Natural language processing 2021




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