Nazreena RahmanIndian Institute of Technology Guwahati | IIT Guwahati · Department of Electronics and Electrical Engineering (EEE)
Nazreena Rahman
IPDF Ph.D. M.Tech. B.Tech
Looking for new opportunities in a challenging environment in AI/ML to utilize my skills.
About
14
Publications
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Introduction
A professional with a Ph.D degree in Computer Science & Engineering, I am passionate and self-motivated person with 9 years of teaching and research experiences. I am now interested in sentence based visual question answering where I can apply deep learning (AI/ML) methods efficiently. I have experiences in the research if Natural Language Processing (NLP). During my Ph.D. work, I proposed a method for Query-Based Extractive Text Summarization Using Sense-Oriented Semantic Relatedness Measure
Publications
Publications (14)
This paper presents a query-based extractive text summarization approach by using sense-oriented semantic relatedness measure. To find the query relevant sentences, we have to find semantic relatedness measure between query and input text sentences. To find the relatedness score, we need to know the exact sense of the words present in query and inp...
This paper presents a query-based extractive text summarization method by using sense-oriented semantic relatedness measure. We have proposed a Word Sense Disambiguation (WSD) technique to find the exact sense of a word present in the sentence. It helps in extracting query relevance sentences while calculating the sense-oriented sentence semantic r...
Word sense disambiguation (WSD) finds the actual meaning of a word according to its context. This paper presents a novel WSD method to find the correct sense of a word present in a sentence. The proposed method uses both the WordNet lexical dictionary and the Wikipedia corpus. Initially, we find all the probable senses of the target word using Word...
Finding semantic relatedness score between two sentences is useful in many research areas. Existing relatedness methods do not consider its sense while computing semantic relatedness score between two sentences. In this study, a Word Sense Disambiguation (WSD) method is proposed which is used in finding the sense-oriented sentence semantic relatedn...
In this paper, a query-based text summarization method is proposed based on common sense knowledge and word sense disambiguation. Common sense knowledge is integrated here by expanding the query terms. It helps in extracting main sentences from text document according to the query. Query-based text summarization finds semantic relatedness score bet...
Finding and correcting incorrect spelling from text documents always plays an important role particularly in information retrieval. There are many approaches to tackle spell checking problem. Here, one spell checking and correcting method is proposed particularly for query-based text summarization purpose. Our method mainly works on non-word errors...
Finding and correcting incorrect spelling from text documents always plays an important role particularly in information retrieval. There are many approaches to tackle spell checking problem. Here, one spell checking and correcting method is proposed particularly for query-based text summarization purpose. Our method mainly works on non-word errors...
In this paper, a semantic relatedness based query focused text summarization technique is introduced to find relevant information from single text document. This semantic relatedness measure extracts the related sentences according to the query. The query focused text summarization approach can work on short query when the query does not contain en...
Contextual spell correction is very important for real word error correction. It gives the correct word for an incorrect word in a particular sentence. The traditional spell checker can correct those misspelled words which are not present in dictionary but here we try to develop a spell checker which can give appropriate word on the basis of the co...
With continuous and rapid growth of web information, the internet has become the important source of textual information. But it is very much difficult to find out the relevant information from the vast source. Therefore, query expansion can be considered as an important method to get the required information correctly. In fact, query expansion pla...
Condensation of document information from the text according to the query is primarily a concerned matter due to the rapid growth of information. In fact, there is not usually enough time to scan through the contents and understand each document and make decision based on the queries. Hence, there is a great demand for query based summarization of...
The Healthcare industry is the most significant amongst the information intensive industries. Medical
information, knowledge and data keep growing on a daily basis. Therefore, huge amounts of data
generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. This leads to the use of data...
Questions
Questions (4)
I am doing Ph.D in Text Summarzation, In that regards, I want to run your NeuralSum (https://github.com/cheng6076/NeuralSum) code in Python3.7 using anaconda (Jupyter-Notebook). I have installed all required packages like numy, nltk, scipy, panda, tensorflow, keras, scikit-learn etc. But I am little bit confused where to place the data file in "NeuralSum" folder and how to update the location of the folder in preptrain, train and evaluate.py files?
I am doing research on text summarization. Can anyone help me out.
I am doing research on Text Summarization. I want to know the difference between query-answeing and query-based summarization. Please help me and try to clear my doubt.
