IRJET - Analysis of Paraphrase Detection using NLP Techniques

Jan 13, 20210 likes23 views

This document discusses analyzing paraphrase detection using natural language processing (NLP) techniques. It proposes applying a multi-head attention mechanism in a Siamese deep neural network to detect semantic similarity between texts and determine if they are paraphrases. The system would tokenize, stem, remove stopwords and part-of-speech tag input texts before applying the neural network. It evaluates the approach on datasets like SNLI and QQP and compares performance to existing methods.

IRJET - Analysis of Paraphrase Detection using NLP Techniques

The document describes a study that used BERT (Bidirectional Encoder Representations from Transformers), a pretrained language model, for extractive text summarization. The researchers developed a two-phase encoder-decoder model where BERT encoded sentences from documents and classified them as included or not included in the summary. They evaluated the model on the CNN/Daily Mail corpus and found it achieved state-of-the-art results comparable to previous models based on both automatic metrics and human evaluation.

EXTRACTIVE SUMMARIZATION WITH VERY DEEP PRETRAINED LANGUAGE MODELijaia

The document presents a new model for extractive text summarization that uses BERT (Bidirectional Encoder Representations from Transformers), a pretrained deep bidirectional transformer model, as the text encoder. The model consists of a BERT encoder and a sentence classifier. Sentences are encoded using BERT and classified as to whether they should be included in the summary. Evaluation on the CNN/Daily Mail corpus shows the model achieves state-of-the-art results comparable to other top models according to automatic metrics and human evaluation, making it the first work to apply BERT to text summarization.

Detection of multiword from a wordnet is complexeSAT Publishing House

IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology

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This document discusses sentiment analysis and summarizes several papers on related topics. It begins with an abstract describing sentiment analysis and its importance. The introduction defines sentiment classification and analysis. The literature survey section summarizes 5 papers on natural language processing and machine learning algorithms for sentiment analysis, including K-means clustering, bag-of-words models, TF-IDF vectorization for document clustering, hierarchical clustering methods, and using naive bayes and SVM for sentiment analysis and text summarization. The conclusion discusses techniques for data processing, natural language processing, and machine learning algorithms covered.

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The document describes a study that uses GloVe word embeddings to measure semantic similarity between short texts. GloVe is an unsupervised learning algorithm for obtaining vector representations of words. The study trains GloVe word embeddings on a large corpus, then uses the embeddings to encode short texts and calculate their semantic similarity, comparing the accuracy to methods that use Word2Vec embeddings. It aims to show that GloVe embeddings may provide better performance for short text semantic similarity tasks.

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This document summarizes a paper that proposes a supervised machine learning approach for detecting hate speech in social media. It begins with an introduction to the problem of hate speech online and anonymity enabling harmful communication. It then describes common text preprocessing techniques like tokenization and filtering used to clean text data. Feature extraction methods are discussed, including n-grams, bag-of-words, and word embeddings. Popular machine learning algorithms for classification are also summarized, such as support vector machines, logistic regression, and neural networks. The document concludes by reviewing related work on hate speech detection and challenges around dataset annotation.

BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...ijnlc

This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.

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The document describes a sentiment analysis algorithm that classifies law enforcement tweets as positive or negative. It uses a lexicon-based approach with sentiment composition rules to determine the polarity of each tweet. The algorithm was evaluated on a dataset of manually annotated law enforcement tweets, achieving an F-score of 75.6% for positive and negative classification. Sentiment composition rules are applied to identify the polarity of noun phrases, verb phrases, and phrases combined with prepositions or the conjunction "but". The overall polarity of each tweet is determined by calculating a positivity to negativity ratio.

Modeling of Speech Synthesis of Standard Arabic Using an Expert Systemcsandit

This document describes an expert system for speech synthesis of Standard Arabic text. It involves two main stages: 1) creation of a sound database and 2) text-to-speech transformation. The transformation process involves phonetic orthographic transcription of the text and then generating voice signals corresponding to the transcribed phonetic sequence. The expert system uses a knowledge base containing sound data and rewriting rules. It transcribes text using graphemes as basic units and then concatenates sound units from the database to synthesize speech. Tests achieved a 96% success rate in pronouncing sentences correctly. Future work aims to improve prosody and develop fully automatic signal segmentation.

Sentiment Analysis In Myanmar Language Using Convolutional Lstm Neural Networkkevig

In recent years, there has been an increasing use of social media among people in Myanmar and writing review on social media pages about the product, movie, and trip are also popular among people. Moreover, most of the people are going to find the review pages about the product they want to buy before deciding whether they should buy it or not. Extracting and receiving useful reviews over interesting products is very important and time consuming for people. Sentiment analysis is one of the important processes for extracting useful reviews of the products. In this paper, the Convolutional LSTM neural network architecture is proposed to analyse the sentiment classification of cosmetic reviews written in Myanmar Language. The paper also intends to build the cosmetic reviews dataset for deep learning and sentiment lexicon in Myanmar Language.

[Paper Reading] Supervised Learning of Universal Sentence Representations fro...Hiroki Shimanaka

This document summarizes the paper "Supervised Learning of Universal Sentence Representations from Natural Language Inference Data". It discusses how the researchers trained sentence embeddings using supervised data from the Stanford Natural Language Inference dataset. They tested several sentence encoder architectures and found that a BiLSTM network with max pooling produced the best performing universal sentence representations, outperforming prior unsupervised methods on 12 transfer tasks. The sentence representations learned from the natural language inference data consistently achieved state-of-the-art performance across multiple downstream tasks.

DOMAIN BASED CHUNKINGkevig

Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.

COMPREHENSIVE ANALYSIS OF NATURAL LANGUAGE PROCESSING TECHNIQUEJournal For Research

Natural Language Processing (NLP) techniques are one of the most used techniques in the field of computer applications. It has become one of the vast and advanced techniques. Language is the means of communication or interaction among humans and in present scenario when everything is dependent on machine or everything is computerized, communication between computer and human has become a necessity. To fulfill this necessity NLP has been emerged as the means of interaction which narrows the gap between machines (computers) and humans. It was evolved from the study of linguistics which was passed through the Turing test to check the similarity between data but it was limited to small set of data. Later on various algorithms were developed along with the concept of AI (Artificial Intelligence) for the successful execution of NLP. In this paper, the main emphasis is on the different techniques of NLP which have been developed till now, their applications and the comparison of all those techniques on different parameters.

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ONTOLOGICAL TREE GENERATION FOR ENHANCED INFORMATION RETRIEVALijaia

This document proposes a methodology to extract information from big data sources like course handouts and directories and represent it in a graphical, ontological tree format. Keywords are extracted from documents using natural language processing techniques and used to generate a hierarchical tree based on the DMOZ open directory project. The trees provide a comprehensive overview of document content and structure. The method is implemented using Python for natural language processing and Java for visualization. Evaluation on computer science course handouts shows the trees accurately represent topic coverage and depth. Future work aims to increase the number of keywords extracted.

L1803058388IOSR Journals

This document summarizes a research paper that proposes and compares fuzzy and Naive Bayes models for detecting obfuscated plagiarism in Marathi language texts. It first provides background on plagiarism detection and describes different types of plagiarism, including obfuscated plagiarism. It then presents the fuzzy semantic similarity model, which uses fuzzy logic rules and semantic relatedness between words to calculate similarity scores between texts. Next, it describes the Naive Bayes model for plagiarism detection using Bayes' theorem. The paper compares the performance of the fuzzy and Naive Bayes models on precision, recall, F-measure and granularity. It finds that the Naive Bayes model provides more accurate detection of obfuscated plagiar

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Suggestion Generation for Specific Erroneous Part in a Sentence using Deep Le...ijtsrd

This document presents a method for generating suggestions for specific erroneous parts of sentences in Indian languages like Malayalam using deep learning. The method uses recurrent neural networks with long short-term memory layers to train a model on input-output examples of sentences and their corrections. The model takes in preprocessed sentence data and generates a set of possible corrections for erroneous parts through multiple network layers. An analysis of the model shows that it can accurately generate suggestions for word length of three, but requires more data and study to handle the complex morphology and symbols of Malayalam. The performance of the method is limited by the hardware used and it could be improved with a more powerful system and additional training data.

IRJET- Survey on Generating Suggestions for Erroneous Part in a SentenceIRJET Journal

This document discusses using deep learning approaches like long short-term memory (LSTM) neural networks to generate suggestions for erroneous parts of sentences in Indian languages. Indian languages pose unique challenges due to their morphological richness and structure differences from English. The document reviews natural language processing techniques like recurrent neural networks, convolutional neural networks, and LSTMs. It proposes using LSTMs to model sentence structure and generate possible corrections for errors in an unsupervised manner. The goal is to develop this technique for morphologically complex Indian languages like Malayalam.

IRJET-Semantic Similarity Between SentencesIRJET Journal

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1. The document discusses the development of an intelligent system to optimize the English language using natural language processing techniques. The system will perform functions like summarization, spell check, grammar check, and sentence auto-completion. 2. It describes the various algorithms used for each function, including extracting important sentences for summarization, comparing words to dictionaries for spell check, analyzing syntax for grammar check, and completing sentences based on previous user data for auto-completion. 3. The system aims to build a smart tool that can correct errors and summarize text in English to improve communication through optimized language.

Improving Neural Abstractive Text Summarization with Prior KnowledgeGaetano Rossiello, PhD

Abstractive text summarization is a complex task whose goal is to generate a concise version of a text without necessarily reusing the sentences from the original source, but still preserving the meaning and the key contents. In this position paper we address this issue by modeling the problem as a sequence to sequence learning and exploiting Recurrent Neural Networks (RNN). Moreover, we discuss the idea of combining RNNs and probabilistic models in a unified way in order to incorporate prior knowledge, such as linguistic features. We believe that this approach can obtain better performance than the state-of-the-art models for generating well-formed summaries.

Performance analysis on secured data method in natural language steganographyjournalBEEI

The rapid amount of exchange information that causes the expansion of the internet during the last decade has motivated that a research in this field. Recently, steganography approaches have received an unexpected attention. Hence, the aim of this paper is to review different performance metric; covering the decoding, decrypting and extracting performance metric. The process of data decoding interprets the received hidden message into a code word. As such, data encryption is the best way to provide a secure communication. Decrypting take an encrypted text and converting it back into an original text. Data extracting is a process which is the reverse of the data embedding process. The effectiveness evaluation is mainly determined by the performance metric aspect. The intention of researchers is to improve performance metric characteristics. The evaluation success is mainly determined by the performance analysis aspect. The objective of this paper is to present a review on the study of steganography in natural language based on the criteria of the performance analysis. The findings review will clarify the preferred performance metric aspects used. This review is hoped to help future research in evaluating the performance analysis of natural language in general and the proposed secured data revealed on natural language steganography in specific.

Analysis of Opinionated Text for Opinion Miningmlaij

In sentiment analysis, the polarities of the opinions expressed on an object/feature are determined to assess the sentiment of a sentence or document whether it is positive/negative/neutral. Naturally, the object/feature is a noun representation which refers to a product or a component of a product, let’s say, the "lens" in a camera and opinions emanating on it are captured in adjectives, verbs, adverbs and noun words themselves. Apart from such words, other meta-information and diverse effective features are also going to play an important role in influencing the sentiment polarity and contribute significantly to the performance of the system. In this paper, some of the associated information/meta-data are explored and investigated in the sentiment text. Based on the analysis results presented here, there is scope for further assessment and utilization of the meta-information as features in text categorization, ranking text document, identification of spam documents and polarity classification problems.

Supervised Approach to Extract Sentiments from Unstructured TextInternational Journal of Engineering Inventions www.ijeijournal.com

IRJET- Factoid Question and Answering SystemIRJET Journal

This document describes a factoid question answering system that uses neural networks and the Tensorflow framework. The system takes in a text document and question as input. It then processes the input using techniques like gated recurrent units and support vector machines to classify the question. The system calculates attention between facts and the question, modifies its memory, and identifies the word closest to the answer to output as the response. Key aspects of the system include training a question answering engine with Tensorflow, storing and retrieving data, and generating the final answer.

Sentiment Analysis: A comparative study of Deep Learning and Machine LearningIRJET Journal

This document compares sentiment analysis techniques using deep learning and machine learning. It summarizes previous work using various machine learning algorithms and deep learning methods for sentiment analysis. The document then outlines the approach taken in this study, which is to determine the best sentiment analysis results using either machine learning or deep learning techniques. It describes preprocessing the Rotten Tomatoes movie review dataset and creating text matrices before selecting models for classification. The goal is to get a generalized understanding of how sentiment analysis can be performed and which practices yield optimal results.

Language and Offensive Word DetectionIRJET Journal

The document presents a study on language recognition and offensive word detection. It discusses developing models using machine learning algorithms like logistic regression, TF-IDF, and SVM to identify the language of text inputs consisting of 44 languages, and detect offensive English and Hindi words. The models are trained on large datasets containing language samples and offensive terms. The system architecture involves data collection, training classifiers, and using techniques like n-grams and ensemble modeling for language identification and offensive word detection. Evaluation shows logistic regression achieving 99.2% accuracy for language recognition. The study aims to build automated tools to analyze multilingual texts and detect inappropriate content.

IRJET - Threat Prediction using Speech AnalysisIRJET Journal

This document describes a proposed system to analyze audio files and detect potential threats by performing speech recognition and sentiment analysis. The system would have three main steps: 1) Convert speech to text using machine learning algorithms like recurrent neural networks, 2) Perform sentiment analysis on the text using natural language processing techniques like naïve Bayes classification to determine if the text is positive, negative, or neutral, 3) Calculate an overall threat percentage and issue a warning if it exceeds a threshold. The goal is to automate audio surveillance and analysis to reduce time and human error compared to manual processes. Artificial neural networks would be used for both speech recognition and sentiment classification.

Contextual Emotion Recognition Using Transformer-Based ModelsIRJET Journal

This document discusses developing a context-aware emotion recognition system using transformer models like BERT. The proposed system aims to improve on existing emotion identification techniques, which often fail to consider context. It analyzes a broad, emotion-labeled dataset to assess the effectiveness of a context-aware algorithm compared to conventional methods and standard transformer models. The findings demonstrate improved accuracy in identifying emotions from text when incorporating contextual information using transformer models.

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Analysis of reinforced concrete deep beam is based on simplified approximate method due to the complexity of the exact analysis. The complexity is due to a number of parameters affecting its response. To evaluate some of this parameters, finite element study of the structural behavior of the reinforced self-compacting concrete deep beam was carried out using Abaqus finite element modeling tool. The model was validated against experimental data from the literature. The parametric effects of varied concrete compressive strength, vertical web reinforcement ratio and horizontal web reinforcement ratio on the beam were tested on eight (8) different specimens under four points loads. The results of the validation work showed good agreement with the experimental studies. The parametric study revealed that the concrete compressive strength most significantly influenced the specimens’ response with the average of 41.1% and 49 % increment in the diagonal cracking and ultimate load respectively due to doubling of concrete compressive strength. Although the increase in horizontal web reinforcement ratio from 0.31 % to 0.63 % lead to average of 6.24 % increment on the diagonal cracking load, it does not influence the ultimate strength and the load-deflection response of the beams. Similar variation in vertical web reinforcement ratio leads to an average of 2.4 % and 15 % increment in cracking and ultimate load respectively with no appreciable effect on the load-deflection response.

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