Hindi question answering systems suffer from a lack of data. To address the same, this paper presents an approach towards automatic question generation. We present a rule-based system for question generation in Hindi by formalizing question transformation methods based on karaka-dependency theory. We use a Hindi dependency parser to mark the karaka roles and use IndoWordNet a Hindi ontology to detect the semantic category of the karaka role heads to generate the interrogatives. We analyze how one sentence can have multiple generations from the same karaka role's rule. The generations are manually annotated by multiple annotators on a semantic and syntactic scale for evaluation. Further, we constrain our generation with the help of various semantic and syntactic filters so as to improve the generation quality. Using these methods, we are able to generate diverse questions, significantly more than number of sentences fed to the system.
With limited work done for them by @cite_24 @cite_18 , question answering in Indian languages has a very poor representation. Among these languages, Hindi has been most extensively researched, yet few rule-based QA systems have been proposed @cite_14 @cite_16 . @cite_8 's system works on code mixed Hindi-English data. @cite_12 's work on a Hindi QA corpus generation was originally done in English. The Hindi version was artificially created through translation methods. These methods do not involve human annotated Hindi data.
@cite_18 @cite_14 @cite_8 @cite_24 @cite_16 @cite_12
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Code-Mixing (CM) is a natural phenomenon observed in many multilingual societies and is becoming the preferred medium of expression and communication in online and social media fora. In spite of this, current Question Answering (QA) systems do not support CM and are only designed to work with a single interaction language. This assumption makes it inconvenient for multi-lingual users to interact naturally with the QA system especially in scenarios where they do not know the right word in the target language. In this paper, we present WebShodh - an end-end web-based Factoid QA system for CM languages. We demonstrate our system with two CM language pairs: Hinglish (Matrix language: Hindi, Embedded language: English) and Tenglish (Matrix language: Telugu, Embedded language: English). Lack of language resources such as annotated corpora, POS taggers or parsers for CM languages poses a huge challenge for automated processing and analysis. In view of this resource scarcity, we only assume the existence of bi-lingual dictionaries from the matrix languages to English and use it for lexically translating the question into English. Later, we use this loosely translated question for our downstream analysis such as Answer Type(AType) prediction, answer retrieval and ranking. Evaluation of our system reveals that we achieve an MRR of 0.37 and 0.32 for Hinglish and Tenglish respectively. We hosted this system online and plan to leverage it for collecting more CM questions and answers data for further improvement. A Question Answering (QA) System is fairly an Information Retrieval(IR) system in which a query is stated to the system and it relocates the correct or closest results to the specific question asked in natural language. It is one of the consequences of Natural Language Interface to Database (NLIDB). The paper discusses the implementation of a Hindi Language QA system developed using Machine Learning approach. The implemented QA system is divided into three phases: Accessing natural language (NL) Query; where the input query is read, preprocessed and get tokenized; next is feature extraction (FE) phase; where specific features vectors are identified from the results of previous phase and finally the Classification phase; where the Naive Baye's classifier has been used, along with the knowledge base already stored in the system. This paper reflects that the concepts of similarity and classification provide better results than the use of ‘equals’ concept by defining the overall accuracy of finding the relevant answers of the specific questions asked by the user. In Contemporary world, life styles and interactions have changed in all applications domain due to increasing advances of internet technology. Due to recent advances in information explosion, tries to build an intelligent question answering system where user may communicate with a machine in natural language to get response to user question using different strategies like Natural Language Processing (NLP), Artificial Intelligence, Information Retrieval and Human Computer Interaction. Natural Language Processing is a technique where computer behave like human, which helps people to talk to the computer in their own language rather than computer commands. The skills needed to build intelligent answering system includes tokenization, parsing, parts of speech tagging, question classification, query construction, sentence understanding, document retrieval, keyword ranking, classifier, answer extraction and validation. The current study intends to develop an intelligent system for user queries in natural language for precise answer. One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.