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Artículos de revistas sobre el tema "Natural language processing analysis"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 16 de febrero de 2022

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1

Yilmaz, A. Egemen. "Natural Language Processing". International Journal of Systems and Service-Oriented Engineering 4, n.º 1 (enero de 2014): 68–83. http://dx.doi.org/10.4018/ijssoe.2014010105.

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Requirement analysis is the very first and crucial step in the software development processes. On the other hand, as previously addressed by other researchers, it is the Achilles' heel of the whole process since the requirements lie on the problem space, whereas other software artifacts are on the solution space. Stating the requirements in a clear manner eases the following steps in the process as well as reducing the number of potential errors. In this paper, techniques for the improvement of the requirements expressed in the natural language are revisited. These techniques try to check the requirement quality attributes via lexical and syntactic analysis methods sometimes with generic, and sometimes domain and application specific knowledge bases.

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2

Cohen, Shay. "Bayesian Analysis in Natural Language Processing". Synthesis Lectures on Human Language Technologies 9, n.º 2 (9 de junio de 2016): 1–274. http://dx.doi.org/10.2200/s00719ed1v01y201605hlt035.

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3

Duh, Kevin. "Bayesian Analysis in Natural Language Processing". Computational Linguistics 44, n.º 1 (marzo de 2018): 187–89. http://dx.doi.org/10.1162/coli_r_00310.

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4

Radev, Dragomir R. y Rada Mihalcea. "Networks and Natural Language Processing". AI Magazine 29, n.º 3 (5 de septiembre de 2008): 16. http://dx.doi.org/10.1609/aimag.v29i3.2160.

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Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.

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5

Belov, Serey, Daria Zrelova, Petr Zrelov y Vladimir Korenkov. "Overview of methods for automatic natural language text processing". System Analysis in Science and Education, n.º 3 (2020) (30 de septiembre de 2020): 8–22. http://dx.doi.org/10.37005/2071-9612-2020-3-8-22.

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This paper provides a brief overview of modern methods and approaches used for automatic processing of text information. In English-language literature, this area of science is called NLP-Natural Language Processing. The very name suggests that the subject of analysis (and for many tasks – and synthesis) are materials presented in one of the natural languages (and for a number of tasks – in several languages simultaneously), i.e. national languages of communication between people. Programming languages are not included in this group. In Russian-language literature, this area is called Computer (or mathematical) linguistics. NLP (computational linguistics) usually includes speech analysis along with text analysis, but in this review speech analysis does not consider. The review used materials from original works, monographs, and a number of articles published the «Open Systems.DBMS» journal.

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6

Fagan, Frank. "Natural Language Processing for Lawyers and Judges". Michigan Law Review, n.º 119.6 (2021): 1399. http://dx.doi.org/10.36644/mlr.119.6.natural.

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7

Jäppinen, H., T. Honkela, H. Hyötyniemi y A. Lehtola. "A Multilevel Natural Language Processing Model". Nordic Journal of Linguistics 11, n.º 1-2 (junio de 1988): 69–87. http://dx.doi.org/10.1017/s033258650000175x.

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In this paper we describe a multilevel model for natural language processing. The distinct computational strata are motivated by invariant linguistic properties which are progressively uncovered from utterances. We examine each level in detail. The processes are morphological analysis, dependency parsing, logico-semantic analysis and query adaptation. Both linguistic and computational aspects are discussed. In addition to theory, we consider certain engineering viewpoints important and discuss them briefly.

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8

Iyer, Hari, Mihir Gandhi y Sindhu Nair. "Sentiment Analysis for Visuals using Natural Language Processing". International Journal of Computer Applications 128, n.º 6 (15 de octubre de 2015): 31–35. http://dx.doi.org/10.5120/ijca2015906581.

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9

Cohen, Shay. "Bayesian Analysis in Natural Language Processing, Second Edition". Synthesis Lectures on Human Language Technologies 12, n.º 1 (8 de abril de 2019): 1–343. http://dx.doi.org/10.2200/s00905ed2v01y201903hlt041.

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10

Korycinski, C. y Alan F. Newell. "Natural-language processing and automatic indexing". Indexer: The International Journal of Indexing: Volume 17, Issue 1 17, n.º 1 (1 de abril de 1990): 21–29. http://dx.doi.org/10.3828/indexer.1990.17.1.8.

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The task of producing satisfactory indexes by automatic means has been tackled on two fronts: by statistical analysis of text and by attempting content analysis of the text in much the same way as a human indexcr does. Though statistical techniques have a lot to offer for free-text database systems, neither method has had much success with back-of-the-bopk indexing. This review examines some problems associated with the application of natural-language processing techniques to book texts.

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11

Bera, Abhijit, Mrinal Kanti Ghose y Dibyendu Kumar Pal. "Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)". International Journal of System Dynamics Applications 10, n.º 4 (octubre de 2021): 1–12. http://dx.doi.org/10.4018/ijsda.20211001.oa16.

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Multilingual Sentiment analysis plays an important role in a country like India with many languages as the style of expression varies in different languages. The Indian people speak in total 22 different languages and with the help of Google Indic keyboard people can express their sentiments i.e reviews about anything in the social media in their native language from individual smart phones. It has been found that machine learning approach has overcome the limitations of other approaches. In this paper, a detailed study has been carried out based on Natural Language Processing (NLP) using Simple Neural Network (SNN) ,Convolutional Neural Network(CNN), and Long Short Term Memory (LSTM)Neural Network followed by another amalgamated model adding a CNN layer on top of the LSTM without worrying about versatility of multilingualism. Around 4000 samples of reviews in English, Hindi and in Bengali languages are considered to generate outputs for the above models and analyzed. The experimental results on these realistic reviews are found to be effective for further research work.

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12

Verma, Vivek Kumar, Mrigank Pandey, Tarun Jain y Pradeep Kumar Tiwari. "Dissecting word embeddings and language models in natural language processing". Journal of Discrete Mathematical Sciences and Cryptography 24, n.º 5 (4 de julio de 2021): 1509–15. http://dx.doi.org/10.1080/09720529.2021.1968108.

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13

Kuroda, Tomohiro, Kazuya Okamoto, Tadamasa Takemura, Naoki Oboshi, Yoshihiro Kuroda y Osamu Oshiro. "Analysis of Japanese-Japanese Sign Language Dictionary Using Natural Language Processing". Japanese Journal of Sign Language Studies 17 (2008): 85–92. http://dx.doi.org/10.7877/jasl.17.85.

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14

Zhao, Liping, Waad Alhoshan, Alessio Ferrari, Keletso J. Letsholo, Muideen A. Ajagbe, Erol-Valeriu Chioasca y Riza T. Batista-Navarro. "Natural Language Processing for Requirements Engineering". ACM Computing Surveys 54, n.º 3 (junio de 2021): 1–41. http://dx.doi.org/10.1145/3444689.

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Natural Language Processing for Requirements Engineering (NLP4RE) is an area of research and development that seeks to apply natural language processing (NLP) techniques, tools, and resources to the requirements engineering (RE) process, to support human analysts to carry out various linguistic analysis tasks on textual requirements documents, such as detecting language issues, identifying key domain concepts, and establishing requirements traceability links. This article reports on a mapping study that surveys the landscape of NLP4RE research to provide a holistic understanding of the field. Following the guidance of systematic review, the mapping study is directed by five research questions, cutting across five aspects of NLP4RE research, concerning the state of the literature, the state of empirical research, the research focus, the state of tool development, and the usage of NLP technologies. Our main results are as follows: (i) we identify a total of 404 primary studies relevant to NLP4RE, which were published over the past 36 years and from 170 different venues; (ii) most of these studies (67.08%) are solution proposals, assessed by a laboratory experiment or an example application, while only a small percentage (7%) are assessed in industrial settings; (iii) a large proportion of the studies (42.70%) focus on the requirements analysis phase, with quality defect detection as their central task and requirements specification as their commonly processed document type; (iv) 130 NLP4RE tools (i.e., RE specific NLP tools) are extracted from these studies, but only 17 of them (13.08%) are available for download; (v) 231 different NLP technologies are also identified, comprising 140 NLP techniques, 66 NLP tools, and 25 NLP resources, but most of them—particularly those novel NLP techniques and specialized tools—are used infrequently; by contrast, commonly used NLP technologies are traditional analysis techniques (e.g., POS tagging and tokenization), general-purpose tools (e.g., Stanford CoreNLP and GATE) and generic language lexicons (WordNet and British National Corpus). The mapping study not only provides a collection of the literature in NLP4RE but also, more importantly, establishes a structure to frame the existing literature through categorization, synthesis and conceptualization of the main theoretical concepts and relationships that encompass both RE and NLP aspects. Our work thus produces a conceptual framework of NLP4RE. The framework is used to identify research gaps and directions, highlight technology transfer needs, and encourage more synergies between the RE community, the NLP one, and the software and systems practitioners. Our results can be used as a starting point to frame future studies according to a well-defined terminology and can be expanded as new technologies and novel solutions emerge.

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15

Bachate, Ravindra Parshuram y Ashok Sharma. "Acquaintance with Natural Language Processing for Building Smart Society". E3S Web of Conferences 170 (2020): 02006. http://dx.doi.org/10.1051/e3sconf/202017002006.

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Natural Language Processing (NLP) deals with the spoken languages by using computer and Artificial Intelligence. As people from different regional areas using different digital platforms and expressing their views in their spoken language, it is now must to focus on working spoken languages in India to make our society smart and digital. NLP research grown tremendously in last decade which results in Siri, Google Assistant, Alexa, Cortona and many more automatic speech recognitions and understanding systems (ASR). Natural Language Processing can be understood by classifying it into Natural Language Generation and Natural Language Understanding. NLP is widely used in various domain such as Health Care, Chatbot, ASR building, HR, Sentiment analysis etc.

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16

Pujeri, Bhagyashree P. y Jagadeesh Sai D. "An Anatomization of Language Detection and Translation using NLP Techniques". International Journal of Innovative Technology and Exploring Engineering 10, n.º 2 (10 de diciembre de 2020): 69–77. http://dx.doi.org/10.35940/ijitee.b8265.1210220.

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The issue with identifying language relates to process of determining natural language in which specific text is written. This is one of the big difficulties in the processing of natural languages. Still, they also pose a problem in improving multiclass classification in this area. Language detection and translation a significant Language Identification task are required. The language analysis method may be carried out according to tools available in a particular language if the source language is known. A successful language detection algorithm determines the achievement of the sentiment analysis task and other identification tasks. Processing natural language and machine learning techniques involve knowledge that is annotated with its language. Algorithms for natural language processing must be updated according to language's grammar.This paper proposes a secure language detection and translation technique to solve the security in natural language processing problems. Language detection algorithm based on char n-gram based statistical detector and translation Yandex API is used.While translating, there should be encryption and decryption for that we are using AES Algorithm.

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17

P., Dr Karrupusamy. "Analysis of Neural Network Based Language Modeling". March 2020 2, n.º 1 (30 de marzo de 2020): 53–63. http://dx.doi.org/10.36548/jaicn.2020.1.006.

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The fundamental and core process of the natural language processing is the language modelling usually referred as the statistical language modelling. The language modelling is also considered to be vital in the processing the natural languages as the other chores such as the completion of sentences, recognition of speech automatically, translations of the statistical machines, and generation of text and so on. The success of the viable natural language processing totally relies on the quality of the modelling of the language. In the previous spans the research field such as the linguistics, psychology, speech recognition, data compression, neuroscience, machine translation etc. As the neural network are the very good choices for having a quality language modelling the paper presents the analysis of neural networks in the modelling of the language. Utilizing some of the dataset such as the Penn Tree bank, Billion Word Benchmark and the Wiki Test the neural network models are evaluated on the basis of the word error rate, perplexity and the bilingual evaluation under study scores to identify the optimal model.

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18

P., Dr Karrupusamy. "Analysis of Neural Network Based Language Modeling". March 2020 2, n.º 1 (30 de marzo de 2020): 53–63. http://dx.doi.org/10.36548/jaicn.2020.3.006.

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The fundamental and core process of the natural language processing is the language modelling usually referred as the statistical language modelling. The language modelling is also considered to be vital in the processing the natural languages as the other chores such as the completion of sentences, recognition of speech automatically, translations of the statistical machines, and generation of text and so on. The success of the viable natural language processing totally relies on the quality of the modelling of the language. In the previous spans the research field such as the linguistics, psychology, speech recognition, data compression, neuroscience, machine translation etc. As the neural network are the very good choices for having a quality language modelling the paper presents the analysis of neural networks in the modelling of the language. Utilizing some of the dataset such as the Penn Tree bank, Billion Word Benchmark and the Wiki Test the neural network models are evaluated on the basis of the word error rate, perplexity and the bilingual evaluation under study scores to identify the optimal model.

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19

Crowston, Kevin, Eileen E. Allen y Robert Heckman. "Using natural language processing technology for qualitative data analysis". International Journal of Social Research Methodology 15, n.º 6 (noviembre de 2012): 523–43. http://dx.doi.org/10.1080/13645579.2011.625764.

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20

Chong, Calvin, Usman Ullah Sheikh, Narina A. Samah y Ahmad Zuri Sha’ameri. "Analysis on Reflective Writing Using Natural Language Processing and Sentiment Analysis". IOP Conference Series: Materials Science and Engineering 884 (21 de julio de 2020): 012069. http://dx.doi.org/10.1088/1757-899x/884/1/012069.

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21

Sengupta, P. y B. B. Chaudhuri. "Natural Language Processing in an Indian Language (Bengali)-I: Verb Phrase Analysis". IETE Technical Review 10, n.º 1 (enero de 1993): 27–41. http://dx.doi.org/10.1080/02564602.1993.11437284.

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22

Li, Yong, Xiaojun Yang, Min Zuo, Qingyu Jin, Haisheng Li y Qian Cao. "Deep Structured Learning for Natural Language Processing". ACM Transactions on Asian and Low-Resource Language Information Processing 20, n.º 3 (9 de julio de 2021): 1–14. http://dx.doi.org/10.1145/3433538.

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The real-time and dissemination characteristics of network information make net-mediated public opinion become more and more important food safety early warning resources, but the data of petabyte (PB) scale growth also bring great difficulties to the research and judgment of network public opinion, especially how to extract the event role of network public opinion from these data and analyze the sentiment tendency of public opinion comment. First, this article takes the public opinion of food safety network as the research point, and a BLSTM-CRF model for automatically marking the role of event is proposed by combining BLSTM and conditional random field organically. Second, the Attention mechanism based on vocabulary in the field of food safety is introduced, the distance-related sequence semantic features are extracted by BLSTM, and the emotional classification of sequence semantic features is realized by using CNN. A kind of Att-BLSTM-CNN model for the analysis of public opinion and emotional tendency in the field of food safety is proposed. Finally, based on the time series, this article combines the role extraction of food safety events and the analysis of emotional tendency and constructs a net-mediated public opinion early warning model in the field of food safety according to the heat of the event and the emotional intensity of the public to food safety public opinion events.

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23

BOURBAKIS, NIKOLAOS G. y RICHARD ANDEL. "EIKONES — A LANGUAGE FOR IMAGE PROCESSING-ANALYSIS-PATTERN RECOGNITION". International Journal on Artificial Intelligence Tools 13, n.º 03 (septiembre de 2004): 547–67. http://dx.doi.org/10.1142/s0218213004001685.

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This paper presents a language for image processing-analysis called ElKONES. The language grammar is derived from the C language with extensions for efficient and flexible image processing. The EIKONES language provides to the user flexibility and friendliness for image processing which are not available in other image processing tools or conventional languages or function libraries. The basic idea behind EIKONES, is the consideration of the image processing algorithms as objects and the appropriate development of a formal grammar for its actual implementation. EIKONES provides a high level language for image processing with features such as learning capability, statistical and region analysis, object tracking. The way in which image operations are specified facilitates a natural language interface for use in a voice-activated system because multiple image operations are mapped to a single image word.

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24

Xiao, Yijun y William Yang Wang. "Quantifying Uncertainties in Natural Language Processing Tasks". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 7322–29. http://dx.doi.org/10.1609/aaai.v33i01.33017322.

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Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks. With empirical experiments on sentiment analysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhancing model performances in various NLP tasks.

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25

MCCORD, MICHAEL, ARENDSE BERNTH, SHALOM LAPPIN y WLODEK ZADROZNY. "NATURAL LANGUAGE PROCESSING WITHIN A SLOT GRAMMAR FRAMEWORK". International Journal on Artificial Intelligence Tools 01, n.º 02 (junio de 1992): 229–77. http://dx.doi.org/10.1142/s021821309200020x.

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This paper contains brief descriptions of the latest form of Slot Grammar and four natural language processing systems developed in this framework. Slot Grammar is a lexicalist, dependency-oriented grammatical system, based on the systematic expression of linguistic rules and data in terms of slots (essentially grammatical relations) and slot frames. The exposition focuses on the kinds of analysis structures produced by the Slot Grammar parser. These structures offer convenient input to post-syntactic processing (in particular to the applications dealt with in the paper); they contain in a single structure a useful combination of surface structure and logical form. The four applications discussed are: (1) An anaphora resolution system dealing with both NP anaphora and VP anaphora (and combinations of the two). (2) A meaning postulate based inference system for natural language, in which inference is done directly with Slot Grammar analysis structures. (3) A new transfer system for the machine translation system LMT, based on a new representation for Slot Grammar analyses which allows more convenient tree exploration. (4) A parser of "constructions", viewed as an extension of the core grammar allowing one to handle some linguistic phenomena that are often labeled "extragrammatical", and to assign a semantics to them.

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26

Liu, Ming, Weiwei Xu y Qiuxia Ran. "An Empirical Study of Writing Feedback Analysis of Non-English Majors in China with Natural Language Processing Technologies". International Journal of e-Education, e-Business, e-Management and e-Learning 5, n.º 2 (2015): 85–93. http://dx.doi.org/10.17706/ijeeee.2015.5.2.85-93.

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27

Longwei, Q. "Ontological approach to Chinese text processing". Doklady BGUIR 18, n.º 6 (1 de octubre de 2020): 49–56. http://dx.doi.org/10.35596/1729-7648-2020-18-6-49-56.

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To implement natural language user interface and an intelligent answer to questions, the knowledgebased semantic model for Chinese language processing is proposed. The article gives careful consideration to the existing methods and various knowledge bases for natural language processing. The analysis of these methods has led to the conclusion that in natural language processing, the knowledge base is the most fundamental and crucial part. The knowledge base makes it possible to ensure processing of a natural language based on initially described knowledge and to explain the processing operations. By virtue of the analysis of various methods for constructing knowledge bases about the English and Chinese languages, an ontological approach to the Chinese language processing was proposed. The Chinese language processing model has two main aspects: the design of knowledge base about the Chinese language and the development of ontology-based knowledge processing machine. The proposed approach is aimed at developing a semantic model of knowledge on the Chinese language. As a stage in the implementation of the approach, I designed the ontology of the Chinese language that can be applied for further processing of the language. This paper considers the preliminary version of the ontology and the principle of building a knowledge base about the Chinese language. There are no uniform standards and evaluation system for designing an ontology. Expansion, refinement and evaluation of the ontology require further research.

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28

Guetterman, Timothy C., Tammy Chang, Melissa DeJonckheere, Tanmay Basu, Elizabeth Scruggs y VG Vinod Vydiswaran. "Augmenting Qualitative Text Analysis with Natural Language Processing: Methodological Study". Journal of Medical Internet Research 20, n.º 6 (29 de junio de 2018): e231. http://dx.doi.org/10.2196/jmir.9702.

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29

Pestian, John, Henry Nasrallah, Pawel Matykiewicz, Aurora Bennett y Antoon Leenaars. "Suicide Note Classification Using Natural Language Processing: A Content Analysis". Biomedical Informatics Insights 3 (enero de 2010): BII.S4706. http://dx.doi.org/10.4137/bii.s4706.

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Suicide is the second leading cause of death among 25–34 year olds and the third leading cause of death among 15–25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of repeated attempts is generally left to clinical judgment. This paper presents our second attempt to determine the role of computational algorithms in understanding a suicidal patient's thoughts, as represented by suicide notes. We focus on developing methods of natural language processing that distinguish between genuine and elicited suicide notes. We hypothesize that machine learning algorithms can categorize suicide notes as well as mental health professionals and psychiatric physician trainees do. The data used are comprised of suicide notes from 33 suicide completers and matched to 33 elicited notes from healthy control group members. Eleven mental health professionals and 31 psychiatric trainees were asked to decide if a note was genuine or elicited. Their decisions were compared to nine different machine-learning algorithms. The results indicate that trainees accurately classified notes 49% of the time, mental health professionals accurately classified notes 63% of the time, and the best machine learning algorithm accurately classified the notes 78% of the time. This is an important step in developing an evidence-based predictor of repeated suicide attempts because it shows that natural language processing can aid in distinguishing between classes of suicidal notes.

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Costa-jussà, Marta R. "An analysis of gender bias studies in natural language processing". Nature Machine Intelligence 1, n.º 11 (14 de octubre de 2019): 495–96. http://dx.doi.org/10.1038/s42256-019-0105-5.

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Warnia Nengsih, M. Mahrus Zein y Nazifa Hayati. "Coarse-Grained Sentiment Analysis Berbasis Natural Language Processing – Ulasan Hotel". Jurnal Nasional Teknik Elektro dan Teknologi Informasi 10, n.º 1 (25 de febrero de 2021): 41–48. http://dx.doi.org/10.22146/jnteti.v10i1.548.

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Sentiment analysis adalah metode untuk memperoleh data dari berbagai platform yang tersedia di internet. Kemajuan teknologi memungkinkan mesin untuk mengenali suatu istilah yang dianggap sebagai opini positif maupun sebaliknya. Data-data dan opini tersebut berperan penting sebagai umpan balik produk, layanan, dan topik lainnya. Tanpa perlu memperoleh opini secara langsung dari masyarakat, pihak penyedia telah mendapatkan evaluasi yang penting guna mengembangkan diri. Bisnis perhotelan merupakan bidang yang terkait dengan jasa memberikan layanan pada pelanggan. Indikator keberlangsungan bisnis ini juga bergantung pada umpan balik pelanggannya dan dijadikan sebagai acuan untuk pengambilan kebijakan strategis. Teknik sentiment analysis berbasis Natural Language Processing dapat mengatasi permasalahan tersebut. Pada makalah ini prediksi dilakukan menggunakan classifier Random Forest (RF), sementara untuk merangkum kualitas classifier, digunakan kurva Receiver Operating Characteristic (ROC). Kurva ROC berupa grafik yang baik untuk merangkum kualitas classifier. Semakin tinggi kurva berada di atas garis diagonal, semakin baik prediksinya, dengan nilai kurva ROC yang diperoleh sebesar 0,90. Terlihat hasil ulasan terhadap opini pelanggan terhadap jasa dan pelayanan yang diberikan oleh hotel untuk kategori positif lebih banyak daripada kategori negatif. Polaritas dari ulasan diperoleh 68% ulasan pelanggan berada pada area positif dan 32% berada pada area negatif.

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32

Hussen Maulud, Dastan, Subhi R. M. Zeebaree, Karwan Jacksi, Mohammed Mohammed Sadeeq y Karzan Hussein Sharif. "State of Art for Semantic Analysis of Natural Language Processing". Qubahan Academic Journal 1, n.º 2 (31 de marzo de 2021): 21–28. http://dx.doi.org/10.48161/qaj.v1n2a40.

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Semantic analysis is an essential feature of the NLP approach. It indicates, in the appropriate format, the context of a sentence or paragraph. Semantics is about language significance study. The vocabulary used conveys the importance of the subject because of the interrelationship between linguistic classes. In this article, semantic interpretation is carried out in the area of Natural Language Processing. The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.

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Wang, Dongyang, Junli Su y Hongbin Yu. "Feature Extraction and Analysis of Natural Language Processing for Deep Learning English Language". IEEE Access 8 (2020): 46335–45. http://dx.doi.org/10.1109/access.2020.2974101.

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Xi, Su Mei. "Application of Natural Language Processing for Information Retrieval". Applied Mechanics and Materials 380-384 (agosto de 2013): 2614–18. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2614.

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Through a comprehensive analysis of using natural language processing in information retrieval, we compared the effects with the various natural language techniques for information retrieval precision in this paper. This is for the tasks of more suitable as well as accurate results of natural language processing.

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MICH, L. "NL-OOPS: from natural language to object oriented requirements using the natural language processing system LOLITA". Natural Language Engineering 2, n.º 2 (junio de 1996): 161–87. http://dx.doi.org/10.1017/s1351324996001337.

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This paper describes NL-OOPS, a CASE tool that supports requirements analysis by generating object oriented models from natural language requirements documents. The full natural language analysis is obtained using as a core system the Natural Language Processing System LOLITA. The object oriented analysis module implements an algorithm for the extraction of the objects and their associations for use in creating object models.

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Efthimiou, Eleni, Stavroula-Evita Fotinea y Galini Sapountzaki. "Feature-based natural language processing for GSL synthesis". Sign Language and Linguistics 10, n.º 1 (16 de octubre de 2007): 3–23. http://dx.doi.org/10.1075/sll.10.1.03eft.

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The work reported in this study is based on research that has been carried out while developing a sign synthesis system for Greek Sign Language (GSL): theoretical linguistic analysis as well as lexicon and grammar resources derived from this analysis. We focus on the organisation of linguistic knowledge that initiates the multi-functional processing required to achieve sign generation performed by a virtual signer. In this context, structure rules and lexical coding support sign synthesis of GSL utterances, by exploitation of avatar technologies for the representation of the linguistic message. Sign generation involves two subsystems: a Greek-to-GSL conversion subsystem and a sign performance subsystem. The conversion subsystem matches input strings of written Greek-to-GSL structure patterns, exploiting Natural Language Processing (NLP) mechanisms. The sign performance subsystem uses parsed output of GSL structure patterns, enriched with sign-specific information, to activate a virtual signer for the performance of properly coded linguistic messages. Both the conversion and the synthesis procedure are based on adequately constructed electronic linguistic resources. Applicability of sign synthesis is demonstrated with the example of a Web-based prototype environment for GSL grammar teaching.

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BALLIM, AFZAL y VINCENZO PALLOTTA. "Robust methods in analysis of natural language data". Natural Language Engineering 8, n.º 2-3 (junio de 2002): 93–96. http://dx.doi.org/10.1017/s1351324902002942.

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The automated analysis of natural language data has become a central issue in the design of intelligent information systems. Processing unconstrained natural language data is still considered as an AI-hard task. However, various analysis techniques have been proposed to address specific aspects of natural language. In particular, recent interest has been focused on providing approximate analysis techniques, assuming that when perfect analysis is not possible, partial results may be still very useful.

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Dorda, W., B. Haidl y P. Sachs. "Processing Medical Natural Language Data by the System WAREL". Methods of Information in Medicine 27, n.º 02 (abril de 1988): 67–72. http://dx.doi.org/10.1055/s-0038-1635521.

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SummaryMany clinical data are in natural language form (diagnoses, therapies, etc.). There is great interest in making these data retrievable to form samples of patients for scientific investigations (statistical analyses, courses of diseases, etc.). To perform this task, “medical natural language data” have to be prepared and stored in a retrieval-oriented database. In this paper, the advantages of processing textual data are shown in contrast to coding. Accordingly, in our system WAREL medical thesauri (like ICD 9 or SNOMED) are not used for codification; they are taken as a knowledge base during the retrieval and for testing the quality of the data during documentation. The fundamental methods (computerized textual analysis and different algorithms for comparing texts) are explained in detail, and their realization within the system WAREL is illustrated (WAREL stands for Wiener Allgemeines Relationenschema).

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39

Belinkov, Yonatan y James Glass. "Analysis Methods in Neural Language Processing: A Survey". Transactions of the Association for Computational Linguistics 7 (noviembre de 2019): 49–72. http://dx.doi.org/10.1162/tacl_a_00254.

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The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work.

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40

Vasavi, Gadamsetty y T. Sudha. "Web Mining System in a Natural Language Processing Based for Social Media Analysis". Asian Journal of Computer Science and Technology 8, S3 (5 de junio de 2019): 72–75. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2083.

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Social Media Monitoring and Analysis are the new trends in technology business. The challenge is to extract correct information from free-form texts of social media communication. Natural Language Processing methods are sometimes used in social media monitoring to improve accuracy in extracting information. This paper discusses a web mining system that is based on Natural Language Processing to analyze social media information. In that process, this research examines Natural Language methods that are important for such analysis. Then the traditional web mining steps are discussed along with proposed use of Natural Language Processing methods.

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41

Georgescu, Tiberiu-Marian. "Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents". Symmetry 12, n.º 3 (2 de marzo de 2020): 354. http://dx.doi.org/10.3390/sym12030354.

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This paper describes the development and implementation of a natural language processing model based on machine learning which performs cognitive analysis for cybersecurity-related documents. A domain ontology was developed using a two-step approach: (1) the symmetry stage and (2) the machine adjustment. The first stage is based on the symmetry between the way humans represent a domain and the way machine learning solutions do. Therefore, the cybersecurity field was initially modeled based on the expertise of cybersecurity professionals. A dictionary of relevant entities was created; the entities were classified into 29 categories and later implemented as classes in a natural language processing model based on machine learning. After running successive performance tests, the ontology was remodeled from 29 to 18 classes. Using the ontology, a natural language processing model based on a supervised learning model was defined. We trained the model using sets of approximately 300,000 words. Remarkably, our model obtained an F1 score of 0.81 for named entity recognition and 0.58 for relation extraction, showing superior results compared to other similar models identified in the literature. Furthermore, in order to be easily used and tested, a web application that integrates our model as the core component was developed.

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42

Panayiotou, Christiana. "An Ontological Analysis and Natural Language Processing of Figures of Speech". International Journal of Artificial Intelligence & Applications 11, n.º 1 (31 de enero de 2020): 17–30. http://dx.doi.org/10.5121/ijaia.2020.11102.

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Dror, Rotem, Gili Baumer, Marina Bogomolov y Roi Reichart. "Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets". Transactions of the Association for Computational Linguistics 5 (diciembre de 2017): 471–86. http://dx.doi.org/10.1162/tacl_a_00074.

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With the ever growing amount of textual data from a large variety of languages, domains, and genres, it has become standard to evaluate NLP algorithms on multiple datasets in order to ensure a consistent performance across heterogeneous setups. However, such multiple comparisons pose significant challenges to traditional statistical analysis methods in NLP and can lead to erroneous conclusions. In this paper we propose a Replicability Analysis framework for a statistically sound analysis of multiple comparisons between algorithms for NLP tasks. We discuss the theoretical advantages of this framework over the current, statistically unjustified, practice in the NLP literature, and demonstrate its empirical value across four applications: multi-domain dependency parsing, multilingual POS tagging, cross-domain sentiment classification and word similarity prediction.

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44

Tierney, Patrick. "A qualitative analysis framework using natural language processing and graph theory". International Review of Research in Open and Distributed Learning 13, n.º 5 (8 de noviembre de 2012): 173. http://dx.doi.org/10.19173/irrodl.v13i5.1240.

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<p style="margin-bottom: 0in; line-height: 200%;">This paper introduces a method of extending natural language-based processing of qualitative data analysis with the use of a very quantitative tool—graph theory. It is not an attempt to convert qualitative research to a positivist approach with a mathematical black box, nor is it a “graphical solution”. Rather, it is a method to help qualitative researchers, especially those with limited experience, to discover and tease out what lies within the data. A quick review of coding is followed by basic explanations of natural language processing, artificial intelligence, and graph theory to help with understanding the method. The process described herein is limited by neither the size of the data set nor the domain in which it is applied. It has the potential to substantially reduce the amount of time required to analyze qualitative data and to assist in the discovery of themes that might not have otherwise been detected.<br /><br /></p>

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Gabrilovich, E. y S. Markovitch. "Wikipedia-based Semantic Interpretation for Natural Language Processing". Journal of Artificial Intelligence Research 34 (30 de marzo de 2009): 443–98. http://dx.doi.org/10.1613/jair.2669.

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Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.

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46

SENGUPTA, P. y B. B. CHAUDHURI. "A MORPHO-SYNTACTIC ANALYSIS BASED LEXICAL SUBSYSTEM". International Journal of Pattern Recognition and Artificial Intelligence 07, n.º 03 (junio de 1993): 595–619. http://dx.doi.org/10.1142/s0218001493000303.

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A lexical subsystem that contains a morphological level parser is necessary for processing natural languages in general and inflectional languages in particular. Such a subsystem should be able to generate the surface form (i.e. as it appears in a natural sentence) of a word, given the sequence of morphemes constituting the word. Conversely, and more importantly, the subsystem should be able to parse a word into its constituent morphemes. A formalism which enables the lexicon writer to specify the lexicon of an inflectional language is discussed. The specifications are used to build up a lexical description in the form of a lexical database on one hand and a formulation of derivational morphology, called Augmented Finite State Automata (AFSA), on the other. A compact lexical representation has been achieved, where generation of the surface forms of a word, as well as parsing of a word is performed in a computationally attractive manner. The output produced as a result of parsing is suitable for input to the next stage of analysis in a Natural Language Processing (NLP) environment, which, in our case is based on a generalization of the Lexical Functional Grammar (LFG). The application of the formalism on inflectional Indian languages is considered, with Bengali, a modern Indian language, as a case study.

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Attrey, Rohin y Alexander Levit. "The promise of natural language processing in healthcare". University of Western Ontario Medical Journal 87, n.º 2 (12 de marzo de 2019): 21–23. http://dx.doi.org/10.5206/uwomj.v87i2.1152.

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The healthcare industry generates data at a rapid rate, with no signs of slowing down. A large portion of this information takes the form of unstructured narrative text, making it difficult for computer systems to analyze the data in a usable format. However, automated analysis of this information could be incredibly useful in daily practice. This could be accomplished with natural language processing, an area of artificial intelligence and computational linguistics that is used to analyze and process large sets of unstructured data, namely spoken or written communication. Natural language processing has already been implemented in many sectors, and the industry is projected to be worth US$16 billion by 2021. Natural language processing could take unstructured patient data and interpret meaning from the text, allowing that information to inform healthcare delivery. Natural language processing can also enable intelligent chatbots, interacting and providing medical support to patients. It has the potential to aid physicians by efficiently summarizing patient charts and predicting patient outcomes. In hospitals, it has the ability to analyze patient satisfaction and facilitate quality improvement. Despite current technical limitations, natural language processing is a rapidly developing technology that promises to improve the quality and efficiency of healthcare delivery.

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Taskin, Zehra y Umut Al. "Natural language processing applications in library and information science". Online Information Review 43, n.º 4 (12 de agosto de 2019): 676–90. http://dx.doi.org/10.1108/oir-07-2018-0217.

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Purpose With the recent developments in information technologies, natural language processing (NLP) practices have made tasks in many areas easier and more practical. Nowadays, especially when big data are used in most research, NLP provides fast and easy methods for processing these data. The purpose of this paper is to identify subfields of library and information science (LIS) where NLP can be used and to provide a guide based on bibliometrics and social network analyses for researchers who intend to study this subject. Design/methodology/approach Within the scope of this study, 6,607 publications, including NLP methods published in the field of LIS, are examined and visualized by social network analysis methods. Findings After evaluating the obtained results, the subject categories of publications, frequently used keywords in these publications and the relationships between these words are revealed. Finally, the core journals and articles are classified thematically for researchers working in the field of LIS and planning to apply NLP in their research. Originality/value The results of this paper draw a general framework for LIS field and guides researchers on new techniques that may be useful in the field.

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Arnold, Taylor. "A Tidy Data Model for Natural Language Processing using cleanNLP". R Journal 9, n.º 2 (2017): 248. http://dx.doi.org/10.32614/rj-2017-035.

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Wei, Wei, Jinsong Wu y Chunsheng Zhu. "Special issue on deep learning for natural language processing". Computing 102, n.º 3 (9 de enero de 2020): 601–3. http://dx.doi.org/10.1007/s00607-019-00788-3.

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