Farm Programmes of Electronic Media: A Comparative Study of Audience Perception in Kerala

Farm communication and extension programs are vital part of the farm development attempts. Electronic media plays a major role in farm extension activities. Kerala, the consumer state, which was a complete agricultural state in pre-independence period, is the sprouting land of agricultural extension and publication activities in print media. Later AIR (All India Radio) farm programs and farm broadcasting of Doordarshan enriched the role of electronic media in farm extension activities. The media saturated southern state of India received this new electronic media farm communication revolution whole heartedly. However, after 1990, Kerala witnessed a flood of private T V channels and currently there are 24 channels in this regional language, named Malayalam. All major news and entertainment channels are broadcasting farm programs. Farm programs of AIR and Doordarshan, broadcasted in Malayalam language, have been well accepted to the farmers‘ in Kerala. However, post-independence period, witnessed the formation of Kerala state in Indian Union and the first ballot-elected communist Government started its administration. After the land reform bills, the state witnessed a gradual decrease in agricultural production. Even if it is not reflected much in the attitude and practices of farm community and farm broadcast of traditional electronic broadcasting, a change is observable after the post-liberalization era of India. Private Television channels, which were focused on entertainment value of programs, started broadcasting farm programs and the parameters of program production went through certain changes. In this situation, there is ample relevance for a study about the farm programs of electronic media in terms of a comparative study of audience perception. The study is limited in the state of Kerala as it is the most media saturated state in India. The study analyzes the rate, nature and scope of adoption of farming methods transmitted through electronic media (T.V. and Radio) in Malayalam language.All kinds of Farm programs including comprehensive program serials, success stories, seasonal cropping methods, experts opinion, been analyzed on the basis of the following objectives.  To find whether propagating new farm methods through farm programs in electronic media or the availability of adequate infrastructure and economic factors make a farmer to adopt a new farming method.  To find which electronic media has more influence on farmers to adopt agricultural programs.  To find which form of electronic media gets better feedback from farmers  To find out whether the programs of T.V. or Radio is more acceptable to farmers than the print media.  To find whether farmers gets the message through their preferred medium for the message. The researcher recorded opinions from a panel of agricultural officers, farm Information officers, agro extension researchers and experts. According to their opinions and guidelines, a pilot study is designed and conducted in Kanjikuzhy Panchayath, in Alappuzha district, Kerala. The Panchayath is selected by considering its ideal nature of being the sample for a social Science research. Besides, the nature of farming in the Panchayath, which devoid of the cultivation of cash crops also supported its sample value. As per the observations from the pilot study, researcher confirmed the Triangulation method as the methodology of research. The questionnaire survey, being the primary part contained 42 Questions with 6 independent and 32 dependent variables. The survey is conducted among 400 respondents in Idukki, Alappuzha and Pathanamthitta districts considering geographical differences and distribution of different types of crops. The response from a total of 360 respondents, 120 from each district, finally selected for tabulation and data analysis.The data analysis, based on percentage analysis, along with the results from focus group discussion among a selected group of 20 farmers, together produced the results as follows. Farmers, who are the audience of farm programs, have a very serious approach towards the medium. They are maintaining a critical point of view towards the content of the programs. Farmers are reasonably aware about the financial side of the programs and the monitory aspirations of both private and Government owned Television channels. Even though, the farmers are not aware on the technical terminology and jargons, they have ideas about success stories, program serials and they are even informed about channels are not maintaining an audience research section like AIR. Though the farmers accept Doordarshan as the credential source of farm information and methods, they are inclined to the entertainment value of programs too. They prefer to have more entertainment value for the programs of Doordarshan. Surprisingly, they have very solid suggestions on even about the shots which add entertainment value to the farm broadcasting methods of Doordarshan. Farmers are very much aware about the fact that media is just an instrument for inspiration and persuasion. They strongly believe that the source of information and new methods is agricultural research and an effective change happens only when there are adequate infrastructure and marketing facilities, along with the proper support from Government agricultural guideline and support systems like Krishi Bhavans. They strongly believe that media alone cannot create any magic in increasing agricultural production. Farmers are pointing out the lack of response to the feedback and queries of farmers on farming methods, as an evidence for the difference in levels of commitment of Government and private owned Television channels.Farmers are still perceiving AIR farm programs are far more committed to farmers and farming than any other electronic medium. However, they are seriously lacking Radio receivers with medium wave reception facility. Farmers perceive that the farming methods on new crops are more adoptable than the farming methods of traditional crops in both private and Government owned Television channels. There are multiple factors behind this observation from farmers. Farmers changed in terms of viewing habits and they prefer success stories, which are totally irrelevant and they even think that such stories encourage people to go for farming and they opined that such stories are good sources of inspiration. However, they are all very much sure about the importance and particular about the presence of entertainment factor even in farm programs. Farmers expect direct interaction of any expert of the new farming method to implement the method in their agriculture practices. Though introduction of a new idea in the T.V. is acceptable, farmers need the direct instruction of expert on field to start implementing the new farming practices Farmers still have an affinity towards print media reports and agricultural pages and they have complaints to print media on the removal of agricultural information pages from news papers. They prefer the reports in print media as it facilitates them to collect and refer articles when they need it. Farmers are having an eye of doubt about the credibility of farm programs by private T.V. channels. Even if they prefer private Television channels for listening and adopting new farming methods and other farm information, they scrutinize programs to know whether they are sponsored programs by agrochemical or agro-fertilizer manufacturer.

Development Of A Pos Tagger For Malayalam-An Experience

A Parts of Speech tagger for Malayalam which uses a stochastic approach has been proposed. The tagger makes use of word frequencies and bigram statistics from a corpus. The morphological analyzer is used to generate a tagged corpus due to the unavailability of an annotated corpus in Malayalam. Although the experiments have been performed on a very small corpus, the results have shown that the statistical approach works well with a highly agglutinative language like Malayalam

English-Malayalam Cross-Lingual Information Retrieval – an Experience

This paper describes about an English-Malayalam Cross-Lingual Information Retrieval system. The system retrieves Malayalam documents in response to query given in English or Malayalam. Thus monolingual information retrieval is also supported in this system. Malayalam is one of the most prominent regional languages of Indian subcontinent. It is spoken by more than 37 million people and is the native language of Kerala state in India. Since we neither had any full-fledged online bilingual dictionary nor any parallel corpora to build the statistical lexicon, we used a bilingual dictionary developed in house for translation. Other language specific resources like Malayalam stemmer, Malayalam morphological root analyzer etc developed in house were used in this work

Author Identification in Malayalam using n-grams

Author identification is the problem of identifying the author of an anonymous text or text whose authorship is in doubt from a given set of authors. The works by different authors are strongly distinguished by quantifiable features of the text. This paper deals with the attempts made on identifying the most likely author of a text in Malayalam from a list of authors. Malayalam is a Dravidian language with agglutinative nature and not much successful tools have been developed to extract syntactic & semantic features of texts in this language. We have done a detailed study on the various stylometric features that can be used to form an authors profile and have found that the frequencies of word collocations can be used to clearly distinguish an author in a highly inflectious language such as Malayalam. In our work we try to extract the word level and character level features present in the text for characterizing the style of an author. Our first step was towards creating a profile for each of the candidate authors whose texts were available with us, first from word n-gram frequencies and then by using variable length character n-gram frequencies. Profiles of the set of authors under consideration thus formed, was then compared with the features extracted from anonymous text, to suggest the most likely author.

An Online Character Recognition System to Convert Grantha Script to Malayalam

This paper presents a novel approach to recognize Grantha, an ancient script in South India and converting it to Malayalam, a prevalent language in South India using online character recognition mechanism. The motivation behind this work owes its credit to (i) developing a mechanism to recognize Grantha script in this modern world and (ii) affirming the strong connection among Grantha and Malayalam. A framework for the recognition of Grantha script using online character recognition is designed and implemented. The features extracted from the Grantha script comprises mainly of time-domain features based on writing direction and curvature. The recognized characters are mapped to corresponding Malayalam characters. The framework was tested on a bed of medium length manuscripts containing 9-12 sample lines and printed pages of a book titled Soundarya Lahari writtenin Grantha by Sri Adi Shankara to recognize the words and sentences. The manuscript recognition rates with the system are for Grantha as 92.11%, Old Malayalam 90.82% and for new Malayalam script 89.56%. The recognition rates of pages of the printed book are for Grantha as 96.16%, Old Malayalam script 95.22% and new Malayalam script as 92.32% respectively. These results show the efficiency of the developed system

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.

A Framework of Statistical Machine Translator from English to Malayalam

In this paper we describe the methodology and the structural design of a system that translates English into Malayalam using statistical models. A monolingual Malayalam corpus and a bilingual English/Malayalam corpus are the main resource in building this Statistical Machine Translator. Training strategy adopted has been enhanced by PoS tagging which helps to get rid of the insignificant alignments. Moreover, incorporating units like suffix separator and the stop word eliminator has proven to be effective in bringing about better training results. In the decoder, order conversion rules are applied to reduce the structural difference between the language pair. The quality of statistical outcome of the decoder is further improved by applying mending rules. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics

Handling OOV Words in Phrase-Based Statistical Machine Translation for Malayalam

Statistical Machine Translation (SMT) is one of the potential applications in the field of Natural Language Processing. The translation process in SMT is carried out by acquiring translation rules automatically from the parallel corpora. However, for many language pairs (e.g. Malayalam- English), they are available only in very limited quantities. Therefore, for these language pairs a huge portion of phrases encountered at run-time will be unknown. This paper focuses on methods for handling such out-of-vocabulary (OOV) words in Malayalam that cannot be translated to English using conventional phrase-based statistical machine translation systems. The OOV words in the source sentence are pre-processed to obtain the root word and its suffix. Different inflected forms of the OOV root are generated and a match is looked up for the word variants in the phrase translation table of the translation model. A Vocabulary filter is used to choose the best among the translations of these word variants by finding the unigram count. A match for the OOV suffix is also looked up in the phrase entries and the target translations are filtered out. Structuring of the filtered phrases is done and SMT translation model is extended by adding OOV with its new phrase translations. By the results of the manual evaluation done it is observed that amount of OOV words in the input has been reduced considerably

Extension schemes for the Alignment Model of English-Malayalam Statistical Machine Translator

In Statistical Machine Translation from English to Malayalam, an unseen English sentence is translated into its equivalent Malayalam sentence using statistical models. A parallel corpus of English-Malayalam is used in the training phase. Word to word alignments has to be set among the sentence pairs of the source and target language before subjecting them for training. This paper deals with certain techniques which can be adopted for improving the alignment model of SMT. Methods to incorporate the parts of speech information into the bilingual corpus has resulted in eliminating many of the insignificant alignments. Also identifying the name entities and cognates present in the sentence pairs has proved to be advantageous while setting up the alignments. Presence of Malayalam words with predictable translations has also contributed in reducing the insignificant alignments. Moreover, reduction of the unwanted alignments has brought in better training results. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics.

Techniques to Improve the word alignments in Statistical Machine Translation from English to Malayalam

In Statistical Machine Translation from English to Malayalam, an unseen English sentence is translated into its equivalent Malayalam translation using statistical models like translation model, language model and a decoder. A parallel corpus of English-Malayalam is used in the training phase. Word to word alignments has to be set up among the sentence pairs of the source and target language before subjecting them for training. This paper is deals with the techniques which can be adopted for improving the alignment model of SMT. Incorporating the parts of speech information into the bilingual corpus has eliminated many of the insignificant alignments. Also identifying the name entities and cognates present in the sentence pairs has proved to be advantageous while setting up the alignments. Moreover, reduction of the unwanted alignments has brought in better training results. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics

A Comparative study of HMM and SVM in Malayalam Digit Recognition

A primary medium for the human beings to communicate through language is Speech. Automatic Speech Recognition is wide spread today. Recognizing single digits is vital to a number of applications such as voice dialling of telephone numbers, automatic data entry, credit card entry, PIN (personal identification number) entry, entry of access codes for transactions, etc. In this paper we present a comparative study of SVM (Support Vector Machine) and HMM (Hidden Markov Model) to recognize and identify the digits used in Malayalam speech.

A Hybrid Architecture for Recognising Speech Signals in Malayalam

Speech is the primary, most prominent and convenient means of communication in audible language. Through speech, people can express their thoughts, feelings or perceptions by the articulation of words. Human speech is a complex signal which is non stationary in nature. It consists of immensely rich information about the words spoken, accent, attitude of the speaker, expression, intention, sex, emotion as well as style. The main objective of Automatic Speech Recognition (ASR) is to identify whatever people speak by means of computer algorithms. This enables people to communicate with a computer in a natural spoken language. Automatic recognition of speech by machines has been one of the most exciting, significant and challenging areas of research in the field of signal processing over the past five to six decades. Despite the developments and intensive research done in this area, the performance of ASR is still lower than that of speech recognition by humans and is yet to achieve a completely reliable performance level. The main objective of this thesis is to develop an efficient speech recognition system for recognising speaker independent isolated words in Malayalam.

An English-Malayalam dictionary /

Title also in Malayalam.

Experiences with an Object-Oriented, Multi-Stage Language

Metaphor is a multi-stage programming language extension to an imperative, object-oriented language in the style of C# or Java. This paper discusses some issues we faced when applying multi-stage language design concepts to an imperative base language and run-time environment. The issues range from dealing with pervasive references and open code to garbage collection and implementing cross-stage persistence.