Abstract: A lot has been said and discussed regarding the rationale and significance of the Bechdel Score. It became a digital sensation in 2013, when Swedish cinemas began to showcase the Bechdel test score of a film alongside its rating. The test has drawn criticism from experts and the film fraternity regarding its use to rate the female presence in a movie. The pundits believe that the score is too simplified and the underlying criteria of a film to pass the test must include 1) at least two women, 2) who have at least one dialogue, 3) about something other than a man, is egregious. In this research, we have considered a few more parameters which highlight how we represent females in film, like the number of female dialogues in a movie, dialogue genre, and part of speech tags in the dialogue. The parameters were missing in the existing criteria to calculate the Bechdel score. The research aims to analyze 342 movies scripts to test a hypothesis if these extra parameters, above with the current Bechdel criteria, are significant in calculating the female representation score. The result of the Principal Component Analysis method concludes that the female dialogue content is a key component and should be considered while measuring the representation of women in a work of fiction.
Abstract: The paper deals with the main issues of methodology of the Corpus of Spoken Lithuanian which was started to be developed in 2006. At present, the corpus consists of 300,000 grammatically annotated word forms. The creation of the corpus consists of three main stages: collecting the data, the transcription of the recorded data, and the grammatical annotation. Collecting the data was based on the principles of balance and naturality. The recorded speech was transcribed according to the CHAT requirements of CHILDES. The transcripts were double-checked and annotated grammatically using CHILDES. The development of the Corpus of Spoken Lithuanian has led to the constant increase in studies on spontaneous communication, and various papers have dealt with a distribution of parts of speech, use of different grammatical forms, variation of inflectional paradigms, distribution of fillers, syntactic functions of adjectives, the mean length of utterances.
Abstract: This research uses computational linguistics, an area of study that employs a computer to process natural language, and aims at discerning the patterns that exist in declarative sentences used in technical texts. The approach is mathematical, and the focus is on instructional texts found on web pages. The technique developed by the author and named the MAYA Semantic Technique is used here and organized into four stages. In the first stage, the parts of speech in each sentence are identified. In the second stage, the subject of the sentence is determined. In the third stage, MAYA performs a frequency analysis on the remaining words to determine the verb and its object. In the fourth stage, MAYA does statistical analysis to determine the content of the web page. The advantage of the MAYA Semantic Technique lies in its use of mathematical principles to represent grammatical operations which assist processing and accuracy if performed on unambiguous text. The MAYA Semantic Technique is part of a proposed architecture for an entire web-based intelligent tutoring system. On a sample set of sentences, partial semantics derived using the MAYA Semantic Technique were approximately 80% accurate. The system currently processes technical text in one domain, namely Cµ programming. In this domain all the keywords and programming concepts are known and understood.
Abstract: Machine Translation, (hereafter in this document
referred to as the "MT") faces a lot of complex problems from its
origination. Extracting multiword expressions is also one of the
complex problems in MT. Finding multiword expressions during
translating a sentence from English into Urdu, through existing
solutions, takes a lot of time and occupies system resources. We have
designed a simple relational data approach, in which we simply set a
bit in dictionary (database) for multiword, to find and handle
multiword expression. This approach handles multiword efficiently.
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.