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

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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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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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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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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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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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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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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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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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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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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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Tesis sobre el tema "Natural language processing analysis"

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Woldemariam, Yonas Demeke. "Natural language processing in cross-media analysis". Licentiate thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-147640.

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A cross-media analysis framework is an integrated multi-modal platform where a media resource containing different types of data such as text, images, audio and video is analyzed with metadata extractors, working jointly to contextualize the media resource. It generally provides cross-media analysis and automatic annotation, metadata publication and storage, searches and recommendation services. For on-line content providers, such services allow them to semantically enhance a media resource with the extracted metadata representing the hidden meanings and make it more efficiently searchable. Within the architecture of such frameworks, Natural Language Processing (NLP) infrastructures cover a substantial part. The NLP infrastructures include text analysis components such as a parser, named entity extraction and linking, sentiment analysis and automatic speech recognition. Since NLP tools and techniques are originally designed to operate in isolation, integrating them in cross-media frameworks and analyzing textual data extracted from multimedia sources is very challenging. Especially, the text extracted from audio-visual content lack linguistic features that potentially provide important clues for text analysis components. Thus, there is a need to develop various techniques to meet the requirements and design principles of the frameworks. In our thesis, we explore developing various methods and models satisfying text and speech analysis requirements posed by cross-media analysis frameworks. The developed methods allow the frameworks to extract linguistic knowledge of various types and predict various information such as sentiment and competence. We also attempt to enhance the multilingualism of the frameworks by designing an analysis pipeline that includes speech recognition, transliteration and named entity recognition for Amharic, that also enables the accessibility of Amharic contents on the web more efficiently. The method can potentially be extended to support other under-resourced languages.
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Shepherd, David. "Natural language program analysis combining natural language processing with program analysis to improve software maintenance tools /". Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 176 p, 2007. http://proquest.umi.com/pqdweb?did=1397920371&sid=6&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Ramachandran, Venkateshwaran. "A temporal analysis of natural language narrative text". Thesis, This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-03122009-040648/.

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Li, Wenhui. "Sentiment analysis: Quantitative evaluation of subjective opinions using natural language processing". Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/28000.

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Sentiment Analysis consists of recognizing sentiment orientation towards specific subjects within natural language texts. Most research in this area focuses on classifying documents as positive or negative. The purpose of this thesis is to quantitatively evaluate subjective opinions of customer reviews using a five star rating system, which is widely used on on-line review web sites, and to try to make the predicted score as accurate as possible. Firstly, this thesis presents two methods for rating reviews: classifying reviews by supervised learning methods as multi-class classification does, or rating reviews by using association scores of sentiment terms with a set of seed words extracted from the corpus, i.e. the unsupervised learning method. We extend the feature selection approach used in Turney's PMI-IR estimation by introducing semantic relatedness measures based up on the content of WordNet. This thesis reports on experiments using the two methods mentioned above for rating reviews using the combined feature set enriched with WordNet-selected sentiment terms. The results of these experiments suggest ways in which incorporating WordNet relatedness measures into feature selection may yield improvement over classification and unsupervised learning methods which do not use it. Furthermore, via ordinal meta-classifiers, we utilize the ordering information contained in the scores of bank reviews to improve the performance, we explore the effectiveness of re-sampling for reducing the problem of skewed data, and we check whether discretization benefits the ordinal meta-learning process. Finally, we combine the unsupervised and supervised meta-learning methods to optimize performance on our sentiment prediction task.
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Keller, Thomas Anderson. "Comparison and Fine-Grained Analysis of Sequence Encoders for Natural Language Processing". Thesis, University of California, San Diego, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10599339.

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Most machine learning algorithms require a fixed length input to be able to perform commonly desired tasks such as classification, clustering, and regression. For natural language processing, the inherently unbounded and recursive nature of the input poses a unique challenge when deriving such fixed length representations. Although today there is a general consensus on how to generate fixed length representations of individual words which preserve their meaning, the same cannot be said for sequences of words in sentences, paragraphs, or documents. In this work, we study the encoders commonly used to generate fixed length representations of natural language sequences, and analyze their effectiveness across a variety of high and low level tasks including sentence classification and question answering. Additionally, we propose novel improvements to the existing Skip-Thought and End-to-End Memory Network architectures and study their performance on both the original and auxiliary tasks. Ultimately, we show that the setting in which the encoders are trained, and the corpus used for training, have a greater influence of the final learned representation than the underlying sequence encoders themselves.

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Patil, Supritha Basavaraj. "Analysis of Moving Events Using Tweets". Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90884.

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The Digital Library Research Laboratory (DLRL) has collected over 3.5 billion tweets on different events for the Coordinated, Behaviorally-Aware Recovery for Transportation and Power Disruptions (CBAR-tpd), the Integrated Digital Event Archiving and Library (IDEAL), and the Global Event Trend Archive Research (GETAR) projects. The tweet collection topics include heart attack, solar eclipse, terrorism, etc. There are several collections on naturally occurring events such as hurricanes, floods, and solar eclipses. Such naturally occurring events are distributed across space and time. It would be beneficial to researchers if we can perform a spatial-temporal analysis to test some hypotheses, and to find any trends that tweets would reveal for such events. I apply an existing algorithm to detect locations from tweets by modifying it to work better with the type of datasets I work with. I use the time captured in tweets and also identify the tense of the sentences in tweets to perform the temporal analysis. I build a rule-based model for obtaining the tense of a tweet. The results from these two algorithms are merged to analyze naturally occurring moving events such as solar eclipses and hurricanes. Using the spatial-temporal information from tweets, I study if tweets can be a relevant source of information in understanding the movement of the event. I create visualizations to compare the actual path of the event with the information extracted by my algorithms. After examining the results from the analysis, I noted that Twitter can be a reliable source to identify places affected by moving events almost immediately. The locations obtained are at a more detailed level than in news-wires. We can also identify the time that an event affected a particular region by date.
Master of Science
News now travels faster on social media than through news channels. Information from social media can help retrieve minute details that might not be emphasized in news. People tend to describe their actions or sentiments in tweets. I aim at studying if such collections of tweets are dependable sources for identifying paths of moving events. In events like hurricanes, using Twitter can help in analyzing people’s reaction to such moving events. These may include actions such as dislocation or emotions during different phases of the event. The results obtained in the experiments concur with the actual path of the events with respect to the regions affected and time. The frequency of tweets increases during event peaks. The number of locations affected that are identified are significantly more than in news wires.
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Giménez, Fayos María Teresa. "Natural Language Processing using Deep Learning in Social Media". Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/172164.

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[ES] En los últimos años, los modelos de aprendizaje automático profundo (AP) han revolucionado los sistemas de procesamiento de lenguaje natural (PLN). Hemos sido testigos de un avance formidable en las capacidades de estos sistemas y actualmente podemos encontrar sistemas que integran modelos PLN de manera ubicua. Algunos ejemplos de estos modelos con los que interaccionamos a diario incluyen modelos que determinan la intención de la persona que escribió un texto, el sentimiento que pretende comunicar un tweet o nuestra ideología política a partir de lo que compartimos en redes sociales. En esta tesis se han propuestos distintos modelos de PNL que abordan tareas que estudian el texto que se comparte en redes sociales. En concreto, este trabajo se centra en dos tareas fundamentalmente: el análisis de sentimientos y el reconocimiento de la personalidad de la persona autora de un texto. La tarea de analizar el sentimiento expresado en un texto es uno de los problemas principales en el PNL y consiste en determinar la polaridad que un texto pretende comunicar. Se trata por lo tanto de una tarea estudiada en profundidad de la cual disponemos de una vasta cantidad de recursos y modelos. Por el contrario, el problema del reconocimiento de personalidad es una tarea revolucionaria que tiene como objetivo determinar la personalidad de los usuarios considerando su estilo de escritura. El estudio de esta tarea es más marginal por lo que disponemos de menos recursos para abordarla pero que no obstante presenta un gran potencial. A pesar de que el enfoque principal de este trabajo fue el desarrollo de modelos de aprendizaje profundo, también hemos propuesto modelos basados en recursos lingüísticos y modelos clásicos del aprendizaje automático. Estos últimos modelos nos han permitido explorar las sutilezas de distintos elementos lingüísticos como por ejemplo el impacto que tienen las emociones en la clasificación correcta del sentimiento expresado en un texto. Posteriormente, tras estos trabajos iniciales se desarrollaron modelos AP, en particular, Redes neuronales convolucionales (RNC) que fueron aplicadas a las tareas previamente citadas. En el caso del reconocimiento de la personalidad, se han comparado modelos clásicos del aprendizaje automático con modelos de aprendizaje profundo, pudiendo establecer una comparativa bajo las mismas premisas. Cabe destacar que el PNL ha evolucionado drásticamente en los últimos años gracias al desarrollo de campañas de evaluación pública, donde múltiples equipos de investigación comparan las capacidades de los modelos que proponen en las mismas condiciones. La mayoría de los modelos presentados en esta tesis fueron o bien evaluados mediante campañas de evaluación públicas, o bien emplearon la configuración de una campaña pública previamente celebrada. Siendo conscientes, por lo tanto, de la importancia de estas campañas para el avance del PNL, desarrollamos una campaña de evaluación pública cuyo objetivo era clasificar el tema tratado en un tweet, para lo cual recogimos y etiquetamos un nuevo conjunto de datos. A medida que avanzabamos en el desarrollo del trabajo de esta tesis, decidimos estudiar en profundidad como las RNC se aplicaban a las tareas de PNL. En este sentido, se exploraron dos líneas de trabajo. En primer lugar, propusimos un método de relleno semántico para RNC, que plantea una nueva manera de representar el texto para resolver tareas de PNL. Y en segundo lugar, se introdujo un marco teórico para abordar una de las críticas más frecuentes del aprendizaje profundo, el cual es la falta de interpretabilidad. Este marco busca visualizar qué patrones léxicos, si los hay, han sido aprendidos por la red para clasificar un texto.
[CA] En els últims anys, els models d'aprenentatge automàtic profund (AP) han revolucionat els sistemes de processament de llenguatge natural (PLN). Hem estat testimonis d'un avanç formidable en les capacitats d'aquests sistemes i actualment podem trobar sistemes que integren models PLN de manera ubiqua. Alguns exemples d'aquests models amb els quals interaccionem diàriament inclouen models que determinen la intenció de la persona que va escriure un text, el sentiment que pretén comunicar un tweet o la nostra ideologia política a partir del que compartim en xarxes socials. En aquesta tesi s'han proposats diferents models de PNL que aborden tasques que estudien el text que es comparteix en xarxes socials. En concret, aquest treball se centra en dues tasques fonamentalment: l'anàlisi de sentiments i el reconeixement de la personalitat de la persona autora d'un text. La tasca d'analitzar el sentiment expressat en un text és un dels problemes principals en el PNL i consisteix a determinar la polaritat que un text pretén comunicar. Es tracta per tant d'una tasca estudiada en profunditat de la qual disposem d'una vasta quantitat de recursos i models. Per contra, el problema del reconeixement de la personalitat és una tasca revolucionària que té com a objectiu determinar la personalitat dels usuaris considerant el seu estil d'escriptura. L'estudi d'aquesta tasca és més marginal i en conseqüència disposem de menys recursos per abordar-la però no obstant i això presenta un gran potencial. Tot i que el fouc principal d'aquest treball va ser el desenvolupament de models d'aprenentatge profund, també hem proposat models basats en recursos lingüístics i models clàssics de l'aprenentatge automàtic. Aquests últims models ens han permès explorar les subtileses de diferents elements lingüístics com ara l'impacte que tenen les emocions en la classificació correcta del sentiment expressat en un text. Posteriorment, després d'aquests treballs inicials es van desenvolupar models AP, en particular, Xarxes neuronals convolucionals (XNC) que van ser aplicades a les tasques prèviament esmentades. En el cas de el reconeixement de la personalitat, s'han comparat models clàssics de l'aprenentatge automàtic amb models d'aprenentatge profund la qual cosa a permet establir una comparativa de les dos aproximacions sota les mateixes premisses. Cal remarcar que el PNL ha evolucionat dràsticament en els últims anys gràcies a el desenvolupament de campanyes d'avaluació pública on múltiples equips d'investigació comparen les capacitats dels models que proposen sota les mateixes condicions. La majoria dels models presentats en aquesta tesi van ser o bé avaluats mitjançant campanyes d'avaluació públiques, o bé s'ha emprat la configuració d'una campanya pública prèviament celebrada. Sent conscients, per tant, de la importància d'aquestes campanyes per a l'avanç del PNL, vam desenvolupar una campanya d'avaluació pública on l'objectiu era classificar el tema tractat en un tweet, per a la qual cosa vam recollir i etiquetar un nou conjunt de dades. A mesura que avançàvem en el desenvolupament del treball d'aquesta tesi, vam decidir estudiar en profunditat com les XNC s'apliquen a les tasques de PNL. En aquest sentit, es van explorar dues línies de treball.En primer lloc, vam proposar un mètode d'emplenament semàntic per RNC, que planteja una nova manera de representar el text per resoldre tasques de PNL. I en segon lloc, es va introduir un marc teòric per abordar una de les crítiques més freqüents de l'aprenentatge profund, el qual és la falta de interpretabilitat. Aquest marc cerca visualitzar quins patrons lèxics, si n'hi han, han estat apresos per la xarxa per classificar un text.
[EN] In the last years, Deep Learning (DL) has revolutionised the potential of automatic systems that handle Natural Language Processing (NLP) tasks. We have witnessed a tremendous advance in the performance of these systems. Nowadays, we found embedded systems ubiquitously, determining the intent of the text we write, the sentiment of our tweets or our political views, for citing some examples. In this thesis, we proposed several NLP models for addressing tasks that deal with social media text. Concretely, this work is focused mainly on Sentiment Analysis and Personality Recognition tasks. Sentiment Analysis is one of the leading problems in NLP, consists of determining the polarity of a text, and it is a well-known task where the number of resources and models proposed is vast. In contrast, Personality Recognition is a breakthrough task that aims to determine the users' personality using their writing style, but it is more a niche task with fewer resources designed ad-hoc but with great potential. Despite the fact that the principal focus of this work was on the development of Deep Learning models, we have also proposed models based on linguistic resources and classical Machine Learning models. Moreover, in this more straightforward setup, we have explored the nuances of different language devices, such as the impact of emotions in the correct classification of the sentiment expressed in a text. Afterwards, DL models were developed, particularly Convolutional Neural Networks (CNNs), to address previously described tasks. In the case of Personality Recognition, we explored the two approaches, which allowed us to compare the models under the same circumstances. Noteworthy, NLP has evolved dramatically in the last years through the development of public evaluation campaigns, where multiple research teams compare the performance of their approaches under the same conditions. Most of the models here presented were either assessed in an evaluation task or either used their setup. Recognising the importance of this effort, we curated and developed an evaluation campaign for classifying political tweets. In addition, as we advanced in the development of this work, we decided to study in-depth CNNs applied to NLP tasks. Two lines of work were explored in this regard. Firstly, we proposed a semantic-based padding method for CNNs, which addresses how to represent text more appropriately for solving NLP tasks. Secondly, a theoretical framework was introduced for tackling one of the most frequent critics of Deep Learning: interpretability. This framework seeks to visualise what lexical patterns, if any, the CNN is learning in order to classify a sentence. In summary, the main achievements presented in this thesis are: - The organisation of an evaluation campaign for Topic Classification from texts gathered from social media. - The proposal of several Machine Learning models tackling the Sentiment Analysis task from social media. Besides, a study of the impact of linguistic devices such as figurative language in the task is presented. - The development of a model for inferring the personality of a developer provided the source code that they have written. - The study of Personality Recognition tasks from social media following two different approaches, models based on machine learning algorithms and handcrafted features, and models based on CNNs were proposed and compared both approaches. - The introduction of new semantic-based paddings for optimising how the text was represented in CNNs. - The definition of a theoretical framework to provide interpretable information to what CNNs were learning internally.
Giménez Fayos, MT. (2021). Natural Language Processing using Deep Learning in Social Media [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172164
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Gorrell, Genevieve. "Generalized Hebbian Algorithm for Dimensionality Reduction in Natural Language Processing". Doctoral thesis, Linköping : Department of Computer and Information Science, Linköpings universitet, 2006. http://www.bibl.liu.se/liupubl/disp/disp2006/tek1045s.pdf.

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Marzo, i. Grimalt Núria. "Natural Language Processing Model for Log Analysis to Retrieve Solutions For Troubleshooting Processes". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300042.

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In the telecommunications industry, one of the most time-consuming tasks is troubleshooting and the resolution of Trouble Report (TR) tickets. This task involves the understanding of textual data which can be challenging due to its domain- and company-specific features. The text contains many abbreviations, typos, tables as well as numerical information. This work tries to solve the issue of retrieving solutions for new troubleshooting reports in an automated way by using a Natural Language Processing (NLP) model, in particular Bidirectional Encoder Representations from Transformers (BERT)- based approaches. It proposes a text ranking model that, given a description of a fault, can rank the best possible solutions to that problem using answers from past TRs. The model tackles the trade-off between accuracy and latency by implementing a multi-stage BERT-based architecture with an initial retrieval stage and a re-ranker stage. Having a model that achieves a desired accuracy under a latency constraint allows it to be suited for industry applications. The experiments to evaluate the latency and the accuracy of the model have been performed on Ericsson’s troubleshooting dataset. The evaluation of the proposed model suggest that it is able to retrieve and re-rank solution for TRs with a significant improvement compared to a non-BERT model.
En av de mest tidskrävande uppgifterna inom telekommunikationsindustrin är att felsöka och hitta lösningar till felrapporter (TR). Denna uppgift kräver förståelse av textdata, som försvåras as att texten innehåller företags- och domänspecifika attribut. Texten innehåller typiskt sett många förkortningar, felskrivningar och tabeller blandat med numerisk information. Detta examensarbete ämnar att förenkla inhämtningen av lösningar av nya felsökningar på ett automatiserat sätt med hjälp av av naturlig språkbehandling (NLP), specifikt modeller baserade på dubbelriktad kodrepresentation (BERT). Examensarbetet föreslår en textrankningsmodell som, givet en felbeskrivning, kan rangordna de bästa möjliga lösningarna till felet baserat på tidigare felsökningar. Modellen hanterar avvägningen mellan noggrannhet och fördröjning genom att implementera den dubbelriktade kodrepresentationen i två faser: en initial inhämtningsfas och en omordningsfas. För industrianvändning krävs att modellen uppnår en given noggrannhet med en viss tidsbegränsning. Experimenten för att utvärdera noggrannheten och fördröjningen har utförts på Ericssons felsökningsdata. Utvärderingen visar att den föreslagna modellen kan hämta och omordna data för felsökningar med signifikanta förbättringar gentemot modeller utan dubbelriktad kodrepresentation.
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Mc, Kevitt Paul. "Analysing coherence of intention in natural language dialogue". Thesis, University of Exeter, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303991.

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Libros sobre el tema "Natural language processing analysis"

1

Jones, Karen Sparck. Evaluating natural language processing systems: An analysis and review. Berlin: Springer, 1995.

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Naive semantics for natural language understanding. Boston: Kluwer Academic Publishers, 1988.

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Sabourin, Conrad. Computational text understanding: Natural language programming, argument analysis : bibliography. Montréal: Infolingua, 1994.

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Applied natural language processing and content analysis: Advances in identification, investigation, and resolution. Hershey, PA: Information Science Reference, 2012.

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Minker, Wolfgang. Stochastically-based semantic analysis. New York: Springer Science+Business Media, 1999.

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Minker, Wolfgang. Stochastically-based semantic analysis. Boston: Kluwer Academic, 1999.

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Creswell, Cassandre. Syntactic form and discourse function in natural language generation. New York, NY: Routledge, 2005.

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Syntactic form and discourse function in natural language generation. New York: Routledge, 2004.

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Text generation: Using discourse strategies and focus constraints to generate natural language text. Cambridge [Cambridgeshire]: Cambridge University Press, 1985.

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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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Capítulos de libros sobre el tema "Natural language processing analysis"

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Taylor, Martin M. y David A. Waugh. "Dialogue analysis using layered protocols". En Natural Language Processing, 189–232. Amsterdam: John Benjamins Publishing Company, 2000. http://dx.doi.org/10.1075/nlp.1.05tay.

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Tapsai, Chalermpol, Herwig Unger y Phayung Meesad. "Semantic Analysis". En Thai Natural Language Processing, 85–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56235-9_4.

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Verma, Rakesh M. y David J. Marchette. "Natural Language Processing". En Cybersecurity Analytics, 223–51. Boca Raton, FL : CRC Press, 2020. | Series: Chapman & Hall/CRC data science series: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429326813-8.

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Cutler, Josh y Matt Dickenson. "Case Study: Natural Language Processing". En Textbooks on Political Analysis, 191–204. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36826-5_14.

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Gezici, Gizem y Berrin Yanıkoğlu. "Sentiment Analysis in Turkish". En Turkish Natural Language Processing, 255–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90165-7_12.

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Sarkar, Dipanjan. "Natural Language Processing Basics". En Text Analytics with Python, 1–68. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4354-1_1.

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Nulty, Paul. "Semantic/Content Analysis/Natural Language Processing". En Encyclopedia of Big Data, 1–5. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-32001-4_182-1.

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Filip, Hana, Michael K. Tanenhaus, Greg N. Carlson, Paul D. Allopenna y Joshua Blatt. "Reduced relatives judged hard require constraint-based analyses". En Natural Language Processing, 255–79. Amsterdam: John Benjamins Publishing Company, 2002. http://dx.doi.org/10.1075/nlp.4.14fil.

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Kordoni, Valia y Julia Neu. "Deep Analysis of Modern Greek". En Natural Language Processing – IJCNLP 2004, 674–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30211-7_71.

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Cheong, Paulo, Dawei Song, Peter Bruza y Kam-Fai Wong. "Information Flow Analysis with Chinese Text". En Natural Language Processing – IJCNLP 2004, 100–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30211-7_11.

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Actas de conferencias sobre el tema "Natural language processing analysis"

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Khuman, Arjab Singh, Yingjie Yang y Sifeng Liu. "Grey relational analysis and natural language Processing". En 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS). IEEE, 2015. http://dx.doi.org/10.1109/gsis.2015.7301838.

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Jakkali, Pratibha y T. Tamilarasi. "Automation of outage analysis using natural language processing". En 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2016. http://dx.doi.org/10.1109/rteict.2016.7808012.

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Rashtian, Hootan, Azadeh Hashemi y Leah Macfadyen. "HARNESSING NATURAL LANGUAGE PROCESSING TO SUPPORT CURRICULUM ANALYSIS". En 13th annual International Conference of Education, Research and Innovation. IATED, 2020. http://dx.doi.org/10.21125/iceri.2020.0445.

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Trivedi, Gaurav. "Clinical Text Analysis Using Interactive Natural Language Processing". En IUI'15: IUI'15 20th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2732158.2732162.

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

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Basile, Pierpaolo, Annalina Caputo, Seamus Lawless y Giovanni Semeraro. "Diachronic Analysis of Entities by ExploitingWikipedia Page revisions". En Recent Advances in Natural Language Processing. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/978-954-452-056-4_011.

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Morita, Hajime y Tomoya wakura. "A Fast and Accurate Partially Deterministic Morphological Analysis". En Recent Advances in Natural Language Processing. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/978-954-452-056-4_093.

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Md Shoeb, Abu Awal, Shahab Raji y Gerard de Melo. "EmoTag – Towards an Emotion-Based Analysis of Emojis". En Recent Advances in Natural Language Processing. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/978-954-452-056-4_126.

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Tobaili, Taha, Miriam Fernandez, Harith Alani, Sanaa Sharafeddine, Hazem Hajj y Goran Glavaš. "SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)". En Recent Advances in Natural Language Processing. Incoma Ltd., Shoumen, Bulgaria, 2019. http://dx.doi.org/10.26615/978-954-452-056-4_138.

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Hadiya, Nidhi y Nirali Nanavati. "Indic SentiReview: Natural Language Processing based Sentiment Analysis on major Indian Languages". En 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2019. http://dx.doi.org/10.1109/iccmc.2019.8819786.

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Informes sobre el tema "Natural language processing analysis"

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

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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, junio de 1994. http://dx.doi.org/10.21236/ada290396.

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Murdick, Dewey, Daniel Chou, Ryan Fedasiuk y Emily Weinstein. The Public AI Research Portfolio of China’s Security Forces. Center for Security and Emerging Technology, marzo de 2021. http://dx.doi.org/10.51593/20200057.

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New analytic tools are used in this data brief to explore the public artificial intelligence (AI) research portfolio of China’s security forces. The methods contextualize Chinese-language scholarly papers that claim a direct working affiliation with components of the Ministry of Public Security, People's Armed Police Force, and People’s Liberation Army. The authors review potential uses of computer vision, robotics, natural language processing and general AI research.
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Tratz, Stephen C. Arabic Natural Language Processing System Code Library. Fort Belvoir, VA: Defense Technical Information Center, junio de 2014. http://dx.doi.org/10.21236/ada603814.

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Wilks, Yorick, Michael Coombs, Roger T. Hartley y Dihong Qiu. Active Knowledge Structures for Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, enero de 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), febrero de 2005. http://dx.doi.org/10.2172/15015192.

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

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

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

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

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