Abstract: A word recognition architecture based on a network
of neural associative memories and hidden Markov models has been
developed. The input stream, composed of subword-units like wordinternal
triphones consisting of diphones and triphones, is provided
to the network of neural associative memories by hidden Markov
models. The word recognition network derives words from this input
stream. The architecture has the ability to handle ambiguities on
subword-unit level and is also able to add new words to the
vocabulary during performance. The architecture is implemented to
perform the word recognition task in a language processing system
for understanding simple command sentences like “bot show apple".
Abstract: This paper explores the use of project work in a
content-based instruction in a Rajabhat University, a teacher college,
where student teachers are instructed to perform teaching roles
mainly in basic education level. Its aim is to link theory to practice,
and to help language teachers maximize the full potential of project
work for genuine communication and give real meaning to writing
activity. Two research questions are formulated to guide this study:
a) What is the academic achievement of the students- writing skill
against the 70% attainment target after the use of project to enhance
the skill? and b) To what degree is the development of the students-
writing skills during the course of project to enhance the skill? The
sample of the study comprised of 38 fourth-year English major
students. The data was collected by means of achievement test,
student writing works, and project diary. The scores in the summative
achievement test were analyzed by mean score, standard deviation,
and t-test. Project diary serves as students- record of the language
acquired during the project. List of structures and vocabulary noted in
the diary has shown students- ability to attend to, recognize, and
focus on meaningful patterns of language forms.
Abstract: Parsing is important in Linguistics and Natural
Language Processing to understand the syntax and semantics of a
natural language grammar. Parsing natural language text is
challenging because of the problems like ambiguity and inefficiency.
Also the interpretation of natural language text depends on context
based techniques. A probabilistic component is essential to resolve
ambiguity in both syntax and semantics thereby increasing accuracy
and efficiency of the parser. Tamil language has some inherent
features which are more challenging. In order to obtain the solutions,
lexicalized and statistical approach is to be applied in the parsing
with the aid of a language model. Statistical models mainly focus on
semantics of the language which are suitable for large vocabulary
tasks where as structural methods focus on syntax which models
small vocabulary tasks. A statistical language model based on Trigram
for Tamil language with medium vocabulary of 5000 words has
been built. Though statistical parsing gives better performance
through tri-gram probabilities and large vocabulary size, it has some
disadvantages like focus on semantics rather than syntax, lack of
support in free ordering of words and long term relationship. To
overcome the disadvantages a structural component is to be
incorporated in statistical language models which leads to the
implementation of hybrid language models. This paper has attempted
to build phrase structured hybrid language model which resolves
above mentioned disadvantages. In the development of hybrid
language model, new part of speech tag set for Tamil language has
been developed with more than 500 tags which have the wider
coverage. A phrase structured Treebank has been developed with 326
Tamil sentences which covers more than 5000 words. A hybrid
language model has been trained with the phrase structured Treebank
using immediate head parsing technique. Lexicalized and statistical
parser which employs this hybrid language model and immediate
head parsing technique gives better results than pure grammar and
trigram based model.