Implementation of a Finite State Automaton to Recognize and Remove Stop Words in English Text on its Retrieval

Behera, Sudersan

doi:10.1109/icoei.2018.8553828

Cited by 13 publications

(3 citation statements)

References 11 publications

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“…For identifying part-of-speech tagging, name entities and morphological analysis rules-based techniques, Google directory and hidden markov model were developed [11]- [15]. For identifying and removing stop words from text a latent semantic indexing (LSI), SVM-based approach and deterministic finite automata (DFA) were developed [16]- [18]. For solving the issue of statement formation of systematic question Template-based approach proposed.…”

Section: Issn: 2252-8938mentioning

confidence: 99%

Text similarity algorithms to determine Indian penal code sections for offence report

Srivastav

Prajapat

2022

IJ-AI

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<p>Taking decisions by comparing two text documents is a new innovative idea. Text documents contain details, rules and information related to a domain. The judiciary system is an area where many textual documents are available. In some documents, rules related to the judiciary are mentioned, such as the Indian penal code (IPC) section documents and other documents like first information report (FIR), and Investigation report. contain details of incidents. Our assumption is that the system can help in making the decision by finding the right IPC Section from the result of text similarity between IPC section document and FIR, investigation report. In this research paper, we preface a new research problem to make decisions to suggest appropriate IPC Section for crime related information from user’s input by using vector space model and natural language processing techniques.</p>

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Section: Issn: 2252-8938mentioning

confidence: 99%

Text similarity algorithms to determine Indian penal code sections for offence report

Srivastav

Prajapat

2022

IJ-AI

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“…Stop words was removed from the dataset, because we need to focus on the actual data that defines the actual meaning of the text. Finite state automaton to consider and replace terms removing stop words [15]. Data arrays are divided into training and test data.…”

Section: Data Processingmentioning

confidence: 99%

Natural language processing based advanced method of unnecessary video detection

Moon

Salehin

Parvin

et al. 2021

IJECE

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<span>In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.</span>

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“…There are several methods for removing stop words, which have been presented by the authors of [25]. Some papers [26] have also proposed time efficient methods. The method we used in this paper is based on a previously compiled list of words.…”

Section: Sentence Retrieval With Language Model (Lm)mentioning

confidence: 99%

Improving Sentence Retrieval Using Sequence Similarity

2020

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Sentence retrieval is an information retrieval technique that aims to find sentences corresponding to an information need. It is used for tasks like question answering (QA) or novelty detection. Since it is similar to document retrieval but with a smaller unit of retrieval, methods for document retrieval are also used for sentence retrieval like term frequency—inverse document frequency (TF-IDF), BM 25 , and language modeling-based methods. The effect of partial matching of words to sentence retrieval is an issue that has not been analyzed. We think that there is a substantial potential for the improvement of sentence retrieval methods if we consider this approach. We adapted TF-ISF, BM 25 , and language modeling-based methods to test the partial matching of terms through combining sentence retrieval with sequence similarity, which allows matching of words that are similar but not identical. All tests were conducted using data from the novelty tracks of the Text Retrieval Conference (TREC). The scope of this paper was to find out if such approach is generally beneficial to sentence retrieval. However, we did not examine in depth how partial matching helps or hinders the finding of relevant sentences.

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Implementation of a Finite State Automaton to Recognize and Remove Stop Words in English Text on its Retrieval

Cited by 13 publications

References 11 publications

Text similarity algorithms to determine Indian penal code sections for offence report

Text similarity algorithms to determine Indian penal code sections for offence report

Natural language processing based advanced method of unnecessary video detection

Improving Sentence Retrieval Using Sequence Similarity

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