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Topic: Statistical semantics

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Automatic Labeling of Semantic Roles - Gildea, Jurafsky (ResearchIndex) 7.0%: Statistical Language Understanding Using Frame Semantics - Gildea (2001) The research outlined here aims to develop a statistical approach to the problem, extending the methodology that has been very successful in statistical parsing one step closer to language understanding. Both specific and more abstract semantic roles are considered in an effort to generalize from semantic frames for which training data is available to... citeseer.ist.psu.edu /333834.html   (616 words)

Automatic Labeling of Semantic Roles - Gildea, Jurafsky (ResearchIndex) 7.0%: Statistical Language Understanding Using Frame Semantics - Gildea (2001) The research outlined here aims to develop a statistical approach to the problem, extending the methodology that has been very successful in statistical parsing one step closer to language understanding. Both specific and more abstract semantic roles are considered in an effort to generalize from semantic frames for which training data is available to... citeseer.ist.psu.edu /333834.html   (616 words)

Lexical Semantics Bibliography The lexical semantics of verbs of motion: The perspective from unaccusativity. Integrating symbolic and statistical representations: The lexicon pragmatic interface. Thus children are not invariably conservative but show conservative tendencies, and their generalizations are influenced by morphophonological and semantic criteria. dingo.sbs.arizona.edu /~hharley/522/522Spring1999/LexSemBiblio.html   (2342 words)

ongoing · On Semantics and Markup Eventually I co-founded Open Text and did search engines and drifted into the SGML community, and was nervous about the notion of semantics as early as 1992; a certain proportion of that community asserted that SGML markup was semantic and that the semantics came from the DTD. The first time I saw this happen was a dozen years ago on the dictionary project, when we started running statistical analysis of the millions of supporting quotations used to illustrate word usages to track temporal patterns in the arrival into and departure from the language of English words. Schemas · The existence of a schema (of whatever flavour) is neither a necessary nor a sufficient condition for attaching semantics to markup. www.tbray.org /ongoing/When/200x/2003/04/09/SemanticMarkup   (1579 words)

Bonnie J. Dorr's Publications Ayan, Fazil, Bonnie J. Dorr, and Nizar Habash, "Application of Alignment to Real-World Data: Combining Linguistic and Statistical Techniques for Adaptable MT", in Proceedings of the 6th Conference of the Association for Machine Translation in the Americas (AMTA-2004), Georgetown University, Washington DC, 2004. "Semantic Annotation and Lexico-Syntactic Paraphrase", in Proceedings of the Workshop on Building Lexical Resources from Semantically Annotated Corpora, LREC, Portugal, 2004. Dorr, Bonnie J. and Douglas Jones, "Acquisition of Semantic Lexicons: Using Word Sense Disambiguation to Improve Precision," Proceedings of the Workshop on Breadth and Depth of Semantic Lexicons, 34th Annual Conference of the Association for Computational Linguistics, Santa Cruz, CA, pp. www.umiacs.umd.edu /~bonnie/publications.html   (1579 words)

Magazine / Science & Technology For complex systems, both Linguistic and Statistical systems sometimes use semantic frames to limit the scope of probable input and simplify the models used for each frame. The semantic modeling minimizes the development required to move from one language to another and even have support for more than one language at a time or a mix of languages (e.g., English and French). This simplification becomes more important when the input modality is at risk of losing accuracy, such as in systems that use speech recognition as their main input modality. www.siliconiran.com /magazine/science_technology/issue3.shtml   (1554 words)

Contextually-Dependent Lexical Semantics - Verspoor (ResearchIndex) Abstract: This thesis is an investigation of phenomena at the interface between syntax, semantics, and pragmatics, with the aim of arguing for a view of semantic interpretation as lexicallydriven yet contextually dependent. @misc{ verspoor97contextuallydependent, author = "C. Verspoor", title = "Contextually-Dependent Lexical Semantics", text = "Verspoor, C. Contextually-Dependent Lexical Semantics. 0.1 : Exploiting Context to Identify Lexical Atoms - A Statistical View.. citeseer.ist.psu.edu /60115.html   (1554 words)

L645: Lexical Semantics Co-occurrence "semantics": using statistical properties of words to cluster them in semantic categories (Content) words as the place where phonology and semantics meet. Positive and negative concepts: Tuesday `the day after Monday and before Wednesday', Gentile `not Jewish', hammer `used to hit nails' www.cs.indiana.edu /classes/c661/words.html   (1554 words)

FrameNet: Building a Semantic Lexicon In Hans Karlgren (ed.), Statistical Methods in Linguistics. In Fillmore and Langendoen (eds.) Studies in Linguistic Semantics. The work is described as "frame-based" since the semantic underpinnings make use of Frame Semantic formalisms, and "corpus-based", since the evidence will be drawn from a large corpus of English text. cslu.cse.ogi.edu /nsf/isgw97/reports/fillmore.html   (1213 words)

Notes C.J. FILLMORE, "The Need for Frame Semantics Within Linguistics", Statistical Methods in Linguistics (1976) 5-29; cf. The proposed description of the authorial and narrative audiences relies solely on the content of the semantic and narrative frames evoked or cultivated by the narration and does not require recourse to particular historical presuppositions or appeals to authorial intent. According to PERRY, "Literary Dynamics", 37, "The frame serves as a guiding norm in the encounter with the text, as a negative defining principle, so that deviation from it becomes perceptible and requires motivation by another frame or principle": cf. www.bsw.org /project/biblica/bibl84/Comm02n.html   (1543 words)

Y. Noh -- Information Studies 277 -- Spring 2004 -- Term Paper Semantic Web activity, led by the World Wide Web Consortium [W3C], builds upon existing standards (e.g., [URI], [Unicode], [XML]) and conventions of practice (e.g., the use of namespaces in programming languages) to define the syntax and semantics of data structures for Web applications. In working with a corpus, there are two major criteria that determine the validity of results obtained through statistical analysis: the sample size and the representativeness of the sample. A corpus, in the linguist's sense, is a body of written or spoken material upon which a linguistic analysis is based [OED 2]. polaris.gseis.ucla.edu /ynoh/paper.html   (7561 words)

LFG4SALSA: An LFG Syntax-Semantics Interface for Frame Semantic Processing In LFG4SALSA we therefore design a syntax-semantics interface for frame semantics on top of a German LFG grammar, and develop techniques for the automated induction of a frame semantic lexicon with syntax-semantics mapping principles from the manually annotated SALSA corpus. The aim of the cooperation project LFG4SALSA is to develop corpus-based techniques for the induction of an LFG-based syntax-semantics interface for frame semantics from the manually annotated SALSA corpus. Further, we develop a system for automatic frame semantic annotation with statistical disambiguation in a parsing architecture for NLP-based processing of German texts. www.dfki.de /pas/f2w.cgi?ltp/lfg4salsa-e   (223 words)

LINGUIST List 12.1709: Allan, Natural Language Semantics Niladri Sekhar Dash, Indian Statistical Institute, Kolkata, India SYNOPSIS In Chapter 1 [Some fundamental concepts of semantics] (pp. So far, the first ten chapters of the book cover the general exegesis of linguistic semantics, approaches to it, and discussion of tools and methods for explaining meaning in natural language. He discusses the semantics of proper names, and locates their place in lexicon. www.linguistlist.org /issues/12/12-1709.html   (223 words)

4.2 Semantics An interesting side-effect of introducing the notion of probability distribution into the semantics of our logic is that our logic will obey the familiar laws of the probability calculus; this will be true both for formulae representing information about degrees of belief, and for formulae representing statistical information. Things are quite different with Clause 4; this clause aims to specify the semantics of formulae involving the system's degrees of belief, so a reference must be made to the interpretations that the system ``believes in principle possible'' and to their respective probabilities. As previously hinted, a denotational semantics for a logical language is obtained by postulating the existence of a number of ``ways the world could be''; these are usually called interpretations. www.dcs.gla.ac.uk /fermi/tech_reports/Sebastiani1/subsection3_4_2.html   (223 words)

PRISM The theoretical background of PRISM system is distribution semantics for parameterized logic programs and EM learning of their parameters from observations [Sato 95, Sato 01c]. It is a new and unprecedented programming language with learning ability for statistical parameters embedded in programs. Speaking mathematically, a PRISM program is a logic program in which facts have a parameterized probability distribution so that the program can be seen as a parameterized statistical model. sato-www.cs.titech.ac.jp /prism   (223 words)

Semantic similarity Next: Shallow lexical semantics in Up: Technical Overview Previous: Statistical similarity The fact that an ex-husband is an ex-spouse belongs to the lexical semantics of the two words ``ex-husband'' and ``ex-spouse.'' This is the level of semantics we wish to capture in the FAQ F We call this a shallow lexical semantics, since it is associated directly with the words. people.cs.uchicago.edu /~kulyukin/ai-mag-paper/node6.html   (223 words)

Inductive Inference 1 It seemed that if there was a usable "theory" (model, semantics, library, prelude) of the "statistical models" that are produced in inductive inference, it might help to reduce the reinvention of basic components, and to increase sharing of components, which would lead to more reliable, more general and lighter code. In addition, some important and well-known instances of statistical models and of inference algorithms for them are also given, first to make a general claim for being practical, and also to study their structures and parts. It was also obvious that polymorphic types and high-order functions would be very useful for programming with statistical models, just as they are for programming with functions. www.csse.monash.edu.au /~lloyd/tildeFP/II/200309/READ-ME.shtml   (6112 words)

Inductive Inference 1 It seemed that if there was a usable "theory" (model, semantics, library, prelude) of the "statistical models" that are produced in inductive inference, it might help to reduce the reinvention of basic components, and to increase sharing of components, which would lead to more reliable, more general and lighter code. In addition, some important and well-known instances of statistical models and of inference algorithms for them are also given, first to make a general claim for being practical, and also to study their structures and parts. It was also obvious that polymorphic types and high-order functions would be very useful for programming with statistical models, just as they are for programming with functions. www.csse.monash.edu.au /~lloyd/tildeFP/II/200309/READ-ME.shtml   (6112 words)

The role of the syntax/semantics mapping in SLA: Computational experiments in verb classification Our computational experiments support the hypothesis that L2 learners use the mapping between the semantics and syntax of verbs in their L1, in acquiring properties of verbs in L2. The role of the syntax/semantics mapping in SLA: Computational experiments in verb classification Earlier work has shown that such statistical syntactic features within English can be used to classify English verbs into semantic classes (Merlo and Stevenson 2001). www.qc.edu /~efernand/CUNY2002/program/absts/141.htm   (6112 words)

Looking back to 1952: the first Machine Translation conference It was not only Oswald's work on micro-semantics but also Kaplan's findings on the potential of context for disambiguation that had demonstrated the value of the statistical approach. On the other hand, they recognised that some problems could be eased by pre-editing or by the regularisation of input texts; that statistical data on language could be valuable; that disambiguation could be helped by the identification of sublanguages; and that there were regularities of syntax which could be usefully applied. More particularly, traditional linguistics had clearly not provided MT researchers with what they needed for dealing with vocabulary and syntax; it was believed that statistical data from real texts could fill the gap. ourworld.compuserve.com /homepages/WJHutchins/TMI-97.htm   (4947 words)

Mobile Speech-to-Speech Translation of Spontaneous Dialogs: An Overview of the Final Verbmobil System While statistical translation is very robust against speech recognition problems and produces quick-and-dirty results, semantic transfer is computationally more expensive and less robust but produces higher quality translations. A chart parser and a statistical LR parser are combined in a package that is visualized in the screenshot as "integrated processing". its use of five concurrent translation engines: statistical translation, case-based translation, substring-based translation, dialog-act based translation, and semantic transfer. verbmobil.dfki.de /ww.html   (4947 words)

Beyond Named Entity Recognition - LREC 2004 Workshop The workshop will offer a forum where experience from lexical semantics and statistical learning will be presented and fruitfuldiscussion among researchers in both fields will be promoted. Semantic disambiguation serves thus the purpose of improving the generalization power of statistical models. One of the questions here is how to determine a suitable level of clustering (for NE identification and for WSD) that would lead to high accuracy and to performance improvement by obtained statistical models. ai-nlp.info.uniroma2.it /ws_lrec04   (626 words)

List of NLP Papers I Have in Printed Form Murat Ersan, Eugene Charniak, "A Statistical Syntactic Disambiguation Program and What It Learns", Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing. Tung-Hui Chiang, Keh-Yih Su, "Statistical Models for Deep-structure Disambiguation", Proceedings of the Fourth Workshop on Very Large Corpora, August 1996, pp.113-124 Philip Resnik, "Selectional Preference and Sense Disambiguation", ACL SIGLEX Workshop on Tagging Text with Lexical Semantics:Why, What and How?, 1997 nlp.korea.ac.kr /~hjchung/labinfo/printnlp.html   (626 words)

ESSLLI 2006 - Courses and Workshops Introduction to symbolic and statistical NLP in Scheme An empirical view on semantic roles within and across languages Counting Words: An Introduction to Lexical Statistics Working with Discourse Representation Theory Data-Driven Methods for Acquiring Linguistic Information Computational semantics: linking language processing to applications Word Sense Disambiguation Temporal anaphora in tenseless languages Linear Logic, Linguistic Resource Sensitivity and Resumption From syntactic structures to logical semantics Applications and Extensions of Dynamic Semantics Semantic Domains in Natural Language Processing Argument Structure Treebank-Based Acquisition of LFG, HPSG and CCG Resources Implementing Argument Alternations Probabilistic Methods in Computational Psycholinguistics Machine Learning and Dialogue Anaphora resolution: theory and practice esslli2006.lcc.uma.es /give-page.php?id=6   (245 words)

ACL Anthology A Statistical Semantic Parser that Integrates Syntax and Semantics Effective use of WordNet Semantics via Kernel-Based Learning Using Morphology and Syntax Together in Unsupervised Learning acl.ldc.upenn.edu /W/W05   (2222 words)

FOA: Preface FOA's statistical methods are good at semantics, knowing gross things about an entire document's meaning -- what words mean in terms of how they relate to other documentss in the corpus and to users' queries. An optimist, however, could see a basic complimentary between statistical methods and the linguists' syntactic methods. Syntactic analysis captures the fine structure of individual sentences, and depends critically on just the same noise words to relibly anchor its parsing. www-cse.ucsd.edu /~rik/foa/l2h/foa-0.html   (2690 words)

FQS 1(3) Jens Allwood, Maria Björnberg, Leif Grönqvist, Elisabeth Ahlsén & Cajsa Ottesjö: The Spoken Language Corpus at the Department of Linguistics, Göteborg University Presently he is heading projects concerned with the semantics of spoken language phenomena, multimodal communication, cultural variation in communication and the influence of social activity on spoken language. His research primarily includes work in semantics and pragmatics. Statistical Techniques for the Study of Language and Language Behaviour. qualitative-research.net /fqs-texte/3-00/3-00allwoodetal-e.htm   (2690 words)

ER 1979: 169-192 Richard Swenson: On the Semantics of the Relational Data Model. Liu: A Methodology of Application Program Analysis and Conversion Based on Database Semantics. Stanley Y. Su, Shamkant B. Navathe, Don S. Batory: Logical and Physical Modeling of Statistical Scientific Databases. www.vldb.org /dblp/db/conf/er/SuL79.html   (770 words)

Natural Language Navigation This translates to using a complex algorithm engine, which uses analysis of the semantics of a language and the probability of a set of phrases conveying a meaning, concept or idea. A logical progression engine works on the premise that human language - especially English language follows a semantic logic discernible to a statistical probability analysis. The complex algorithm behind the working of a logical progression engine uses the given input and establishes a pattern based on statistical analysis. www.targetwoman.com /articles/natural-language-navigation.html   (770 words)

Studiehandboken kurser Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in causal form) and data. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. Four, Bayesian statistical method in conjunction with Bayesian networks offer an efficient and principled approach for avoiding the over fitting of data. www.luth.se /publ/stuka/2001/3210/KSMD127.sv.htm   (770 words)

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