Browsing by Author "UzZaman, Naushad"

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Analysis of N-Gram based text categorization for Bangla in a newspaper
(BRAC University, 2006) Mansur, Munirul; UzZaman, Naushad; Khan, Mumit
In this paper, we study the outcome of using ngram based algorithm for Bangla text categorization. To analyze the efficiency of this methodology we used one year Prothom-Alo news corpus. Our results show that n-grams of length 2 or 3 are the most useful for categorization. Using gram lengths more than 3reduces the performance of categorization.
Comparison of different POS tagging techniques for some South Asian languages
(BRAC University, 2006-12) Hasan, Fahim Muhammad; Khan, Mumit; UzZaman, Naushad
There are different approaches to the problem of assigning a part of speech (POS) tag to each word of a natural language sentence. We present a comparison of the different approaches of POS tagging for the Bangla language and two other South Asian languages, as well as the baseline performances of different POS tagging techniques for the English language. The most widely used methods for English are the statistical methods i.e. n-gram based tagging or Hidden Markov Model (HMM) based tagging, the rule based or transformation based methods i.e. Brill’s tagger. Subsequent researches add various modifications to these basic approaches to improve the performance of the taggers for English. Here, we present an elaborate review of previous work in the area with the focus on South Asian Languages such as Hindi and Bangla. We experiment with Brill’s transformation based tagger and the supervised HMM based tagger without modifications for added improvement in accuracy, on English using training corpora of different sizes from the Brown corpus. We also compare the performances of these taggers on three South Asian languages with the focus on Bangla using two different tagsets and corpora of different sizes, which reveals that Brill's transformation based tagger performs considerably well for South Asian languages. We also check the baseline performances of the taggers for English and try to conclude how these approaches might perform if we use a considerable amount of annotated training corpus.
History (Forward N-Gram) or future (Backward N-Gram)? Which model to consider for N-Gram analysis in Bangla?
(BRAC University, 2006) Khan, Naira; Habib, Md. Tarek; Alam, Md. Jahangir; Rahman, Rajib; UzZaman, Naushad; Khan, Mumit
This paper presents a directional advantage of n-gram modeling in terms of backward or forward n-gram modeling in Bangla. The most commonly used n-gram analysis is predominantly a forward n-gram. However in Bangla it appears that a backward n-gram is repeatedly more successful and yields more grammatical results than a forward n-gram. This paper hypothesizes that the rationale behind this success is the syntactic ordering of constituents in Bangla. Bangla is a head-final specifier-initial language as opposed to English, which is head-initial specifier-initial. Hence in Bangla, the head comes after its argument in a phrase. If an n-gram analysis begins with a head and moves backwards it will stretch to its own argument but if you move for-wards then you'll probably grab the argument of an-other head. As probability of occurrence of heads is higher, probability of depending on a head is also higher and hence a backward n-gram will probably have a greater chance of yielding grammatical results. We carried out several experiments to compare different directional results in different applications with an advantage in the backward direction. This will prove a useful linguistic insight in terms of n-gram based analysis depending upon variations of constituent analysis.
N-gram based statistical grammar checker for Bangla and English
(Center for research on Bangla language processing (CRBLP), BRAC University, 2006) Alam, Md. Jahangir; UzZaman, Naushad; Khan, Mumit
This paper describes a statistical grammar checker, which considers the n-gram based analysis of words and POS tags to decide whether the sentence is grammatically correct or not. We employed this technique for both Bangla and English and also described limitation in our approach with possible solutions.
Rule based automated pronunciation generator
(BRAC University, 2006) Mosaddeque, Ayesha Binte; UzZaman, Naushad; Khan, Mumit
This paper presents a rule based ronunciation generator for Bangla words. It takes a word and finds the pronunciations for the graphemes of the word. A grapheme is a unit in writing that cannot be analyzed into smaller components. Resolving the pronunciation of a polyphone grapheme (i.e. a grapheme that generates more than one phoneme) is the major hurdle that the Automated Pronunciation Generator (APG) encounters. Bangla is partially phonetic in nature, thus we can define rules to handle most of the cases. Besides, up till now we lack a balanced corpus which could be used for a statistical pronunciation generator. As a result, for the time being a rule-based approach towards implementing the APG for Bangla turns out to be efficient.