The ultimate aim of this research is to develop a Rule Based Machine Translation System (RBMT) using sentence simplification. The sentence pattern for English is SVO and Tamil is SOV. Complex and larger sentence are not easy to parse and translate. So, the sentence simplifier is also accommodated in the rule based system to split a large sentence into simple multiple sentences. Machine translation is the process of translating sentence from one language to another language. Here, English is the source language for the translation system and Tamil is the target language. During the translation process, the system need to learn and get trained by linguistic rules. These rules are classified into two types, namely reordering rules and morphological rules. A bilingual dictionary has been created to support this automatic translation. Linguistic information act as a backbone for the proposed system. To evaluate the performance of the system, we have experimented by testing 250 sentence patterns and we got the overall accuracy about 0.7186. Module wise human evaluation has been done to understand the issues in each modules.

The word ontology refers to the hierarchical structure of entities and their relationships. The entities are nodes and each node is dominated by their parent node in the hierarchical structure. The nodes are related by semantic and lexical relations such as synonymy, homonymy, meronymy, antonymy, etc. Hierarchical structure is created for different types of semantic domains. The top domains are entities, events, abstracts and relationals and the hierarchical structure is converted into a visual representation using tree viewer. This paper describes in detail about the building of ontological structure for Tamil vocabulary and how the ontological structure is converted in to a visual knowledge representation and user friendly retrieval system. All types of lexical and semantic relationship between lexical items are captured by using unique notations. The overall system is ontology based intelligent system for information retrieval and along with the ontology implementation, thesaurus based on ontology called as Onto-thesaurus also described in this paper. Onto-thesaurus tool plays effective role in information retrieval, researching and education platform, especially for teaching, training and learning purpose.

Social media text holds information regarding various important aspects. Extraction of such information serves as the basis for the most preliminary task in Natural Language Processing called Entity extraction. The work is submitted as a part of Shared task on Code Mix Entity Extraction for Indian Languages(CMEE-IL) at Forum for Information Retrieval Evaluation (FIRE) 2016. Three different methodology is proposed in this paper for the task of entity extraction for code-mix data. Proposed systems include approaches based on the Embedding models and feature based model. Creation of trigram embedding and BIO tag formatting were done during feature extraction. Evaluation of the system is carried out using machine learning based classifier, SVM-Light. Overall accuracy through cross validation has proven that the proposed system is efficient in classifying unknown tokens too

This work is submitted to Consumer Health Information Search (CHIS) Shared Task in Forum for Information Retrieval Evaluation (FIRE) 2016. Information retrieval from any part of web should include informative content relevant to the search of web user. Hence the major task is to retrieve only relevant documents according to the users query. The given task includes further refinement of the classification process into three categories of relevance such as support, oppose and neutral. Any user reading an article from web must know whether the content of that article supports or opposes title of the article. This seems to be a big challenge to the system. Our proposed system is developed based on the combination of Keyword based features and Word embedding based features. Classification of sentences is done by machine learning based classifier, Support Vector Machine (SVM).

Entity Recognition is an essential part of Information Extraction, where explicitly available information and relations are extracted from the entities within the text. Plethora of information is available in social media in the form of text and due to its nature of free style representation, it introduces much complexity while mining information out of it. This complexity is enhanced more by representing the text in more than one language and the usage of transliterated words. In this work we utilized sequential modeling algorithm with hybrid features to perform the Entity Recognition on the corpus given by CMEE-IL (Code Mixed Entity Extraction - Indian Language) organizers. The experimented approach performed great on both the Tamil-English and Hindi-English tweet corpus by attaining nearly 95% against the training corpus and 45.17%, 31.44% against the testing corpus.

This paper explains the overview of the shared task "Detecting Paraphrases in Indian Languages" (DPIL) conducted at FIRE 2016. Given a pair of sentences in the same language, participants are asked to detect the semantic equivalence between the sentences. The shared task is proposed for four Indian languages namely Tamil, Malayalam, Hindi and Punjabi. The dataset created for the shared task has been made available online and it is the first open-source paraphrase detection corpora for Indian languages.

Question classification is a key task in many question answering applications. Nearly all previous work on question classification has used machine learning and knowledge-based methods. This working note presents an embedding based Bag-of-Words method and Recurrent Neural Network to achieve an automatic question classification in the code-mixed Bengali-English text. We build two systems that classify questions mostly at the sentence level. We used a recurrent neural network for extracting features from the questions and Logistic regression for classification. We conduct experiments on Mixed Script Information Retrieval (MSIR) Task 1 dataset at FIRE20161. The experimental result shows that the proposed method is appropriate for the question classification task.

Machine Translation is one of the major oldest and the most active research area in Natural Language Processing. Currently, Statistical Machine Translation (SMT) dominates the Machine Translation research. Statistical Machine Translation is an approach to Machine Translation which uses models to learn translation patterns directly from data, and generalize them to translate a new unseen text. The SMT approach is largely language independent, i.e. the models can be applied to any language pair. Statistical Machine Translation (SMT) attempts to generate translations using statistical methods based on bilingual text corpora. Where such corpora are available, excellent results can be attained translating similar texts, but such corpora are still not available for many language pairs. Statistical Machine Translation systems, in general, have difficulty in handling the morphology on the source or the target side especially for morphologically rich languages. Errors in morphology or syntax in the target language can have severe consequences on meaning of the sentence. They change the grammatical function of words or the understanding of the sentence through the incorrect tense information in verb. Baseline SMT also known as Phrase Based Statistical Machine Translation (PBSMT) system does not use any linguistic information and it only operates on surface word form. Recent researches shown that adding linguistic information helps to improve the accuracy of the translation with less amount of bilingual corpora. Adding linguistic information can be done using the Factored Statistical Machine Translation system through pre-processing steps. This paper investigates about how English side pre-processing is used to improve the accuracy of English-Tamil SMT system.