Relevant bibliographies by topics / Natural language processing techniques

Academic literature on the topic 'Natural language processing techniques'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Natural language processing techniques"

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Yilmaz, A. Egemen. "Natural Language Processing." International Journal of Systems and Service-Oriented Engineering 4, no. 1 (January 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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Valiyev, Giavid, Marcello Piraino, Arvid Kok, Michael Street, Ivana Ilic Mestric, and Retzius Birger. "Initial Exploitation of Natural Language Processing Techniques on NATO Strategy and Policies." Information & Security: An International Journal 47, no. 2 (2020): 187–202. http://dx.doi.org/10.11610/isij.4713.

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Pujeri, Bhagyashree P., and Jagadeesh Sai D. "An Anatomization of Language Detection and Translation using NLP Techniques." International Journal of Innovative Technology and Exploring Engineering 10, no. 2 (December 10, 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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H.Yousif, Jabar. "Natural Language Processing based Soft Computing Techniques." International Journal of Computer Applications 77, no. 8 (September 18, 2013): 43–49. http://dx.doi.org/10.5120/13418-1089.

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Radev, Dragomir R., and Rada Mihalcea. "Networks and Natural Language Processing." AI Magazine 29, no. 3 (September 5, 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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Korycinski, C., and Alan F. Newell. "Natural-language processing and automatic indexing." Indexer: The International Journal of Indexing: Volume 17, Issue 1 17, no. 1 (April 1, 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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Panchal, Drashti, Mihika Mehta, Aryaman Mishra, Saish Ghole, and Mrs Smita Dandge. "Sentiment Analysis Using Natural Language Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2262–66. http://dx.doi.org/10.22214/ijraset.2022.42711.

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Abstract: In recent years, there has been an increasing interest in using natural language processing (NLP) to perform sentiment analysis. This is because NLP can help to automatically extract and identify the sentiment expressed in text data, which is often more accurate and reliable than using human annotation. There are a variety of NLP techniques that can be used for sentiment analysis, including opinion mining, text classification, and lexical analysis. Each of these methods has its own advantages and disadvantages, and the choice of technique will often depend on the type and quality of the text data that is available. In general, sentiment analysis using NLP is a very promising area of research with many potential applications. As more and more text data is generated, it will become increasingly important to be able to automatically extract the sentiment expressed in this data.
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Jain, Dr Meghna. "Sentiment Classification of Hindi Language using Natural Language Processing Techniques." Journal of Language and Linguistics in Society, no. 26 (November 21, 2022): 7–10. http://dx.doi.org/10.55529/jlls.26.7.10.

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This paper has presented Hybrid Approach for determination of sentimental phrase or words from Hindi text automatically through use of Hindi sentiment’s lexicon and classifying them into polarity i.e. Positive, Negative and Neutral.
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Fairie, Paul, Zilong Zhang, Adam G. D'Souza, Tara Walsh, Hude Quan, and Maria J. Santana. "Categorising patient concerns using natural language processing techniques." BMJ Health & Care Informatics 28, no. 1 (June 2021): e100274. http://dx.doi.org/10.1136/bmjhci-2020-100274.

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ObjectivesPatient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the results of using a semiautomated, computational topic-modelling approach to analyse a corpus of patient feedback.MethodsPatient concerns were received by Alberta Health Services between 2011 and 2018 (n=76 163), regarding 806 care facilities in 163 municipalities, including hospitals, clinics, community care centres and retirement homes, in a province of 4.4 million. Their existing framework requires manual labelling of pre-defined categories. We applied an automated latent Dirichlet allocation (LDA)-based topic modelling algorithm to identify the topics present in these concerns, and thereby produce a framework-free categorisation.ResultsThe LDA model produced 40 topics which, following manual interpretation by researchers, were reduced to 28 coherent topics. The most frequent topics identified were communication issues causing delays (frequency: 10.58%), community care for elderly patients (8.82%), interactions with nurses (8.80%) and emergency department care (7.52%). Many patient concerns were categorised into multiple topics. Some were more specific versions of categories from the existing framework (eg, communication issues causing delays), while others were novel (eg, smoking in inappropriate settings).DiscussionLDA-generated topics were more nuanced than the manually labelled categories. For example, LDA found that concerns with community care were related to concerns about nursing for seniors, providing opportunities for insight and action.ConclusionOur findings outline the range of concerns patients share in a large health system and demonstrate the usefulness of using LDA to identify categories of patient concerns.
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Nohria, Ankita, and Harkiran Kaur. "Evaluation of Parsing Techniques in Natural Language Processing." International Journal of Computer Trends and Technology 60, no. 1 (June 25, 2018): 31–34. http://dx.doi.org/10.14445/22312803/ijctt-v60p104.

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Dissertations / Theses on the topic "Natural language processing techniques"

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Cosh, Kenneth John. "Supporting organisational semiotics with natural language processing techniques." Thesis, Lancaster University, 2003. http://eprints.lancs.ac.uk/12351/.

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Harmain, H. M. "Building object-oriented conceptual models using natural language processing techniques." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312740.

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Califf, Mary Elaine. "Relational learning techniques for natural language information extraction /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Eyecioglu, Ozmutlu Asli. "Paraphrase identification using knowledge-lean techniques." Thesis, University of Sussex, 2016. http://sro.sussex.ac.uk/id/eprint/65497/.

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This research addresses the problem of identification of sentential paraphrases; that is, the ability of an estimator to predict well whether two sentential text fragments are paraphrases. The paraphrase identification task has practical importance in the Natural Language Processing (NLP) community because of the need to deal with the pervasive problem of linguistic variation. Accurate methods for identifying paraphrases should help to improve the performance of NLP systems that require language understanding. This includes key applications such as machine translation, information retrieval and question answering amongst others. Over the course of the last decade, a growing body of research has been conducted on paraphrase identification and it has become an individual working area of NLP. Our objective is to investigate whether techniques concentrating on automated understanding of text requiring less resource may achieve results comparable to methods employing more sophisticated NLP processing tools and other resources. These techniques, which we call “knowledge-lean”, range from simple, shallow overlap methods based on lexical items or n-grams through to more sophisticated methods that employ automatically generated distributional thesauri. The work begins by focusing on techniques that exploit lexical overlap and text-based statistical techniques that are much less in need of NLP tools. We investigate the question “To what extent can these methods be used for the purpose of a paraphrase identification task?” For the two gold standard data, we obtained competitive results on the Microsoft Research Paraphrase Corpus (MSRPC) and reached the state-of-the-art results on the Twitter Paraphrase Corpus, using only n-gram overlap features in conjunction with support vector machines (SVMs). These techniques do not require any language specific tools or external resources and appear to perform well without the need to normalise colloquial language such as that found on Twitter. It was natural to extend the scope of the research and to consider experimenting on another language, which is poor in resources. The scarcity of available paraphrase data led us to construct our own corpus; we have constructed a paraphrasecorpus in Turkish. This corpus is relatively small but provides a representative collection, including a variety of texts. While there is still debate as to whether a binary or fine-grained judgement satisfies a paraphrase corpus, we chose to provide data for a sentential textual similarity task by agreeing on fine-grained scoring, knowing that this could be converted to binary scoring, but not the other way around. The correlation between the results from different corpora is promising. Therefore, it can be surmised that languages poor in resources can benefit from knowledge-lean techniques. Discovering the strengths of knowledge-lean techniques extended with a new perspective to techniques that use distributional statistical features of text by representing each word as a vector (word2vec). While recent research focuses on larger fragments of text with word2vec, such as phrases, sentences and even paragraphs, a new approach is presented by introducing vectors of character n-grams that carry the same attributes as word vectors. The proposed method has the ability to capture syntactic relations as well as semantic relations without semantic knowledge. This is proven to be competitive on Twitter compared to more sophisticated methods.
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Chong, Man Yan Miranda. "A study on plagiarism detection and plagiarism direction identification using natural language processing techniques." Thesis, University of Wolverhampton, 2013. http://hdl.handle.net/2436/298219.

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Ever since we entered the digital communication era, the ease of information sharing through the internet has encouraged online literature searching. With this comes the potential risk of a rise in academic misconduct and intellectual property theft. As concerns over plagiarism grow, more attention has been directed towards automatic plagiarism detection. This is a computational approach which assists humans in judging whether pieces of texts are plagiarised. However, most existing plagiarism detection approaches are limited to super cial, brute-force stringmatching techniques. If the text has undergone substantial semantic and syntactic changes, string-matching approaches do not perform well. In order to identify such changes, linguistic techniques which are able to perform a deeper analysis of the text are needed. To date, very limited research has been conducted on the topic of utilising linguistic techniques in plagiarism detection. This thesis provides novel perspectives on plagiarism detection and plagiarism direction identi cation tasks. The hypothesis is that original texts and rewritten texts exhibit signi cant but measurable di erences, and that these di erences can be captured through statistical and linguistic indicators. To investigate this hypothesis, four main research objectives are de ned. First, a novel framework for plagiarism detection is proposed. It involves the use of Natural Language Processing techniques, rather than only relying on the vii traditional string-matching approaches. The objective is to investigate and evaluate the in uence of text pre-processing, and statistical, shallow and deep linguistic techniques using a corpus-based approach. This is achieved by evaluating the techniques in two main experimental settings. Second, the role of machine learning in this novel framework is investigated. The objective is to determine whether the application of machine learning in the plagiarism detection task is helpful. This is achieved by comparing a thresholdsetting approach against a supervised machine learning classi er. Third, the prospect of applying the proposed framework in a large-scale scenario is explored. The objective is to investigate the scalability of the proposed framework and algorithms. This is achieved by experimenting with a large-scale corpus in three stages. The rst two stages are based on longer text lengths and the nal stage is based on segments of texts. Finally, the plagiarism direction identi cation problem is explored as supervised machine learning classi cation and ranking tasks. Statistical and linguistic features are investigated individually or in various combinations. The objective is to introduce a new perspective on the traditional brute-force pair-wise comparison of texts. Instead of comparing original texts against rewritten texts, features are drawn based on traits of texts to build a pattern for original and rewritten texts. Thus, the classi cation or ranking task is to t a piece of text into a pattern. The framework is tested by empirical experiments, and the results from initial experiments show that deep linguistic analysis contributes to solving the problems we address in this thesis. Further experiments show that combining shallow and viii deep techniques helps improve the classi cation of plagiarised texts by reducing the number of false negatives. In addition, the experiment on plagiarism direction detection shows that rewritten texts can be identi ed by statistical and linguistic traits. The conclusions of this study o er ideas for further research directions and potential applications to tackle the challenges that lie ahead in detecting text reuse.
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Al, Qady Mohammed Abdelrahman. "Concept relation extraction using natural language processing the CRISP technique /." [Ames, Iowa : Iowa State University], 2008.

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Björner, Amanda. "Natural Language Processing techniques for feedback on text improvement : A qualitative study on press releases." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301303.

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Press releases play a key role in today’s news production by being public statements of newsworthy content that function as a pre-formulation of news. Press releases originate from a wide range of actors, and a common goal is for them to reach a high societal impact. This thesis examines how Natural Language Processing (NLP) techniques can be successful in giving feedback to press release authors that help enhance the content and quality of their texts. This could, in turn, contribute to increased impact. To examine this, the research question is divided into two parts. The first part examines how content-perception feedback can contribute to improving press releases. This is examined by the development of a web tool where user- written press releases get analyzed. The analysis consists of a readability assessment using the LIX metric and linguistic bias detection of weasel words and peacock words through rule-based sentiment analysis. The user experiences and opinions are evaluated through an online questionnaire and semi-structured interviews. The second part of the research question examines how trending topic information can contribute to improving press releases. This part is examined theoretically based on a literature review of state-of-the- art methods and qualitatively by gathering opinions from press release authors in the previously mentioned questionnaire and interviews. Based on the results, it is identified that for content-perception feedback, it is especially lesser experienced authors and scientific content aimed at the general public that would achieve improved text quality from objective readability assessment and detection of biased expressions. Nevertheless, most of the evaluation participants were more satisfied with their press releases after editing based on the readability feedback, and all participants with biased words in their texts reported that the detection led to positive changes resulting in improved text quality. As for the theoretical part, it is considered that both text quality and the number of publications increase when writing about trending topics. To give authors trending topic information on a detailed level is indicated to be the most helpful.
Aktörer som sträcker sig från privata företag till mydigheter och forskare använder pressmeddelanden för att offentligt delge information med nyhetsvärde. Dessa pressmeddelanden spelar därefter en nyckelroll i dagens nyhetsproduktion genom att förformulera nyheter och eftersträvar därför att hålla en viss språklig nivå. För att förbättra kvalitet och innehåll i pressmeddelanden undersöker detta examensarbete hur språkteknologisk textanalys och återkoppling till författare kan stödja dem i att förbättra sina texter. Denna frågeställning undersöks i två delar, en tillämpad del och en teoretisk del. Den tillämpade delen undersöker hur återkoppling kring innehållsuppfattning kan förbättra pressmeddelanden. Ett webb-baserat verktyg utvecklades där användare kan skriva in pressmeddelanden och få dessa analyserade. Analysen baseras på läsbarhet som bedöms med hjälp av måttet LIX samt språklig bias (partiska uttryck) i form av weasel words (vessleord) och peacock words (påfågelord) som detekteras genom regelbaserad sentimentanalys. Denna del utvärderades kvalitativt genom en enkätundersökning till användarna samt djupintervjuer. Den teoretiska delen av frågeställningen undersöker hur information om trendande ämnen kan bidra till att förbättra pressmeddelanden. Undersökningen genomfördes som en litteraturstudie och utvärderades kvalitativt genom att sammanställa åsikter från yrkesverksamma som arbetar med pressmeddelanden i enkätundersökningen och djupintervjuerna som beskrevs ovan. Resultaten indikerar att för feedback om innehållsuppfattning är det särskilt mindre erfarna författare och vetenskapligt innehåll riktat till allmänheten som skulle uppnå förbättrad textkvalitet till följd av läsbarhetsbedömning och upptäckt av partiska uttryck. Samtidigt var en majoritet av deltagarna i utvärderingen mer nöjda med sina pressmeddelanden efter redigering baserat på läsbarhetsfeedbacken. Dessutom rapporterade alla deltagare med partiska uttryck i sina texter att upptäckten ledde till positiva förändringar som resulterade i förbättrad textkvalitet. Gällande den teoretiska delen anses både textkvaliteten och antalet publikationer öka för pressmeddelnanden om trendande ämnen. Att ge författare information om trendande ämnen på en detaljerad nivå indikeras vara det mest hjälpsamma.
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Peri, Deepthi. "Applying Natural Language Processing and Deep Learning Techniques for Raga Recognition in Indian Classical Music." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99967.

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In Indian Classical Music (ICM), the Raga is a musical piece's melodic framework. It encompasses the characteristics of a scale, a mode, and a tune, with none of them fully describing it, rendering the Raga a unique concept in ICM. The Raga provides musicians with a melodic fabric, within which all compositions and improvisations must take place. Identifying and categorizing the Raga is challenging due to its dynamism and complex structure as well as the polyphonic nature of ICM. Hence, Raga recognition—identify the constituent Raga in an audio file—has become an important problem in music informatics with several known prior approaches. Advancing the state of the art in Raga recognition paves the way to improving other Music Information Retrieval tasks in ICM, including transcribing notes automatically, recommending music, and organizing large databases. This thesis presents a novel melodic pattern-based approach to recognizing Ragas by representing this task as a document classification problem, solved by applying a deep learning technique. A digital audio excerpt is hierarchically processed and split into subsequences and gamaka sequences to mimic a textual document structure, so our model can learn the resulting tonal and temporal sequence patterns using a Recurrent Neural Network. Although training and testing on these smaller sequences, we predict the Raga for the entire audio excerpt, with the accuracy of 90.3% for the Carnatic Music Dataset and 95.6% for the Hindustani Music Dataset, thus outperforming prior approaches in Raga recognition.
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In Indian Classical Music (ICM), the Raga is a musical piece's melodic framework. The Raga is a unique concept in ICM, not fully described by any of the fundamental concepts of Western classical music. The Raga provides musicians with a melodic fabric, within which all compositions and improvisations must take place. Raga recognition refers to identifying the constituent Raga in an audio file, a challenging and important problem with several known prior approaches and applications in Music Information Retrieval. This thesis presents a novel approach to recognizing Ragas by representing this task as a document classification problem, solved by applying a deep learning technique. A digital audio excerpt is processed into a textual document structure, from which the constituent Raga is learned. Based on the evaluation with third-party datasets, our recognition approach achieves high accuracy, thus outperforming prior approaches.
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Imperatore, Gennaro. "Improving ease and speed of use of mobile augmentative and alternative communication systems through the use of natural language processing and natural language generation techniques." Thesis, University of Strathclyde, 2016. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27381.

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Communication is recognised as a human right by the United Nations. Currently there are millions of people who for a variety of reasons cannot communicate comfortably. For example, in the UK alone there are 250,000 people who as a result of a stroke are now unable to communicate and are affected by a condition known as Aphasia. These people are said to have Complex Communication Needs. With the proliferation of smart devices like tablets and Smartphone, people with Complex Communication Needs are discovering the assistive potential of these devices to aid them either in the recuperation of their communicative abilities or to assist them in their daily lives. These systems are called Alternative and Augmentative Communication Systems. However current AAC systems suffer from the fact that they are cumbersome to use and users require a long time to form sentences, with the result that they cannot confidently communicate and therefore are left isolated and frustrated. Even though much work has been done in the area these systems are remain slow and communication is not effective. This thesis investigates whether the inclusion of Natural Language Processing and Natural Language Generation techniques into Augmentative and Alternative Communication systems on mobile devices can improve the ease and speed of use for users with Complex Communication Needs by implementing “Dictum” an AAC app which makes use of NLG/NLP techniques. The work followed the approach of Action Research in which the target users help the investigator by identifying the problem, sanctioning the research and evaluating the results. Therefore, users were actively involved in the design of the application from the very start and gave feedback after each iteration leading to the final application. This work has found that the inclusion of NLP and NLG in AAC does indeed improve ease and speed of use when compared to popular apps available today. Dictum improves speed by doing two things: reducing the set space of words by providing words that are relevant to the last word inserted by using a Semantic Network of nouns and allowing the user to build sentences by requiring selection of key words only and delegating the responsibility of sentence formation to the application itself. In addition, during the course of this work, an effective mechanism of capturing requirements for users with Complex Communication Needs discovered by looking at how users adapt the functionality of their devices. The app was evaluated both quantitatively, by computing keystrokes savings and evaluating the interface using well-established HCI laws, and qualitatively by asking for the feedback of potential users and Speech and Language Therapists, following the practice of Action Research to involve those touched by the problem.
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Antici, Francesco. "Advanced techniques for cross-language annotation projection in legal texts." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23884/.

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Nowadays, the majority of the services we benefit from, are provided online and their use is regulated by the acceptance to the terms of service by the users. All our data are handled accordingly with the clauses of such document and all our behaviours must comply with it. Given so, it would be very useful to find automated techniques to ensure fairness of the document or inform the users about possible threats. The focus of this work, is to create resources aimed to the development of such tools in languages other than English, which may lack in linguistic resources and annotated corpus. The enormous breakthroughs of the last years in Natural Language Processing techniques made it possible the creation of such tools through automated and unsupervised process. One of the means to achieve that is through the annotation projection between two parallel corpora. The difficulties and costs of creating ad hoc resource for every language has brought the need to find another way for achieving the goal.\\ This work investigates the cross language annotation projection technique based on sentence embedding and similarity metrics to find matches between sentences. Several combination of methods and algorithms are compared, among which there are monolingual and multilingual embedding neural models. The experiments are conducted on two datasets, where the reference language is always English and the projection are evaluated on Italian, German and Polish. The results obtained provide a robust and reliable technique for the task and a good starting point to build multilingual tools.
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Books on the topic "Natural language processing techniques"

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Perez-Marin, Diana. Conversational agents and natural language interaction: Techniques and effective practices. Hershey, PA: Information Science Reference, 2011.

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Harrington, Jonathan. Techniques in speech acoustics. Dordrecht: Kluwer Academic Publishers, 1999.

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1952-, Cassidy Steve, ed. Techniques in speech acoustics. Dordrecht: Kluwer Academic Publishers, 1999.

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C, Rothman Raymond. Programming in your words, expert or novice: With any database program, custom-make your programs, linguistic technique, filing and accounting system. Woodland Hills, Calif., USA: Database Users & Information Managers International, 1988.

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Natural language processing. Oxford, OX: Blackwell Scientific Publications, 1988.

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Filgueiras, M., L. Damas, N. Moreira, and A. P. Tomás, eds. Natural Language Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-53678-7.

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Shwartz, Steven P. Applied natural language processing. Princeton, N.J: Petrocelli Books, 1987.

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Cohen, Kevin Bretonnel, and Dina Demner-Fushman. Biomedical natural language processing. Amsterdam: John Benjamins Publishing Company, 2014.

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Kulkarni, Akshay, and Adarsha Shivananda. Natural Language Processing Recipes. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7351-7.

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Kulkarni, Akshay, Adarsha Shivananda, and Anoosh Kulkarni. Natural Language Processing Projects. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7386-9.

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Book chapters on the topic "Natural language processing techniques"

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Bethu, Srikanth, Suresh Mamidisetti, S. Bhargavi Latha, and B. Sankara Babu. "S2S Translator by Natural Language Processing." In Smart Computing Techniques and Applications, 475–85. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0878-0_47.

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Fujita, Atsushi. "Natural language processing techniques for translation." In Metalanguages for Dissecting Translation Processes, 216–36. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003250852-19.

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Korkontzelos, Ioannis, Ioannis P. Klapaftis, and Suresh Manandhar. "Reviewing and Evaluating Automatic Term Recognition Techniques." In Advances in Natural Language Processing, 248–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85287-2_24.

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Bouamor, Houda, Aurélien Max, and Anne Vilnat. "Comparison of Paraphrase Acquisition Techniques on Sentential Paraphrases." In Advances in Natural Language Processing, 67–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14770-8_9.

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Rodzin, Sergey, Victoria Bova, Yury Kravchenko, and Lada Rodzina. "Deep Learning Techniques for Natural Language Processing." In Artificial Intelligence Trends in Systems, 121–30. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09076-9_11.

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Chang, Yi, Hongbo Xu, and Shuo Bai. "A Re-examination of IR Techniques in QA System." In Natural Language Processing – IJCNLP 2004, 71–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30211-7_8.

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Díaz, Isabel, Lidia Moreno, Inmaculada Fuentes, and Oscar Pastor. "Integrating Natural Language Techniques in OO-Method." In Computational Linguistics and Intelligent Text Processing, 560–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30586-6_60.

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Kozareva, Zornitsa, and Andrés Montoyo. "Paraphrase Identification on the Basis of Supervised Machine Learning Techniques." In Advances in Natural Language Processing, 524–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11816508_52.

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Fernandez, Izaskun, Yolanda Lekuona, Ruben Ferreira, Santiago Fernández, and Aitor Arnaiz. "Sentiment Analysis Techniques for Positive Language Development." In Natural Language Processing and Information Systems, 172–83. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07983-7_24.

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Opdahl, Andreas L. "Knowledge Graphs and Natural-Language Processing." In Big Data in Emergency Management: Exploitation Techniques for Social and Mobile Data, 75–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48099-8_4.

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Conference papers on the topic "Natural language processing techniques"

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Kaur, Kamaldeep. "Topic tracking techniques for natural language processing." In the International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2007052.2007066.

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Shinu, Ashmy Achu, V. Keerthi, Madhav Manoj, Muhammed Shaheer, Namitha S. Nair, and Ansamma John. "Customized Alumni Portal implementing Natural Language Processing Techniques." In 2019 International Conference on Intelligent Computing and Control Systems (ICCS). IEEE, 2019. http://dx.doi.org/10.1109/iccs45141.2019.9065320.

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Singh, Jyotika. "Social Media Analysis using Natural Language Processing Techniques." In Python in Science Conference. SciPy, 2021. http://dx.doi.org/10.25080/majora-1b6fd038-009.

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Arbaaeen, Ammar, and Asadullah Shah. "Natural Language Processing based Question Answering Techniques: A Survey." In 2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS). IEEE, 2020. http://dx.doi.org/10.1109/icetas51660.2020.9484290.

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Patten, Terry, Catherine Call, Daniel Mitchell, Jason Taylor, and Samuel Lasser. "Defining the Malice Space with Natural Language Processing Techniques." In 2016 Cybersecurity Symposium (CYBERSEC). IEEE, 2016. http://dx.doi.org/10.1109/cybersec.2016.015.

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Radulescu, Cristina, Mihaela Dinsoreanu, and Rodica Potolea. "Identification of spam comments using natural language processing techniques." In 2014 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2014. http://dx.doi.org/10.1109/iccp.2014.6936976.

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Jeschke, Sabina, Nicole Natho, Sebastian Rittau, and Marc Wilke. "mArachna - Applying Natural Language Processing Techniques to Ontology Engineering." In Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007). IEEE, 2007. http://dx.doi.org/10.1109/icalt.2007.185.

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McCoy, Kathleen, Patrick Demasco, Mark Jones, Christopher Pennington, and Charles Rowe. "Applying natural language processing techniques to augmentative communication systems." In the 13th conference. Morristown, NJ, USA: Association for Computational Linguistics, 1990. http://dx.doi.org/10.3115/991146.991235.

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Iyer, Gish, and McCarthy. "Unsupervised training techniques for natural language call routing." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1004770.

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Pan, Yuran, Qiangwen Xu, and Yanjun Li. "Food Recipe Alternation and Generation with Natural Language Processing Techniques." In 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW). IEEE, 2020. http://dx.doi.org/10.1109/icdew49219.2020.000-1.

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Reports on the topic "Natural language processing techniques"

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Li, Eliot, Charles Nicholas, Tim Oates, and Raman K. Mehra. Intelligent Record Linkage Techniques Based on Information Retrieval, Natural Language Processing, and Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada408937.

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Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41960.

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Abstract:
The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in nonquantitative text descriptions. We present initial text mining analyses of parsed text in the digitally available USDA soil taxonomy documentation and the Soil Survey Geographic database. Previous research has shown that latent information structure can be extracted from scientific literature using Natural Language Processing techniques, and we show that this latent information can be used to expedite query performance by using syntactic elements and part-of-speech tags as indices. Technical vocabulary often poses a text mining challenge due to the rarity of its diction in the broader context. We introduce an extension to the common English vocabulary that allows for nearly-complete indexing of USDA Soil Series Descriptions.
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Steedman, Mark. Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada290396.

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Tratz, Stephen C. Arabic Natural Language Processing System Code Library. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada603814.

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Wilks, Yorick, Michael Coombs, Roger T. Hartley, and Dihong Qiu. Active Knowledge Structures for Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada245893.

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Firpo, M. Natural Language Processing as a Discipline at LLNL. Office of Scientific and Technical Information (OSTI), February 2005. http://dx.doi.org/10.2172/15015192.

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Anderson, Thomas. State of the Art of Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada188112.

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Hobbs, Jerry R., Douglas E. Appelt, John Bear, Mabry Tyson, and David Magerman. Robust Processing of Real-World Natural-Language Texts. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada258837.

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Neal, Jeannette G., Elissa L. Feit, Douglas J. Funke, and Christine A. Montgomery. An Evaluation Methodology for Natural Language Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada263301.

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Lehnert, Wendy G. Using Case-Based Reasoning in Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada273538.

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