Relevant bibliographies by topics / Understanding language

Academic literature on the topic 'Understanding language'

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Published: 25 May 2024

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Journal articles on the topic "Understanding language"

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Stowe, Laurie A., and Marco Haverkort. "Understanding language." Brain and Language 86, no. 1 (July 2003): 1–8. http://dx.doi.org/10.1016/s0093-934x(02)00591-6.

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Hammadou, Joann, G. Brown, K. Malmkjaer, A. Pollitt, and J. Williams. "Language and Understanding." Modern Language Journal 80, no. 2 (1996): 233. http://dx.doi.org/10.2307/328641.

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Barr, Avron. "Natural Language Understanding." AI Magazine 1, no. 1 (July 1, 2017): 5. http://dx.doi.org/10.1609/aimag.v1i1.85.

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This is an excerpt from the Handbook of Artificial Intelligence, a compendium of hundreds of articles about AI ideas, techniques, and programs being prepared at Stanford University by AI researchers and students from across the country. In addition to articles describing the specifics of various AI programming methods, the Handbook contains dozens of overview articles like this one, which attempt to give historical and scientific perspective to work in the different areas of AI research. This article is from the Handbook chapter on natural language understanding. Cross-references to other articles in the handbook have been removed-terms discussed in more detail elsewhere are italicized. Many people have contributed to this chapter, including especially Anne Gardner, James Davidson, and Terry Winograd. Avron Barr and Edward A. Feigenbaum are the Handbook's general editors.
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Francis, W. N., and April M. S. McMahon. "Understanding Language Change." Language 71, no. 3 (September 1995): 600. http://dx.doi.org/10.2307/416231.

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Shute, Sara. "Understanding Language Acquisition." International Studies in Philosophy 29, no. 2 (1997): 125–26. http://dx.doi.org/10.5840/intstudphil199729251.

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Smith, Barry C. "VI—Understanding Language." Proceedings of the Aristotelian Society 92, no. 1 (June 1, 1992): 109–42. http://dx.doi.org/10.1093/aristotelian/92.1.109.

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Paul, Rhea. "Understanding Language Delay." American Journal of Speech-Language Pathology 6, no. 2 (May 1997): 40–49. http://dx.doi.org/10.1044/1058-0360.0602.40.

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Purnell, Larry. "Understanding cultural language." OR Nurse 3, no. 4 (July 2009): 49–52. http://dx.doi.org/10.1097/01.orn.0000357646.14680.fc.

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Musson, Gill, and Laurie Cohen. "Understanding Language Processes." Management Learning 30, no. 1 (March 1999): 27–42. http://dx.doi.org/10.1177/1350507699301003.

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Ye-Yi Wang, Li Deng, and A. Acero. "Spoken language understanding." IEEE Signal Processing Magazine 22, no. 5 (September 2005): 16–31. http://dx.doi.org/10.1109/msp.2005.1511821.

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Dissertations / Theses on the topic "Understanding language"

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Pettit, Dean R. (Dean Reid) 1967. "Understanding language." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/17560.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, February 2003.
Includes bibliographical references (leaves 139-140).
My dissertation concerns the nature of linguistic understanding. A standard view about linguistic understanding is that it is a propositional knowledge state. The following is an instance of this view: given a speaker S and an expression a that means M, S understand a just in case S knows that a means M. I refer to this as the epistemic view of linguistic understanding. The epistemic view would appear to be a mere conceptual truth about linguistic understanding, since it is entailed by the following two claims that themselves seem to be mere conceptual truths: (i) S understands a iff S knows what a means, and-given that a means M-(ii) S knows what a means iff S knows that a means M. I argue, however, that this is not a mere conceptual truth. Contrary to the epistemic view, propositional knowledge of the meaning of a is not necessary for understanding a. I argue that linguistic understanding does not even require belief. My positive proposal is that our understanding of language is typically realized, at least in native speakers, as a perceptual capacity. Evidence from cognitive neuropsychology suggests that our perceptual experience of language comes to us already semantically interpreted. We perceive a speaker's utterance as having content, and it is by perceiving the speaker's utterances as having the right content that we understand what the speaker says. We count as understanding language (roughly) in virtue of having this capacity to understand what speakers say when they use language. This notion of perceiving an utterance as having content gets analyzed in terms of Dretske's account of representation in terms of a teleological notion of function: you perceive a speaker's utterance as having content when the utterance produces in you a perceptual state that has a certain function in your psychology.
(cont.) I show how this view about the nature of linguistic understanding provides an attractive account of how identity claims can be semantically informative, as opposed to merely pragmatically informative, an account that avoids the standard difficulties for Fregean views that attempt to account for the informativeness of identity claims in terms of their semantics.
by Dean R. Pettit.
Ph.D.
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Rudzicz, Frank. "Clavius : understanding language understanding in multimodal interaction." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99536.

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Natural communication between humans is not limited to speech, but often requires simultaneous coordination of multiple streams of information---especially hand gestures---to complement or supplement understanding. This thesis describes a software architecture, called CLAVIUS Whose purpose is to generically interpret multiple modes of input as singular semantic utterances through a modular programming interface that supports various sensing technologies. This interpretation is accomplished through a new multi-threaded parsing algorithm that co-ordinates top-down and bottom-up methods asynchronously on graph-based unification grammars. The interpretation process follows a best-first approach where partial parses are evaluated by a combination of scoring metrics, related to such criteria as information content, grammatical structure and language models. Furthermore, CLAVIUS relaxes two traditional constraints in conventional parsing---namely, it abandons forced relative ordering of right-hand constituents in grammar rules, and it allows parses to be expanded with null constituents.
The effects of this parsing methodology, and of the scoring criteria it employs, are analyzed within the context of experiments and data collection on a small group of users. Both CLAVIUS and its component modules are trained on this data, and results show improvements in performance accuracy, and the overcoming of several difficulties in other multimodal frameworks. General discussion as to the linguistic behaviour of speakers in a multimodal context are also described.
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Eiben, Robert Joseph. "Understanding Dead Languages." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/32798.

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Dead languages present a case where the original language community no longer exists. This results in a language for which the evidence is limited by the paucity of surviving texts and in which no new linguistic uses can be generated. Ludwig Wittgenstein argued that the meaning of language is simply its use by a language community. On this view a dead language is coextensive with the existing corpus, with the linguistic dynamic provided by the community of readers. Donald Davidson argued that the meaning of language is not conventional, but rather is discovered in a dynamic process of â passing theoriesâ generated by the speaker and listener. On this view a dead language is incomplete, because such dynamic theories can only be negotiated by participating in a living language community and are thus not captured by the extant corpus. We agree with Davidsonâ s view of theories of meaning and conclude that our interpretations of dead languages will suffer epistemological underdetermination that removes any guarantee that they reflect the meanings as heard by the original language community.
Master of Arts
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Al-Khonaizi, Mohammed Taqi. "Natural Arabic language text understanding." Thesis, University of Greenwich, 1999. http://gala.gre.ac.uk/6096/.

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The most challenging part of natural language understanding is the representation of meaning. The current representation techniques are not sufficient to resolve the ambiguities, especially when the meaning is to be used for interrogation at a later stage. Arabic language represents a challenging field for Natural Language Processing (NLP) because of its rich eloquence and free word order, but at the same time it is a good platform to capture understanding because of its rich computational, morphological and grammar rules. Among different representation techniques, Lexical Functional Grammar (LFG) theory is found to be best suited for this task because of its structural approach. LFG lays down a computational approach towards NLP, especially the constituent and the functional structures, and models the completeness of relationships among the contents of each structure internally, as well as among the structures externally. The introduction of Artificial Intelligence (AI) techniques, such as knowledge representation and inferencing, enhances the capture of meaning by utilising domain specific common sense knowledge embedded in the model of domain of discourse and the linguistic rules that have been captured from the Arabic language grammar. This work has achieved the following results: (i) It is the first attempt to apply the LFG formalism on a full Arabic declarative text that consists of more than one paragraph. (ii) It extends the semantic structure of the LFG theory by incorporating a representation based on the thematic-role frames theory. (iii) It extends to the LFG theory to represent domain specific common sense knowledge. (iv) It automates the production process of the functional and semantic structures. (v) It automates the production process of domain specific common sense knowledge structure, which enhances the understanding ability of the system and resolves most ambiguities in subsequent question-answer sessions.
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Batsuren, Khuyagbaatar. "Understanding and Exploiting Language Diversity." Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/368635.

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Languages are well known to be diverse on all structural levels, from the smallest (phonemic) to the broadest (pragmatic). We propose a set of formal, quantitative measures for the language diversity of linguistic phenomena, the resource incompleteness, and resource incorrectness. We apply all these measures to lexical semantics where we show how evidence of a high degree of universality within a given language set can be used to extend lexico-semantic resources in a precise, diversity-aware manner. We demonstrate our approach on several case studies: First is on polysemes and homographs among cases of lexical ambiguity. Contrarily to past research that focused solely on exploiting systematic polysemy, the notion of universality provides us with an automated method also capable of predicting irregular polysemes. Second is to automatically identify cognates from the existing lexical resource across different orthographies of genetically unrelated languages. Contrarily to past research that focused on detecting cognates from 225 concepts of Swadesh list, we captured 3.1 million cognate pairs across 40 different orthographies and 335 languages by exploiting the existing wordnet-like lexical resources.
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Batsuren, Khuyagbaatar. "Understanding and Exploiting Language Diversity." Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/3451/1/disclaimer_batsuren.pdf.

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Languages are well known to be diverse on all structural levels, from the smallest (phonemic) to the broadest (pragmatic). We propose a set of formal, quantitative measures for the language diversity of linguistic phenomena, the resource incompleteness, and resource incorrectness. We apply all these measures to lexical semantics where we show how evidence of a high degree of universality within a given language set can be used to extend lexico-semantic resources in a precise, diversity-aware manner. We demonstrate our approach on several case studies: First is on polysemes and homographs among cases of lexical ambiguity. Contrarily to past research that focused solely on exploiting systematic polysemy, the notion of universality provides us with an automated method also capable of predicting irregular polysemes. Second is to automatically identify cognates from the existing lexical resource across different orthographies of genetically unrelated languages. Contrarily to past research that focused on detecting cognates from 225 concepts of Swadesh list, we captured 3.1 million cognate pairs across 40 different orthographies and 335 languages by exploiting the existing wordnet-like lexical resources.
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Williams, Clive Richard. "ATLAS : a natural language understanding system." Thesis, University of Bristol, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320139.

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Marlen, Michael Scott. "An approach to Natural Language understanding." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/17581.

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Doctor of Philosophy
Department of Computing and Information Sciences
David A. Gustafson
Natural Language understanding over a set of sentences or a document is a challenging problem. We approach this problem using semantic extraction and an ontology for answering questions based on the data. There is more information in a sentence than that found by extracting out the visible terms and their obvious relations between one another. It is the hidden information that is not seen that gives this solution the advantage over alternatives. This methodology was tested against the FraCas Test Suite with near perfect results (correct answers) for the sections that are the focus of this paper (Generalized Quantifiers, Plurals, Adjectives, Comparatives, Verbs, and Attitudes). The results indicate that extracting the visible semantics as well as the unseen semantics and their interrelations using an ontology to reason over it provides reliable and provable answers to questions validating this technology.
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Swain, Bradley Andrew. "Path understanding using geospatial natural language." [Pensacola, Fla.] : University of West Florida, 2009. http://purl.fcla.edu/fcla/etd/WFE0000182.

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Thesis (M.S.)--University of West Florida, 2009.
Submitted to the Dept. of Computer Science. Title from title page of source document. Document formatted into pages; contains 45 pages. Includes bibliographical references.
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Autayeu, Aliaksandr. "Descriptive Phrases: Understanding Natural Language Metadata." Doctoral thesis, Università degli studi di Trento, 2010. https://hdl.handle.net/11572/368353.

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Fast development of information and communication technologies made available vast amounts of heterogeneous information. With these amounts growing faster and faster, information integration and search technologies are becoming a key for the success of information society. To handle such amounts efficiently, data needs to be leveraged and analysed at deep levels. Metadata is a traditional way of getting leverage over the data. Deeper levels of analysis include language analysis, starting from purely string-based (keyword) approaches, continuing with syntactic-based approaches and now semantics is about to be included in the processing loop. Metadata gives a leverage over the data. Often a natural language, being the easiest way of expression, is used in metadata. We call such metadata ``natural language metadata''. The examples include various titles, captions and labels, such as web directory labels, picture titles, classification labels, business directory category names. These short pieces of text usually describe (sets of) objects. We call them ``descriptive phrases''. This thesis deals with a problem of understanding natural language metadata for its further use in semantics aware applications. This thesis contributes by portraying descriptive phrases, using the results of analysis of several collected and annotated datasets of natural language metadata. It provides an architecture for the natural language metadata understanding, complete with the algorithms and the implementation. This thesis contains the evaluation of the proposed architecture.
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Books on the topic "Understanding language"

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Perera, Katharine. Understanding language. (Warwick): National Association of Advisers in English, 1987.

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Burridge, Kate. Understanding Language Change. Milton Park, Abingdon, Oxon; New York, NY: Routledge, [2017] | Series:: Routledge, 2016. http://dx.doi.org/10.4324/9781315463018.

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Natural language understanding. Menlo Park, Calif: Benjamin/Cummings Pub. Co., 1987.

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Tur, Gokhan, and Renato De Mori, eds. Spoken Language Understanding. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119992691.

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Understanding language change. Cambridge: Cambridge University Press, 1994.

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Natural language understanding. 2nd ed. Redwood City, Calif: Benjamin/Cummings Pub. Co., 1995.

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Understanding language testing. London: Hodder Education, 2009.

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Evolutionary language understanding. London: Cassell, 1996.

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Natural language understanding. 2nd ed. Menlo Park, Calif: Benjamin/Cummings Pub. Co., 1994.

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Douglas, Dan. Understanding language testing. London: Hodder Education, 2009.

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Book chapters on the topic "Understanding language"

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Fowler, Roger. "Language." In Understanding Language, 1–18. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003267836-1.

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Thompson, Neil. "Understanding language." In Effective Communication, 38–64. London: Macmillan Education UK, 2011. http://dx.doi.org/10.1007/978-1-137-28528-7_3.

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Lewis, Moira, Courtenay Norbury, Rhiannon Luyster, Lauren Schmitt, Andrea McDuffie, Eileen Haebig, Donna S. Murray, et al. "Language Understanding." In Encyclopedia of Autism Spectrum Disorders, 1697. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_100786.

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Weik, Martin H. "language understanding." In Computer Science and Communications Dictionary, 871. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_9932.

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Thompson, Neil. "Understanding Language." In Effective Communication, 40–68. London: Macmillan Education UK, 2018. http://dx.doi.org/10.1057/978-1-352-00223-2_3.

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McNeill, Patrick. "Understanding Language." In Society Today 2, 24–26. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-12065-9_8.

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Fowler, Roger. "Learning language." In Understanding Language, 189–211. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003267836-9.

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Thornton, Stephanie. "Language." In Understanding Human Development, 177–217. London: Macmillan Education UK, 2008. http://dx.doi.org/10.1007/978-1-137-29449-4_5.

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Fowler, Roger. "Uses of language." In Understanding Language, 240–58. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003267836-11.

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Fowler, Roger. "Words within sentences." In Understanding Language, 70–95. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003267836-4.

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Conference papers on the topic "Understanding language"

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Chen, Stanley F., Abhinav Sethy, and Bhuvana Ramabhadran. "Pruning exponential language models." In Understanding (ASRU). IEEE, 2011. http://dx.doi.org/10.1109/asru.2011.6163937.

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Chen, Stanley F., Lidia Mangu, Bhuvana Ramabhadran, Ruhi Sarikaya, and Abhinav Sethy. "Scaling shrinkage-based language models." In Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373380.

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Xu, Puyang, Sanjeev Khudanpur, and Asela Gunawardana. "Randomized maximum entropy language models." In Understanding (ASRU). IEEE, 2011. http://dx.doi.org/10.1109/asru.2011.6163935.

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Quarteroni, Silvia, Marco Dinarelli, and Giuseppe Riccardi. "Ontology-based grounding of Spoken Language Understanding." In Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373500.

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Rastrow, Ariya, Abhinav Sethy, and Bhuvana Ramabhadran. "Constrained discriminative training of N-gram language models." In Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373338.

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Xu, Puyang, Damianos Karakos, and Sanjeev Khudanpur. "Self-supervised discriminative training of statistical language models." In Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373401.

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Schultz, Tanja. "Rapid language adaptation tools for multilingual speech processing." In Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373503.

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Rastrow, Ariya, Mark Dredze, and Sanjeev Khudanpur. "Efficient discriminative training of long-span language models." In Understanding (ASRU). IEEE, 2011. http://dx.doi.org/10.1109/asru.2011.6163933.

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Shi, Yangyang, Pascal Wiggers, and Catholijn M. Jonker. "Socio-situational setting classification based on language use." In Understanding (ASRU). IEEE, 2011. http://dx.doi.org/10.1109/asru.2011.6163974.

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Cui, Jia, Yonggang Deng, and Bowen Zhou. "Reinforcing language model for speech translation with auxiliary data." In Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373308.

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Reports on the topic "Understanding language"

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Ksiezyk, Tomasz, and Ralph Grishman. Equipment Simulation for Language Understanding, Revision. Fort Belvoir, VA: Defense Technical Information Center, June 1988. http://dx.doi.org/10.21236/ada203608.

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Batges, Madeleine, Robert Bobrow, Robert Ingria, and David Stallard. The Delphi Natural Language Understanding System. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada457479.

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Martin, James H. Computer Understanding of Conventional Metaphoric Language. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada461663.

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Yanco, Holly A., Hadas Kress-Gazit, Holly Yanco, Daniel J. Brooks, Constantine Lignos, Cameron Finucane, Kenton Lee, et al. SUBTLE: Situation Understanding Bot through Language and Environment. Fort Belvoir, VA: Defense Technical Information Center, January 2016. http://dx.doi.org/10.21236/ad1006698.

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Moldovan, Dan. Inducing Ontologies from Folksonomies using Natural Language Understanding. Fort Belvoir, VA: Defense Technical Information Center, November 2009. http://dx.doi.org/10.21236/ada512325.

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Hirschman, Lynette, Stephanie Seneff, David Goodine, and Michael Phillips. Integrating Syntax and Semantics into Spoken Language Understanding. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada460560.

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Rowe, Neil C. Understanding of Navy Technical Language via Statistical Parsing. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada465750.

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Evens, Martha W. Language Understanding and Generation of Complex Tutorial Dialogues. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada384733.

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Sidner, C. Research in Knowledge Representation for Natural Language Understanding. Fort Belvoir, VA: Defense Technical Information Center, February 1985. http://dx.doi.org/10.21236/ada152260.

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Ward, Wayne, and Sunil Issar. Recent Improvements in the CMU Spoken Language Understanding System. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada458062.

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