Building a foundation of HPSG-based treebank on Bangla language

Includes bibliographical references (page 6).Now a day, the importance of a large annotated corpus for NLP researchers is widely known. In this paper, we describe an initial phase of developing a linguistically annotated corpus for non-configurational ‘Bangla’ language. Since, the formalism differs from those posited for configurational languages; several features have been added for constraint based parsing through HPSG-based formalism. We propose an outline of a semi-automated process by applying both case marking approach and some morphological analysis to constraint the parsing of a relatively free word order language for creating a linguistically rich, highly-lexicalized annotated corpus

Construction of Large Scale Isolated Word Speech Corpus in Bangla

A new speech corpus of isolated words in Bangla language has been recorded including high frequent words from a text corpus BdNC01 It has been specifically designed for various research activities related to speaker-independent Bangla speech recognition The database consists of speech of 100 speakers each of them speaking 1081 words Another 50 new speakers were employed to speak all the list of speech to construct a test database Every utterance was repeated 5 times in different days to avoid time variation of speaker property The total 400 hours of recording makes the corpora largest in its type size and language domain This paper describes the motivation for the corpora and the processes undertaken in its construction The paper concludes with the usability of the corpu

Comparion of different POS tagging technique (N-Gram, HMM and Brill's tagger) for Bangla

Includes bibliographical references (page 6-7).There are different approaches to the problem of assigning each word of a text with a parts-of-speech tag, which is known as Part-Of-Speech (POS) tagging. In this paper we compare the performance of a few POS tagging techniques for Bangla language, e.g. statistical approach (n-gram, HMM) and transformation based approach (Brill’s tagger). A supervised POS tagging approach requires a large amount of annotated training corpus to tag properly. At this initial stage of POS-tagging for Bangla, we have very limited resource of annotated corpus. We tried to see which technique maximizes the performance with this limited resource. We also checked the performance for English and tried to conclude how these techniques might perform if we can manage a substantial amount of annotated corpus.Naushad UzZamanFahim Muhammad HasanMumit Kha

Part of Speech Tagging of Marathi Text Using Trigram Method

In this paper we present a Marathi part of speech tagger. It is a morphologically rich language. It is spoken by the native people of Maharashtra. The general approach used for development of tagger is statistical using trigram Method. The main concept of trigram is to explore the most likely POS for a token based on given information of previous two tags by calculating probabilities to determine which is the best sequence of a tag. In this paper we show the development of the tagger. Moreover we have also shown the evaluation done

Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi

In this paper we discuss an in-progress work on the development of a speech corpus for four low-resource Indo-Aryan languages -- Awadhi, Bhojpuri, Braj and Magahi using the field methods of linguistic data collection. The total size of the corpus currently stands at approximately 18 hours (approx. 4-5 hours each language) and it is transcribed and annotated with grammatical information such as part-of-speech tags, morphological features and Universal dependency relationships. We discuss our methodology for data collection in these languages, most of which was done in the middle of the COVID-19 pandemic, with one of the aims being to generate some additional income for low-income groups speaking these languages. In the paper, we also discuss the results of the baseline experiments for automatic speech recognition system in these languages.Comment: Speech for Social Good Workshop, 2022, Interspeech 202

Comparison of different POS tagging techniques for some South Asian languages

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2006.Cataloged from PDF version of thesis report.Includes bibliographical references (page 47).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.Fahim Muhammad HasanB. Computer Science and Engineerin