ed.). (1972). Webster's Seventh New Collegiate Dictionary G & C. Merriam Grishman, R. (2003). Information Extraction. In R. Mitkov (Ed.), The Oxford handbook of computational linguistics. Oxford: Oxford University Press.

Hirst, G. (1987). Semantic interpretation and the resolution of ambiguity. Cambridge: Cambridge University Press.

Ide, N., & Veronis, J. (1990). Very large neural networks for word sense disambiguation. European Conference on Artificial Intelligence. Stockholm.


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