natural language processing 2019


Natural language processing is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data.

Proceedings of the Natural Legal Language Processing Workshop 2019
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the Association for Computational Linguistics. Many industries have embraced natural language processing (NLP) approaches, which have altered healthcare, finance, education and other fields. The legal domain however

Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
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This workshop series builds on a successful string of iterations, with dozens of interdisciplinary submissions to make NLP techniques and insights standard practice in CSS research. Our focus is on NLP for social sciences-to continue the progress of CSS, and to

Neural transfer learning for natural language processing
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Language is often regarded as the hallmark of human intelligence. Developing systems that can understand human language is thus one of the main obstacles on the quest towards artificial general intelligence. This objective has driven research in artificial intelligence and

Natural language processing for mobile app privacy compliance
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Many Internet services collect a flurry of data from their users. Privacy policies are intended to describe the services privacy practices. However, due to their length and complexity, reading privacy policies is a challenge for end users, government regulators, and

Natural Language Processing based Hybrid Model for Detecting Fake News Using Content-Based Features and Social Features
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Internet acts as the best medium for proliferation and diffusion of fake news. Information quality on the internet is a very important issue, but web-scale data hinders the experts ability to correct much of the inaccurate content or fake content present over these platforms

Transfer learning in natural language processing
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The classic supervised machine learning paradigm is based on learning in isolation, a single predictive model for a task using a single dataset. This approach requires a large number of training examples and performs best for well-defined and narrow tasks. Transfer

of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing
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Welcome to EMNLP-IJCNLP 2019 in Hong Kong! I hope that this will be a successful conference in the 25-year tradition of EMNLP and IJCNLP, as well as an enjoyable and enriching experience for all. Continuing recent trends, this year has seen overwhelming

Proceedings of the 2nd Clinical Natural Language Processing Workshop
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This volume contains papers from the 2nd Workshop on Clinical Natural Language Processing (ClinicalNLP), held at NAACL 2019. Clinical text offers unique challenges that differentiate it not only from open-domain data, but from other types of text in the biomedical

A multi-level ransomware detection framework using natural language processing and machine learning
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Ransomware attacks in recent years have proved expensive due to significant damages and obstructions these caused in various sectors such as health, insurance, business, and education. Several malware detection methods have been proposed to uncover different

Natural Language Processing with Process Models (NLP4RE Report Paper)
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This paper is a report paper that focuses on research at the intersection of business process management and requirements engineering. It gives an overview of the research on natural language processing with process models organized in terms of 25 challenges. This

Gluoncv and gluonnlp: Deep learning in computer vision and natural language processing
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Abstract We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate

Insights from Natural Language Processing
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We present an exemplary text categorization pipeline for online citizen participation and aim at discussing our ongoing research in terms of insights from natural language processing (NLP) application. For each of the steps in the categorization pipeline, we share our

Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
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Welcome to TextGraphs, the Workshop on Graph-Based Methods for Natural Language Processing . The thirteenth edition of our workshop is being organized on November 201 in conjunction with the 2019 Conference on Empirical Methods in Natural Language

Using time series and natural language processing to identify viral moments in the 2016 US Presidential Debate
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This paper proposes a method for identifying and studying viral moments or highlights during a political debate. Using a combined strategy of time series analysis and domain adapted word embeddings, this study provides an in-depth analysis of several key moments Purpose The highly structured nature of medical reports makes them feasible for automated large-scale patient identification. This study aimed to develop a natural language processing (NLP) model to retrospectively retrieve patients with presence and history of carotid stenosis

Natural Language Processing for Government: Problems and Potential
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Natural Language Processing (NLP) is a broad umbrella of technologies used for computa- tionally studying large amounts of text and extracting meaning both syntactic and semantic information. Software using NLP technologies, if engineered for that purpose, generally have

Voice call analytics using natural language processing
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The main challenge of call centres based on voice call is the quality monitoring, where a person has to listen to all the recorded audio files or a random sample of audio files, to check how the call centre representative has performed. It also helps in recording the

On the surprising capacity of linear combinations of embeddings for natural language processing
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As Websters classic 1900 text English: Composition and Literature states A sentence is a group of words expressing a complete thought. People use natural language to represent thoughts. Thus the representation of natural language, in turn, is of fundamental importance

Natural language processing
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1. Obtain a distributional vector nj for each noun nj in the lexicon. 2. Collect adjective noun pairs (ai, nj) from the corpus. 3. Obtain a distributional vector pij of each pair (ai, nj) from the same corpus using a conventional DSM. 4. The set of tuples 1 (nj, pij) lj represents a dataset

Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing
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The Nordic Language Processing Laboratory (NLPL; http://nlpl. eu) is a collaboration of university research groups in Natural Language Processing (NLP) in Northern Europe. Our vision is to implement a virtual laboratory for large-scale NLP research by (a) creating new


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