Department of Computer Science and Information Systems

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Author: search.filters.author.Rohil, Mukesh KumarSubject: search.filters.subject.Natural Language Processing

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    An Interactive System leveraging Automatic Speech Recognition and Machine Translation for learning Hindi as a Second Language
    (IEEE, 2022) Rohil, Mukesh Kumar
    When English speakers are in the early stages of learning Hindi, formulating sentences in Hindi is often attempted by a verbatim translation of English words to corresponding Hindi words. Due to this reason, they are unable to learn Hindi sentences correctly. We have tried to overcome this problem by use of technology for second language learners. The use of Automatic Speech Recognition, and Machine Translation for second language learning, here learning Hindi by English speaker, has been illustrated by taking English speech as input and translating the given English sentences and words into Hindi and then displaying its equivalent construct in Devanagari script. The interactive system under study displays and speaks the same. It has been observed that a second language can be learnt faster by frequently listening to the vocabulary and sentences of the language. Thus the system furnishes the functionality of speaking the sentence in Hindi once it is represented in Devanagari script. The English sentences and words from the grammar tool books are given as input to the system for experimentation. We have observed that the critical problem encountered while doing so is the translation of English to Hindi. Another problem encountered at times is insertion error for letters (only surfaced). The system cannot translate sentences represented using continuous tense and perfect continuous tense correctly. The overall accuracy of the system, otherwise, is approximately 67% which can help the second language learners in the beginning.
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    An exploratory study of automatic text summarization in biomedical and healthcare domain
    (Elsevier, 2022-11) Rohil, Mukesh Kumar
    In the last two decades, the uses of automatic text summarization have been realized in a wide range of applications in various fields cutting across a number of verticals. Amongst these, one of the most inquired is the domain of healthcare and medicine. Many of the studies have revealed that the use of automatic text summarization in the biomedical and healthcare domain helps researchers and medical professionals save their time and access more information in considerably short spans of time. This article reports some of the recent studies that enumerate the benefits and limitations of the uses of automatic text summarization in the biomedical and healthcare domain. In addition, the paper also explores certain new possible applications of automatic text summarization in the biomedical and healthcare domain. Furthermore, it discusses the trends and vision towards future opportunities for possible research in automatic text summarization in the context of medical and healthcare domain.
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    Natural Language Interfaces to Domain Specific Knowledge Bases: An Illustration for Querying Elements of the Periodic Table
    (IEEE, 2018) Rohil, Mukesh Kumar
    The task of providing Natural Language Interface (NLI) to any domain specific knowledge base is much demanding despite (potentially) favorable factors like low volume of vocabulary, unambiguous and precise meaning of words, less number of relations among the entities, etc. The simplification of this task has been proposed and presented in this research. The authors have made a successful effort to develop an NLI system to answer the user's simple queries (in English) about the properties of chemical elements and their grouping in the Periodic Table. Adding to the ease, the user is not required to know anything about the structure of the knowledge base of the elements, since the software is implemented (using Logic Programming constructs) in Prolog wherein program and data are treated indistinguishably. Firstly, the system accepts a query and subsequently, it can analyze and understand the query, if the query contains all words within the domain specific vocabulary. Finally, it efficiently searches the knowledge base to answer the query, by reducing search space using artificial intelligence techniques (like symbolic manipulation). If the query is not understood by the system, it reports to the user the words not available in the knowledge base and the particular relations among the entities which could not be set. The knowledge base (~150 KB) contains the properties of chemical elements, their arrangement in Periodic Table and the inter-relationships among these properties. In a nutshell, the research suggests that to develop an NLI to a domain specific knowledge base, it is better to develop a parser capable of handling the entities and their interrelationships as understood in the domain; hence, only little is to be coded for the various grammars, languages, transition networks, etc