Academic literature on the topic 'Parts of speech'
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Journal articles on the topic "Parts of speech"
Ansaldo, Umberto, Jan Don, and Roland Pfau. "Parts of Speech." Parts of Speech: Descriptive tools, theoretical constructs 32, no. 3 (September 3, 2008): 505–8. http://dx.doi.org/10.1075/sl.32.3.02ans.
Full textMirsky, Steve. "Parts of Speech." Scientific American 286, no. 2 (February 2002): 28. http://dx.doi.org/10.1038/scientificamerican0202-28b.
Full textPamela, Z. "Parts of Speech." Theater 30, no. 2 (January 1, 2000): 59–64. http://dx.doi.org/10.1215/01610775-30-2-59.
Full textSzabó, Zoltán Gendler. "Major Parts of Speech." Erkenntnis 80, S1 (September 18, 2014): 3–29. http://dx.doi.org/10.1007/s10670-014-9658-1.
Full textWang, Chenguang. "Transitory speech parts recognition." Speech Communication 7, no. 1 (March 1988): 98. http://dx.doi.org/10.1016/0167-6393(88)90026-x.
Full textNir, Bracha, and Ruth A. Berman. "Parts of speech as constructions." Constructions and Frames 2, no. 2 (December 31, 2010): 242–74. http://dx.doi.org/10.1075/cf.2.2.05nir.
Full textİbrahim DELİCE, H. "How Must Parts Of Speech Categorize?" Journal of Turkish Studies Volume 7 Issue 4-I, no. 7 (2012): 27–34. http://dx.doi.org/10.7827/turkishstudies.4257.
Full textKhoury, Richard. "Sentence Clustering Using Parts-of-Speech." International Journal of Information Engineering and Electronic Business 4, no. 1 (February 27, 2012): 1–9. http://dx.doi.org/10.5815/ijieeb.2012.01.01.
Full textXu, Ming, Zongzhi Wu, and Yun Luo. "Parts of Speech of ligAnQuanli\g." Journal of Risk Analysis and Crisis Response 4, no. 2 (2014): 108. http://dx.doi.org/10.2991/jrarc.2014.4.2.6.
Full textSadock, Jerrold M. "Parts of Speech in Autolexical Syntax." Annual Meeting of the Berkeley Linguistics Society 16, no. 1 (August 25, 1990): 269. http://dx.doi.org/10.3765/bls.v16i0.1709.
Full textDissertations / Theses on the topic "Parts of speech"
Miller, Barbara L. "Grammar Efficiency of Parts-of-Speech Systems." Kent State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=kent1300373267.
Full textSchutte, Kenneth Thomas 1979. "Parts-based models and local features for automatic speech recognition." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53301.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 101-108).
While automatic speech recognition (ASR) systems have steadily improved and are now in widespread use, their accuracy continues to lag behind human performance, particularly in adverse conditions. This thesis revisits the basic acoustic modeling assumptions common to most ASR systems and argues that improvements to the underlying model of speech are required to address these shortcomings. A number of problems with the standard method of hidden Markov models (HMMs) and features derived from fixed, frame-based spectra (e.g. MFCCs) are discussed. Based on these problems, a set of desirable properties of an improved acoustic model are proposed, and we present a "parts-based" framework as an alternative. The parts-based model (PBM), based on previous work in machine vision, uses graphical models to represent speech with a deformable template of spectro-temporally localized "parts", as opposed to modeling speech as a sequence of fixed spectral profiles. We discuss the proposed model's relationship to HMMs and segment-based recognizers, and describe how they can be viewed as special cases of the PBM. Two variations of PBMs are described in detail. The first represents each phonetic unit with a set of time-frequency (T-F) "patches" which act as filters over a spectrogram. The model structure encodes the patches' relative T-F positions. The second variation, referred to as a "speech schematic" model, more directly encodes the information in a spectrogram by using simple edge detectors and focusing more on modeling the constraints between parts.
(cont.) We demonstrate the proposed models on various isolated recognition tasks and show the benefits over baseline systems, particularly in noisy conditions and when only limited training data is available. We discuss efficient implementation of the models and describe how they can be combined to build larger recognition systems. It is argued that the flexible templates used in parts-based modeling may provide a better generative model of speech than typical HMMs.
by Kenneth Thomas Schutte.
Ph.D.
Mainzer, Jacob Emil. "Labeling Parts of Speech Using Untrained Annotators on Mechanical Turk." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322708732.
Full textBeck, David. "The typology of parts of speech systems, the markedness of adjectives." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ45730.pdf.
Full textParadis, Michel. "Speech in parts : understanding and modelling the semantic differences between words." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.568502.
Full textRobinson, Tyler. "Disaster tweet classification using parts-of-speech tags: a domain adaptation approach." Thesis, Kansas State University, 2016. http://hdl.handle.net/2097/34531.
Full textDepartment of Computer Science
Doina Caragea
Twitter is one of the most active social media sites today. Almost everyone is using it, as it is a medium by which people stay in touch and inform others about events in their lives. Among many other types of events, people tweet about disaster events. Both man made and natural disasters, unfortunately, occur all the time. When these tragedies transpire, people tend to cope in their own ways. One of the most popular ways people convey their feelings towards disaster events is by offering or asking for support, providing valuable information about the disaster, and voicing their disapproval towards those who may be the cause. However, not all of the tweets posted during a disaster are guaranteed to be useful or informative to authorities nor to the general public. As the number of tweets that are posted during a disaster can reach the hundred thousands range, it is necessary to automatically distinguish tweets that provide useful information from those that don't. Manual annotation cannot scale up to the large number of tweets, as it takes significant time and effort, which makes it unsuitable for real-time disaster tweet annotation. Alternatively, supervised machine learning has been traditionally used to learn classifiers that can quickly annotate new unseen tweets. But supervised machine learning algorithms make use of labeled training data from the disaster of interest, which is presumably not available for a current target disaster. However, it is reasonable to assume that some amount of labeled data is available for a prior source disaster. Therefore, domain adaptation algorithms that make use of labeled data from a source disaster to learn classifiers for the target disaster provide a promising direction in the area of tweet classification for disaster management. In prior work, domain adaptation algorithms have been trained based on tweets represented as bag-of-words. In this research, I studied the effect of Part of Speech (POS) tag unigrams and bigrams on the performance of the domain adaptation classifiers. Specifically, I used POS tag unigram and bigram features in conjunction with a Naive Bayes Domain Adaptation algorithm to learn classifiers from source labeled data together with target unlabeled data, and subsequently used the resulting classifiers to classify target disaster tweets. The main research question addressed through this work was if the POS tags can help improve the performance of the classifiers learned from tweet bag-of-words representations only. Experimental results have shown that the POS tags can improve the performance of the classifiers learned from words only, but not always. Furthermore, the results of the experiments show that POS tag bigrams contain more information as compared to POS tag unigrams, as the classifiers learned from bigrams have better performance than those learned from unigrams.
Seidler, Christopher Fabian. "Utterance- and phrase-initial parts of speech in German interactions and textbooks." Kansas State University, 2015. http://hdl.handle.net/2097/20549.
Full textDepartment of Modern Languages
Janice McGregor
The current study investigates phrase-initial parts of speech as found in intermediate German textbooks and compares these findings to utterance-initial parts of speech as found in spontaneous speech in German-language interactions. This is important, because learning and using German word order appears to be a struggle for German learners whose first language is English. Research has shown that possible word order realizations in a language are partly restricted by the parts of speech system of that language (Hengeveld, Rijkhoff, & Siewierska, 2004; Vulanovic & Köhler, 2009). This is important because English and German have different parts of speech systems (Hengeveld et. al., 2004; Hengeveld & van Lier, 2010). Doherty (2005) analyzed English to German translations of an international science magazine and found that almost every second sentence begins differently. Instead, this study looks at talk in contexts of use and compares these findings with textbook language because, in recent years, communicative approaches to language teaching have been adopted by a large number of US German language programs. One would thus expect that textbooks used in these classrooms would contain at least some input with constructions that are typical to contexts of use. The results of the study indicate that construction-initial parts of speech in textbooks and in contexts of use are quite different. These differences imply that if it is a communicative approach that is being promoted, textbook authors and German educators would do well to expose students to actual talk from contexts of use so that they might learn to make meaning based on considerations of context.
鄭佩芳 and Pui-fong Cheng. "A study on parts of speech, word formation, and the change of word meaning in modern Chinese." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31234124.
Full textCorey, Vicka Rael. "The electrophysiological difference between nouns and verbs /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/9092.
Full textDezotti, Lucas Consolin. "Arte menor e Arte maior de Donato: tradução, anotação e estudo introdutório." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/8/8143/tde-22092011-161749/.
Full textThis dissertation aims to bring two contributions to the historiography of linguistic thought. The first is a complete and annotated unprecedented translation into Portuguese of Ars Donati, one of the most influential grammatical treatises produced by Greco-Roman culture. The second is an introductory presentation concerning the parts of speech, core of ancient grammatical doctrine and ancestors of our word classes. Ancient sources and recent studies guide the investigation of emergence and establishment of this doctrine in classical antiquity, by the way of a comparative study that seeks evidences of possible influence between dialectics (Plato, Aristotle, Stoics) and grammar as regards the criteria for analysis and classification of linguistic data.
Books on the topic "Parts of speech"
Ansaldo, Umberto, Jan Don, and Roland Pfau, eds. Parts of Speech. Amsterdam: John Benjamins Publishing Company, 2010. http://dx.doi.org/10.1075/bct.25.
Full textCheung, Candice Chi-Hang. Parts of Speech in Mandarin. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0398-1.
Full textMcNeal, Drema. Jake learns all 8 parts of speech. Terra Alta, W.V: Headline Books, 2010.
Find full textL, Gibbs D., Angle Scott ill, and Chandler Jeff ill, eds. Grammar all-stars: The parts of speech. Pleasantville, NY: Gareth Stevens Pub., 2008.
Find full textSaunders-Smith, Phd Gail, Jennifer Fandel, and Sheri Doyle. Parts of Speech. Pebble Books, 2013.
Find full textBook chapters on the topic "Parts of speech"
Harrison, Mark, Vanessa Jakeman, and Ken Paterson. "Parts of speech." In Improve Your Grammar, 2–3. London: Macmillan Education UK, 2012. http://dx.doi.org/10.1007/978-1-137-27240-9_2.
Full textHarrison, Mark, Vanessa Jakeman, and Ken Paterson. "Parts of speech." In Improve Your Grammar, 4–5. London: Macmillan Education UK, 2017. http://dx.doi.org/10.1057/978-1-137-39030-1_2.
Full textHengeveld, Kees. "Parts of Speech." In Layered Structure and Reference in a Functional Perspective, 29. Amsterdam: John Benjamins Publishing Company, 1992. http://dx.doi.org/10.1075/pbns.23.04hen.
Full textBender, Emily M. "Parts of speech." In Linguistic Fundamentals for Natural Language Processing, 57–60. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-031-02150-3_6.
Full textDraze, Dianne, and Mary Lou Johnson. "Parts of Speech." In Red Hot Root Words, 27. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003237679-7.
Full textAlbert, Tim. "The parts of speech." In Write effectively, 107–8. London: CRC Press, 2021. http://dx.doi.org/10.1201/9780429183874-17.
Full textCamp, Gregory. "Other Parts of Speech." In Linguistics for Singers, 95–105. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003320753-11.
Full textde Brauw, Michael. "The Parts of the Speech." In A Companion to Greek Rhetoric, 185–202. Oxford, UK: Blackwell Publishing Ltd, 2007. http://dx.doi.org/10.1002/9780470997161.ch13.
Full textRui, Guo. "Criteria for classifying parts of speech." In Modern Chinese Parts of Speech, 107–27. London ; New York, NY : Routledge, 2019. |: Routledge, 2019. http://dx.doi.org/10.4324/9781351269209-5.
Full textNugues, Pierre M. "Words, Parts of Speech, and Morphology." In Language Processing with Perl and Prolog, 169–203. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41464-0_6.
Full textConference papers on the topic "Parts of speech"
L R, Swaroop, Rakshit Gowda G S, Shriram Hegde, and Sourabh U. "Parts of Speech Tagging for Kannada." In Student Research Workshop Associated with RANLP 2019. Incoma Ltd., 2019. http://dx.doi.org/10.26615/issn.2603-2821.2019_005.
Full textKumar, S. Suresh, and S. Ashok Kumar. "Parts of Speech Disambiguation in Telugu." In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.78.
Full textKanakaraddi, Suvarna G., and Suvarna S. Nandyal. "Survey on Parts of Speech Tagger Techniques." In 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT). IEEE, 2018. http://dx.doi.org/10.1109/icctct.2018.8550884.
Full textSajjad, Hassan, and Helmut Schmid. "Tagging Urdu text with parts of speech." In the 12th Conference of the European Chapter of the Association for Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1609067.1609144.
Full textPrabhu Khorjuvenkar, Diksha N., Megha Ainapurkar, and Sufola Chagas. "PARTS OF SPEECH TAGGING FOR KONKANI LANGUAGE." In 2018 Second International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2018. http://dx.doi.org/10.1109/iccmc.2018.8487620.
Full textSchulman, Alan, and Salvador Barbosa. "Text Genre Classification Using only Parts of Speech." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00236.
Full textTanawongsuwan, Patrawadee. "Product review sentiment classification using parts of speech." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5563883.
Full textBasumatary, Bedawati, Mirzanur Rahman, Shikhar Kr Sarma, Parvez Aziz Boruah, and Kuwali Talukdar. "Deep Learning Based Bodo Parts of Speech Tagger." In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2023. http://dx.doi.org/10.1109/icccnt56998.2023.10308365.
Full textJoshi, Aravind K., and B. Srinivas. "Disambiguation of super parts of speech (or supertags)." In the 15th conference. Morristown, NJ, USA: Association for Computational Linguistics, 1994. http://dx.doi.org/10.3115/991886.991912.
Full textIsmail, Sabir, M. Shahidur Rahman, and Md Abdullah Al Mumin. "Developing an automated Bangla parts of speech tagged dictionary." In 2013 16th International Conference on Computer and Information Technology (ICCIT). IEEE, 2014. http://dx.doi.org/10.1109/iccitechn.2014.6997347.
Full textReports on the topic "Parts of speech"
Diesner, Jana, and Kathleen M. Carley. Looking Under the Hood of Stochastic Machine Learning Algorithms for Parts of Speech Tagging. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada487511.
Full textKuzmina, Aleksandra, Amalia Kuregyan, and Ekaterina Pertsevaya. PSUDOINTERNATIONAL WORDS IN THE TRANSLATION OF ECONOMIC TEXTS CARRIED OUT BY THE STUDENTS OF NON-LINGUISTIC UNIVERSITIES. Crimean Federal University named after V.I. Vernadsky, 2023. http://dx.doi.org/10.12731/ttxnbz.
Full textLarina, E. Speech therapy examination of children with impaired violation disorder, rate of speech, stutterinq: еducational methodical manual. SIB-Expertise, December 2022. http://dx.doi.org/10.12731/er0662.15122022.
Full textChew, Peter A., Brett William Bader, and Alla Rozovskaya. Using DEDICOM for completely unsupervised part-of-speech tagging. Office of Scientific and Technical Information (OSTI), February 2009. http://dx.doi.org/10.2172/978915.
Full textAminzadeh, A. R., and Wade Shen. Low-Resource Speech Translation of Urdu to English Using Semi-Supervised Part-of-Speech Tagging and Transliteration. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada519247.
Full textGimpel, Kevin, Nathan Schneider, Brendan O'Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, and Noah A. Smith. Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada547371.
Full textDunlavy, Daniel, and Peter A. Chew. Constrained Versions of DEDICOM for Use in Unsupervised Part-Of-Speech Tagging. Office of Scientific and Technical Information (OSTI), May 2016. http://dx.doi.org/10.2172/1254278.
Full textWallace, Alan. Adjustable Speed Drive Study, Part 2. Office of Scientific and Technical Information (OSTI), August 1989. http://dx.doi.org/10.2172/5485631.
Full textBrown, Henry, Samuel Labi, and Andrzej Tarko. A Tool for Evaluating Access Control on High Speed Urban Arterials, Part I. West Lafayette, IN: Purdue University, 1998. http://dx.doi.org/10.5703/1288284313131.
Full textBäumler, Maximilian, Madlen Ringhand, Christian Siebke, Marcus Mai, Felix Elrod, and Günther Prokop. Report on validation of the stochastic traffic simulation (Part B). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.243.
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