Studies on a syntax based approach for translation between structurally different languages and the development of a prototype for Malayalam to English translation

This thesis summarizes the results on the studies on a syntax based approach for translation between Malayalam, one of Dravidian languages and English and also on the development of the major modules in building a prototype machine translation system from Malayalam to English. The development of the system is a pioneering effort in Malayalam language unattempted by previous researchers. The computational models chosen for the system is first of its kind for Malayalam language. An in depth study has been carried out in the design of the computational models and data structures needed for different modules: morphological analyzer , a parser, a syntactic structure transfer module and target language sentence generator required for the prototype system. The generation of list of part of speech tags, chunk tags and the hierarchical dependencies among the chunks required for the translation process also has been done. In the development process, the major goals are: (a) accuracy of translation (b) speed and (c) space. Accuracy-wise, smart tools for handling transfer grammar and translation standards including equivalent words, expressions, phrases and styles in the target language are to be developed. The grammar should be optimized with a view to obtaining a single correct parse and hence a single translated output. Speed-wise, innovative use of corpus analysis, efficient parsing algorithm, design of efficient Data Structure and run-time frequency-based rearrangement of the grammar which substantially reduces the parsing and generation time are required. The space requirement also has to be minimised

Application of bacterins and yeast Acremonium dyosporii to protect the larvae of Macrobrachium rosenbergii from vibriosis

The fresh water prawn, Macrobrachium rosenbergii, has proven potential for use as an aquaculture species (Hanson & Goodwin, 1997; Kurup, 1984). In India alone, culture of this species of prawn in low saline areas requires about 200 million seed per year (Kurup, 1984). In hatcheries poor survival rate has been associated with vibriosis at di#erent stages of the larval cycle. Members of the family Vibrionaceae associated with the larvae of M. rosenbergii were shown to be pathogenic under laboratory conditions (Bhat et al., 2000, in press). Vibrios have been associated with mortality of penaeid prawns by several workers (Aquacop, 1977; Hameed, 1993; Karunasagar et al., 1994). Two methods have been suggested to protect both the larvae and juveniles from vibriosis; one is the administration of bacterins prepared from pathogenic strains (Itami et al., 1989, 1991; Adams, 1991; Song & Sung, 1990; Sung et al., 1991) and the other is the utilization of yeast 1-3 and 1-6 glucans as immunostimulants for enhancing the non-specific defense system (Sung et al., 1994; Song et al., 1997). In the light of these observations it was hypothesised that bacterins and yeast glucans may also be e#ective in protecting the larvae of M. rosenbergii from vibriosis as has been achieved in the case of penaeids. To examine this hypothesis, the ability of bacterins and an extracellular glucan-producing yeast to increase the overall survival and metamorphosis of larvae in a hatchery, as well as to protect against an experimental challenge under laboratory conditions, was evaluated

Syntactic Based Machine Translation from English to Malayalam

Due to the emergence of multiple language support on the Internet, machine translation (MT) technologies are indispensable to the communication between speakers using different languages. Recent research works have started to explore tree-based machine translation systems with syntactical and morphological information. This work aims the development of Syntactic Based Machine Translation from English to Malayalam by adding different case information during translation. The system identifies general rules for various sentence patterns in English. These rules are generated using the Parts Of Speech (POS) tag information of the texts. Word Reordering based on the Syntax Tree is used to improve the translation quality of the system. The system used Bilingual English –Malayalam dictionary for translation.