natural language processing IEEE PAPER 2022
Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data
Using Natural Language Processing Techniques to Improve Manual Test Case Descriptions
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Manual test cases are often described in natural language of techniques such as Natural Language Processing (NLP) cases specified in natural language and provide actionable
Using Natural Language Processing for Centralising Research Project Calls
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There are a multitude of research project calls scattered across a variety of websites-some are easier to find than others or just very difficult to parse for the right information. The task for
Natural Language Processing RANLP 2017
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Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP Proceedings of the International Conference Recent Advances
Data Statements: Documenting the datasets used for training and testing natural language processing systems
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A guide for writing data statements for natural language processing . https://techpolicylab.uw. Data statements for natural language processing : Toward mitigating system bias and
Survey of Machine Learning Algorithms Used in Natural Language Processing and Understanding Tasks
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of Natural Language Processing (NLP). Today, machine learning is present everywhere around us, from your e-commerce platform predicting what you would purchase based on your
Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing -A Structured
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And since reasoning is inextricably linked to language it makes intuitive sense that Natural Language Processing (NLP), would be a particularly well-suited candidate for NeSy. We
Learning analytics of MOOCs based on natural language processing
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We demonstrate that even the usage of the simple tool developed based on python natural language processing libraries gives an understanding of the advantages and disadvantages
Darmok and Jalad on the Internet: the importance of metaphors in natural languages and natural language processing
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translator are issues for today s natural language processing as well. Metaphors are often a significant challenge in language processing and automated translation. Such bottlenecks
Trends in Co-mention of Stimulants and Opioids: A Natural Language Processing Driven Analysis of Reddit Forums
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We apply natural language processing methods to improve the detection of true mentions of opioids and stimulants, and to exclude false positives (eg, negated concepts and
Fake News Detection and Classification using Natural Language Processing
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We can all agree that social media is a double-edged it has many advantages and also has many disadvantages. Everything begins with a piece of information be it correct or incorrect,
A hybrid solution for extracting information from unstructured data using optical character recognition (OCR) with natural language processing (NLP).
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we use spark engines to read these unstructured data to securely store and transform them to a structured format, which will be our inputs to the natural language processing (NLP).
Modeling Phishing Decisions using Instance Based Learning and Natural Language Processing
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the effectiveness of using established natural language processing methods, such as LSA, NLP methods that capture semantic meanings in natural language not be effective at
Reducing Racial and Gender Bias in Machine Learning and Natural Language Processing Tasks Using a GAN Approach
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IS Mandis terra-docs.s3.us-east-2.amazonaws To prove the generalizability of the GAN, the GAN is used to debias a Natural Language Processing (NLP) task: a word vector association task trained on 000 random Wikipedia
AN INNOVATIVE ARTIFICIAL INTELLIGENCE AND NATURAL LANGUAGE PROCESSING FRAMEWORK FOR ASSET PRICE FORECASTING BASED ON
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We present an innovative framework for integrating a nonlinear autoregressive network with a natural language processing approach to analyze stock price movements and forecasting.
Government plans in the andPeruvian presidential elections: A natural language processing analysis of the health chapters
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Over the last few years, researchers working in clinical medicine have adopted artificial intelligence and deep learning techniques such as Natural Language Processing (NLP). Due toinstrumentation of machine learning and natural language processing would give profound of Waikato) and the natural language processing through Orange data mining toolkit (from
Extending self-supervised natural language models through human brain
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ural Language Processing (NLP). Using machine learning methods and freshly proposed the performance of brain data on downstream natural language processing tasks. Our aim is to
NLI-GSC: A Natural Language Interface for Generating SourceCode
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[16] AT Imam and AJ Alnsour, The use of natural language processing approach for converting pseudo code to c# code, Journal of Intelligent Systems, vol. 2 no. pp.
Stylete: Styling the Web with Natural Language
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Figure 4: Our computational pipeline integrates a natural language processing (NLP) module at inference time processing user s input of natural language and clicks to generate a set
CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations
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structured data from natural language supervision in form of to learn from natural language explanations can generalize tasks paired with natural language explanations. Over the last
Using Human Intelligence to Test the Impact of Popular Preprocessing Steps and Feature Extraction in the Analysis of Human Language
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Perhaps this is an indication that it is time to reconsider our current mathematical approaches to feature-extraction natural language processing and instead use computational power to
Research Title Improving Multi-task deep neural network architecture for natural language understanding usage
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All in all, we could say, today despite lots of proposed different deep learning-based model, these issues (Text classification, Natural language processing Natural language
Natural Language Understanding
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become quite standard throughout natural language processing . Algorithms for parsing, part-of-speech tagging, reference resolution, and discourse processing all began to incorporate
AUTOMATIC SENTENCE PARSER FOR AMHARIC LANGUAGE USING SVM
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Natural language processing has an important role in our daily life, by enabling computers Now a day parsing underlies most of the applications in natural language processing . A
Logical Structure-based Pretrained Models for Legal Text Processing
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in natural language processing coming from pretrained models based on the Transformer architecture. In the field of legal text processing a although the natural language is used for
SoundexGR: An Algorithm for Phonetic Matching for the Greek Language
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and Natural Language Processing tasks. Although there is a wide variety of choices for tackling this issue in the English language this is not the case for other languages. For the Greek
WarMemoirSampo: A semantic portal for war veteran interview videos
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This is carried out by processing natural language texts in collaboration between Natural Language Processing and is carried out using Natural Language Processing (NLP) techniques
Layer-Wise Multi-View Decoding for Improved Natural Language Generation
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It is also considered by in natural language processing but with opposite results, ie, performance degradation. The reason is that a direct transfer of such pattern is not viable for
VAST: The Valence-Assessing Semantics Test for Contextualizing Language Models
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conference on computational natural language learning, art natural language processing . In Proceedings of the Conference on Empirical Methods in Natural Language Processing
Discourse Processing WS 21/22
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Empirical Methods in Natural Language Processing Lisbon, Natural Language Processing with Python Analyzing Text quality assessment in natural language . In Proceedings of the
Opinion Mining of Gujarati Language Text Using Hybrid Approach
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Natural Language Processing because of the growing electronic data in last two decades. The field has been very well explored for the English language ; however, Gujarati language is
Surveying the BioNLP Shared Task Series
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Shared tasks are increasingly common in Natural Language Processing and new become more popular in Natural Language Processing (NLP), speech and image processing . In the
Improving Coreference Resolution with Word Formation
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In natural language processing (NLP), coreference ing in natural language and as such has received attention for long. Given the unprecedented success of contextualized languageinformation be efficiently represented is important for many natural language applications, in particular, for smoothing language models. Furthermore, analysis of words as they are In addition, many machine learning and natural language processing models have been utilized for data management, especially for data integration tasks. We thus hope to bringprocessing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing pre- processing feature extractions
Ontology Formation and Comparison for Syllabus Structure Using NLP
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This is done with the extraction of the syllabus, creation of ontology for the representing syllabus, then parsing the ontology and applying Natural language processing to remove
A Machine Learning based Study on Classical Arabic Authorship Identification
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Due to its very peculiars linguistic properties, it constitutes a difficult challenge to some natural language processing applications such as authorship identification, especially in its
A Neural Language Model with Attention Mechanism Based on Convolutional Neural Network
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technology and natural language processing (NLP) has rapidly increased the demands for on the natural language that involves plenty of ambiguities not present in formal language . Abstract Topic modeling refers to a range of algorithms in natural language processing that gives us an insight into the latent semantic topics or patterns in a collection of documents. The culmination of many converging technologies such as Natural Language Processing (NLP), Virtual Reality (VR), Augmented Reality (AR) and Artificial Intelligence (AI) has led to
Robust Lottery Tickets for Pre-trained Language Models
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Large-scale pre-trained language models (PLMs), such as BERT (Devlin et al. 2019) have achieved great success in the field of natural language processing . As more transformer
AraBERT transformer model for Arabic comments and reviews analysis
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This reminder of this paper is structured as: in section we present the most important techniques and approaches used in the natural language processing (NLP) field to deal with the
Drug Sentiment Analysis using Machine Learning Classifiers
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dimensions of natural language processing is sentiment analysis which refers to analyzing to have low performance when applied to natural language processing . On the contrary, the
Twitter Sentiment Analysis during the Lockdown on New Zealand
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Natural language processing (NLP), which is a form of Artificial intelligent was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon
A Literature Survey of Recent Advances in Chatbots. Information 202 1 41
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Therefore, we decided to discard these kinds of articles because our aim was to survey Deep Learning algorithms and Natural Language Processing techniques used in the latest
A Review of Machine Learning Algorithms for Text Classification
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Text classification is a basic task in the field of natural language processing . It is a process is the basic technology of natural language processing and social network analyzing, which The advancements like natural language processing are utilized with the help of GTTS, Google API to change over from text to speech and the reverse way around. Here, the whole
Sentiment Analysis Technique for Text Analytics in Scientific Literatures Domain: A Review
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s emotions, attitude or view from either speech, text in the web contents like tweets using natural language processing (NPL). They further referred SA as an opinion mining (OP) byrole in natural language application such as cross language many of the natural language processing tasks like machine named entity and term processing corpus alignment,
FORCE: A Framework of Rule-Based Conversational Recommender System
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Act into the bot response in natural language form based on configured templates 2. The Natural Language Processing and the 9th International Joint Conference on Natural Language
THE ACCURACY COMPARISON AMONG WORD2VEC, GLOVE, AND FASTTEXT TOWARDS CONVOLUTION NEURAL NETWORK (CNN) TEXT
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Extraction of features in the area of Text Processing or Natural Language Processing (NLP) presents unique difficulties owing to the unstructured nature of text. Thus, feature extraction
DOCUMENT CLASSIFICATION SYSTEM FOR THE SPANISH LANGUAGE
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The classification of documents is one of the main tasks that has been carried out as a result of the development and study of Natural Language Processing and Computational
OneRel: Joint Entity and Relation Extraction with One Module in One Step
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Extracting pairs of entities and their relations in the form of (head, relation, tail) or (h, r, t) from unstructured text is an important task in natural language processing and knowledge graph
Finding and Fixing Undesirable Behaviors in Pretrained Language Models
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the problem of misalignment in the context of Natural Language Processing (NLP). In particular, we focus on a popular class of NLP models: Pretrained Language Models (PLMs).
Digital Service in Brazilian Large Collections: Proposed Mobile Solution Integrated to Chatbots applied to University Collections
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Natural Language Processing ); the implementation of the web structure with the support of a chatbot to provide guidance for the user; the demonstration of experiments (and challenges
Comparative Analysis on Machine Learning Techniques: A case study on Amazon Product
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Sentiment analysis is the analysis of customer s opinion using natural language processing . or opinion mining play a vital role in text mining using natural language processing .
SAS: Self-Augmentation Strategy for Language Model Pre-training
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In recent years, language model pre-training has achieved enormous success in various natural language processing (NLP) downstream tasks, largely due to innovations in pretraining
A Bot Assisted Instructional Framework for Teaching Introductory Programming Course (s)
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A natural language processing (NLP) based conversational agent was designed and of predefined rules aided with natural language processing algorithms was chosen. There
A LANGUAGE FOR LEGAL DISCOURSE IS ALL YOU NEED
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target language for natural language processing . natural language interface, such as Robert Kowalski s Logical English . Another alternative is to experiment with a large language
Natural language processing IEEE PAPER 2021
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IEEE PAPERS ON NATURAL LANGUAGE PROCESING
Attention in natural language processing
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Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced advances in this domain, a systematic overview of attention is still
Natural language processing for requirements engineering: The best is yet to come
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As part of the growing interest in natural language processing for requirements engineering (RE), RE researchers, computational linguists, and industry practitioners met at the First Workshop on Natural Language Processing for Requirements Engineering (NLP4RE 18)
Experience report: Log mining using natural language processing and application to anomaly detection
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Event logging is a key source of information on a system state. Reading logs provides insights on its activity, assess its correct state and allows to diagnose problems. However, reading does not scale: with the number of machines increasingly rising, and the
Feature extraction and analysis of natural language processing for deep learning English language
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NLP ( Natural Language Processing ) is a technology that enables computers to understand human languages. Deep-level grammatical and semantic analysis usually uses words as the basic unit, and word segmentation is usually the primary task of NLP. In order to solve
Arabic natural language processing and machine learning-based systems
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Arabic natural language processing (ANLP) consists of developing techniques and tools that can utilize and analyze the Arabic language in both written and spoken contexts. ANLP makes an important contribution to many existing developed systems. It provides Arabic and
Recent trends in deep learning based natural language processing
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Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural
A survey of the usages of deep learning for natural language processing
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Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This article provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It This paper underlines the necessity to incorporate Deep learning and Neural networking in language models under scrutiny for Natural Language Processing . The paper describes various statistical models proposed and the limitations incurred in the same due to limited
Automated extraction of semantic legal metadata using natural language processing
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[Context] Semantic legal metadata provides information that helps with understanding and interpreting the meaning of legal provisions. Such metadata is important for the systematic analysis of legal requirements.[Objectives] Our work is motivated by two observations:(1)
Review on Natural Language Processing (NLP) and its toolkits for opinion mining and sentiment analysis
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As the majority of online networking on the Internet, opinion mining has turned into a fundamental way to deal with investigating such huge numbers of information. Different applications show up in an extensive variety of modern areas. In the interim, opinions have
Large-scale point-of-interest category prediction using natural language processing models
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Point-of-Interest (POI) recommendation is an important application in Location-based Social Networks (LBSN). The category prediction problem is to predict the next POI category that users visit. The predicted category information is critical in large-scale POI
Natural language processing methods for acoustic and landmark event-based features in speech-based depression detection
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The processing of speech as an explicit sequence of events is common in automatic speech recognition (linguistic events), but has received relatively little attention in paralinguistic speech classification despite its potential for characterizing broad acoustic event sequences
On application of natural language processing in machine translation
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Natural language processing is the core of machine translation. In the history, its development process is almost the same as machine translation, and the two complement each other. This article compares the natural language processing of statistical corpora with
A natural language processing framework for assessing hospital readmissions for patients with COPD
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With the passage of recent federal legislation, many medical institutions are now responsible for reaching target hospital readmission rates. Chronic diseases account for many hospital readmissions and chronic obstructive pulmonary disease has been recently
Automating articulation: Applying natural language processing to post-secondary credit transfer
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Within the field of post-secondary student mobility, the assessment, and evaluation of transfer credit is a labor-intensive human intelligence task that is subject to time limits and human bias. This paper introduces a semi-automated approach to assessing transfer credit
Inflectional review of deep learning on natural language processing
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In the age of knowledge, Natural Language Processing (NLP) express its demand by a huge range of utilization. Previously NLP was dealing with statically data. Contemporary time NLP is doing considerably with the corpus, lexicon database, pattern reorganization. Considering
A review of natural language processing and machine learning tools used to analyze arabic social media
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Arabic Language is spoken widely in the world. It has very special characteristics that made it hard to be handled by computers. Recently, Social Media is considered as one of the richest source for knowledge sharing and information gathering in the internet. Arabic
Matching real-world facilities to building information modeling data using natural language processing
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Building Information Modeling (BIM) is a promising technology for building informatics. Currently, an increasing number of applications adopt BIM to improve the building operations and facility management. In these applications, matching real-world facilities to
Predicting decisions of the philippine supreme court using natural language processing and machine learning
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For the past decades, Philippine courts have been experiencing severe court congestion and case backlog problems. This study aims to provide a solution to alleviate these problems by predicting the outcome of court cases. As the Philippine Supreme Court case
LexNLP: Natural language processing and information extraction for legal and regulatory texts
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Over the last two decades, many high-quality, open-source packages for natural language processing and machine learning have been released. Researchers and developers can quickly write applications in languages such as Java, Python, and R that stand on theCustomer satisfaction is very important for public service providers, customer satisfaction can be delivered with a survey application or writing criticism that can be used to evaluate and improve service. Unfortunately, there are only a few customers who are willing to give
TRUNAJOD: A text complexity library to enhance natural language processing
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We present TRUNAJOD, a text complexity analysis tool that includes a wide variety of linguistics measurements that can be extracted from texts as an approximation for readability, coherence, and cohesion. The features that TRUNAJOD can extract from the text
Exploring the Possibilities of Applying Transfer Learning Methods for Natural Language Processing in Software Development
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Nowadays, we have a growing number of mature applications in the field of natural language processing (NLP), especially natural language understanding (NLU) and generation (NLG), like chatbots or auto-generated reports. Such applications relieve users
Development of a Natural Language Processing based Deep Learning Model for Automated HS Code Classification of the Imported Goods
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The Korean Customs Law requires the owner to directly classify items on imported goods and pay customs duties at the tax rate of the declared HS code. However, the rapid changes in the industrial environment, the expansion of trade, and the emergence of new
Extraction and Analysis of Stories: Natural Language Processing for Software Engineering.
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ABSTRACT GUO, HUI. Extraction and Analysis of Stories: Natural Language Processing for Software Engineering.(Under the direction of Dr. Munindar P. Singh.) Modern software systems are deployed in sociotechnical settings, combining social entities (humans and
Proceedings of the First Workshop on Bridging Human Computer Interaction and Natural Language Processing
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The rapid advancement of natural language processing (NLP) research has led to a variety of applications spanning a wide range of domains. As NLP applications are being used by more people in their everyday lives, and are increasingly powered by data generated by
A Principled Approach to Enriching Security-related Data for Running Processes through Statistics and Natural Language Processing
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We propose a principled method of enriching security related information for running processes. Our methodology applies to large organizational infrastructures, where information is properly collected and stored. The data we use is based on the Hubble Stack
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
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TextFlint is a multilingual robustness evaluation toolkit for NLP tasks that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis. This enables
Comparing Natural Language Processing Methods for Text Classification of Small Educational Data
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Over the past decade, new natural language processing techniques have been developed that show promise when applied to educational data. Although these methods can be effective, little work has been done to measure the comparative strength of these methods
RuThes Thesaurus for Natural Language Processing
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In natural language processing (NLP) and information retrieval (IR), it is often useful to utilize various types of knowledge, including lexical knowledge about relations between words, their senses, domain-specific knowledge, and commonsense knowledge. The conventional
How You Say It Matters: Text Analysis of FOMC Statements Using Natural Language Processing
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Taeyoung Doh is a senior economist at the Federal Reserve Bank of Kansas City. Sungil Kim is a research associate at the bank. Shu-Kuei Yang is a software engineer at the bank. This article is on the banks website at www.KansasCityFed.org The Federal Reserve has increasingly
Secure Computational Resource Sharing System Using Natural Language Processing
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Nowadays, cloud computing is used in every industry, and the growth of cloud computing has increased exponentially since the release of Amazon Web Services (AWS). It enables the user to develop or create a software or product without investing upfront in the hardware
Parametrized Quantum Circuits of Synonymous Sentences in Quantum Natural Language Processing
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In this paper we develop a compositional vector-based semantics of positive transitive sentences in quantum natural language processing for a non-English language ie Persian, to compare the parametrised quantum circuits of two synonymous sentences in two
Sinhala Language Plagiarism Tool with Internet Resources Using Natural Language Processing
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With the digitalization of text through the World Wide Web, plagiarism turned into a crucial problem. Hence, a way to detect plagiarism became a very essential thing. Even though there are many plagiarism detection systems, applying in the world by considering other
Visualizing NLP in Undergraduate Students Learning about Natural Language
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Keywords: natural language processing linguistics, un- dergraduate students, non-majors, educational visualization and visual summarization, interpretability, inclusivity AllenNLP: A Deep Semantic Natural Language Processing Platform
of theConference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
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The Industry Track at NAACL HLTrepresents innovations and implementations in speech and natural language processing technologies and systems that are relevant to industrial applications. The primary focus of this
Clustering Natural Language Test Case Instructions as Input for Deriving Automotive Testing DSLs
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Both be applicable for instructions in automotive test case specifications. Furthermore, typical natural language processing techniques for comparing texts are Word2Vec (Mikolov et al. 2013), Doc2Vec (Le Mikolov 2014), or TF-IDF (Rajaraman Ull- man 2011) Other than giving access to the technical literature and state of the art in the field of natural language processing to non-specialist readers, the paper seeks to reveal the conceptual and philosophi- cal stakes involved in the recent application of new neural network techniques to
Natural Language Understanding
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The current state of research in natural language understanding finds its foundations in such diverse fields as linguistics (computational linguistics), computer science ( natural language processing ), electrical engineering (speech recognition) and psychology (computational Abstract: Clinical parsing is useful in medical domain .Clinical narratives are difficult to understand as it is in unstructured format .Medical Natural language processing systems are used to make these clinical narratives in readable format
Deep Learning for the Russian Language
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org/10.1007/978-3-030-42855-6_26 CHAPTER 26 Deep Learning for the Russian Language Ekaterina Artemova 26.1 INTRODUCTION Deep learning has conquered the natural language processing (NLP) research area in the mid-2010s
Why Do Masked Neural Language Models Still Need Semantic Knowledge in Question Answering
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Page 4. Page 5. Abstract Pre-trained language models have widely been used to solve various natural language processing tasks MNLM Masked Neural Language Model. 4 1 1 1 18 NLP Natural Language Processing . 18 QA Question Answering In particular, the sentiment analysis of short texts such as single sentences and social media tweets and comments are one of the main chal- lenges of natural language processing (NLP) given the limited contextual information they usually convey
Bangla language textual image description by hybrid neural network model
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the substances of the picture as well as the communications among the substances [1-5]. Image description is essentially the language based textual description of an image, which has been an active field of research in computer vision and natural language processing [6-13]
Simple Ways to Improve NER in Every Language using Markup.
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issues: the processing of uppercase tokens, the detection of entity boundaries and low generalization. Specifically, we first explore the marking of uppercase tokens for providing extra casing information. We then randomly mask tokens, as in a masked language model, and Hindi to English: Transformer-Based Neural Machine Translation Kavit Gangar, Hardik Ruparel, and Shreyas Lele Abstract Machine Translation (MT) is one of the most prominent tasks in Natural Language Processing (NLP) which involves the automatic conversion of texts from Computer Security Based Question Answering System with IR and Google BERT Pragya Agrawal, Priyanka Askani, Ranjitha Nayak, and HR Mamatha Abstract Machine learning perception and question answering is a fundamental errand in natural language processing
The finsim shared task: Learning semantic representations for the financial domain
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The FinSim shared task, colocated with the FinNLP workshop, offered the challenge to automatically learn effective and precise semantic models for the financial domain. Going beyond the mere representation of words is a key step to industrial applications that make
SPEECH PROCESSING Electronic textbook
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teachers, scientists and all those who independently wish to study methods of computer processing of the Kazakh language based on their morphological analysis and synthesis, the current state of the problem of natural language processing theoretical foundations of word
Case study: How Well Can IBMs Requirements Quality Assistant Review Automotive Requirements
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aRobert Bosch GmbH, Postfach 300240, 70442 Stuttgart, Germany Abstract In the last years natural language processing evolved immensely Keywords case study, requirements analysis, natural language processing Requirements Quality Assistant 1. Introduction
A Comparative Analysis for English and Ukrainian Texts Processing Based on Semantics and Syntax Approach
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Keywords 1 Natural language processing word order, Ukrainian language NLP, English language noun group, verb group, direct word order, grammatical category, base form, structural scheme, information resource processing natural language text, terminal chain Natural Language Processing system is used for unlock information from the EHR (electronic heath records) Natural language processing with the combination of PCFG (probabilistic context free grammar) gives accurate result. Clinical narrative is in unstructured formfor neutral (anti-ISIS) group it needs further investigation. Keywords: Radicalization; extremism; machine learning; natural language processing ; twitter; text mining 1 Introduction Internet and social media networks play a
CS 230: Film Success Prediction Using NLP Techniques
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Abstract While Natural Language Processing has been applied to the film industry already and with some success Deep Learning techniques have not previously been used to study the predictive capabilities of movie scripts on revenue and reviews, due to the difficulty of
Risk Minimization for Zero-shot Sequence Labeling
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Zero-shot sequence labeling aims to build a sequence labeler without human-annotated datasets. One straightforward approach is utilizing existing systems (source models) to generate pseudo-labeled datasets and train a target sequence labeler accordingly
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision
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In structured prediction problems, crosslingual transfer learning is an efficient way to train quality models for low-resource languages, and further improvement can be obtained by learning from multiple source languages. However, not all source models are created equal
Mitigating political bias in language models through reinforced calibration
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1 Introduction Large-scale language models (LMs) can generate human- like text and have shown promise in many Natural Lan- guage Generation (NLG) applications such as dialogue gen- eration (Zhang et al. 2020; Peng et al. 2020) and machine translation (Yang et al
Exploring chord embedding spaces between musical composers and eras
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Abstract In recent years, natural language processing al- gorithms have become more sophisticated and powerful due to the development of deep neural networks Foun- dations of Statistical Natural Language Processing . Cambridge, Massachusetts: The MIT Press
Building interpretable interaction trees for deep nlp models
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Abstract This paper proposes a method to disentangle and quantify in- teractions among words that are encoded inside a DNN for natural language processing . We construct a tree to encode salient interactions extracted by the DNN