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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 Hits 1 – 20 of 66 1 A Refutation of Finite-State Language Models through Zipf’s Law for Factual Knowledge Łukasz Dębowski In: Entropy ; Volume 23 ; Issue 9 (2021) BASE Show details 2 STATISTICAL RELATIONAL LEARNING AND SCRIPT INDUCTION FOR TEXTUAL INFERENCE Mooney,Raymond. - 2017 BASE Show details 3 Confound and control in language experiments ... Alday, Phillip M.; Sassenhagen, Jona. - : figshare, 2016 BASE Show details 4 Confound and control in language experiments ... Alday, Phillip M.; Sassenhagen, Jona. - : figshare, 2016 BASE Show details 5 Structural Complexity in Linguistic Systems Research Topic 3: Mathematical Sciences Crutchfield, James P In: DTIC (2015) BASE Show details 6 A Fast Variational Approach for Learning Markov Random Field Language Models Jernite, Yacine; Rush, Alexander M; Sontag, David In: DTIC (2015) BASE Show details 7 Learning to Understand Natural Language with Less Human Effort Krishnamurthy, Jayant In: DTIC (2015) Abstract: Learning to understand the meaning of natural language is an important problem within language processing that has the potential to revolutionize human interactions with computer systems. Informally, the problem specification is to map natural language text to a formal semantic representation connected to the real world. This problem has applications such as information extraction and understanding robot commands, and also may be helpful for other natural language processing tasks. Human annotation is a significant bottleneck in constructing language understanding systems. These systems have two components that are both constructed using human annotation: a semantic parser and a knowledge base. Semantic parsers are typically trained on individually-annotated sentences. Knowledge bases are typically manually constructed and given to the system. While these annotations can be provided in simple settings -- specifically, when the knowledge base is small -- the annotation burden quickly becomes unbearable as the size of the knowledge base increases. More annotated sentences are required to train the semantic parser and the knowledge base itself requires more annotations. Alternative methods to build language understanding systems that require less human annotation are necessary in order to learn to understand natural language in these more challenging settings. This thesis explores alternative supervision assumptions for building language understanding systems with the goal of reducing the annotation burden described above. I focus on two applications: information extraction and understanding language in physical environments. In the information extraction application, I present algorithms for training semantic parsers using only predicate instances from a knowledge base and an unlabeled text corpus. ; Sponsored in part by AFOSR, grant no. FA9750-09-C-0179. Keyword: *COMPUTATIONAL LINGUISTICS; *COMPUTER PROGRAMS; *NATURAL LANGUAGE; *STATISTICAL INFERENCE; ALGORITHMS; CLASSIFICATION; Computer Programming and Software; DISTANT SUPERVISION; FEATURE EXTRACTION; GROUNDED LANGUAGE UNDERSTANDING; INFORMATION EXTRACTION SYSTEMS; INFORMATION PROCESSING; KNOWLEDGE BASED SYSTEMS; LEARNING MACHINES; Linguistics; MAPPING; NATURAL LANGUAGE UNDERSTANDING; OPERATING SYSTEMS(COMPUTERS); PARSERS; PROBABILITY; SEMANTIC PARSING; SEMANTICS; Statistics and Probability; STOCHASTIC PROCESSES; TEXT PROCESSING; THESES URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA624638 http://www.dtic.mil/docs/citations/ADA624638 BASE Hide details 8 The role of markup in the digital humanities Schmidt, Desmond In: Historical Social Research ; 37 ; 3 ; 125-146 ; Kontroversen um die Digitalen Geisteswissenschaften / Controversies around the digital humanities (2015) BASE Show details 9 Mandarin listeners can learn non-native lexical tones through distributional learning Ong, Jia (S31400); Burnham, Denis K. (R7357); Escudero, Paola (R16636). - : U.K., University of Glasgow, 2015 BASE Show details 10 A nonparametric Bayesian perspective for machine learning in partially-observed settings ... Akova, Ferit. - : IUPUI University Library, 2014 BASE Show details 11 A nonparametric Bayesian perspective for machine learning in partially-observed settings Akova, Ferit. - 2014 BASE Show details 12 The Negations of Conjunctions, Conditionals, and Disjunctions Khemlani, Sangeet; Orenes, Isabel; Johnson-Laird, P N In: DTIC (2014) BASE Show details 13 What's Wrong With Automatic Speech Recognition (ASR) and How Can We Fix It? Morgan, Nelson; Wegmann, Steven; Cohen, Jordan In: DTIC (2013) BASE Show details 14 Identität in Erzählung und im Erzählen: Versuch einer Bestimmung der Besonderheit des narrativen Diskurses für die sprachliche Verfassung von ldentität Bamberg, Michael In: Journal für Psychologie ; 7 ; 1 ; 43-55 (2012) BASE Show details 15 Coherent Demodulation of Nonstationary Random Processes Clark, Charles Pascal. - 2012 BASE Show details 16 Incremental Syntactic Language Models for Phrase-Based Translation Schwartz, Lane; Callison-Burch, Chris; Schuler, William... In: DTIC (2011) BASE Show details 17 Using Linguistic Knowledge in Statistical Machine Translation Zbib, Rabih M. In: DTIC (2010) BASE Show details 18 Related Entity Finding: University of Waterloo at TREC 2010 Entity Track Vechtomova, Olga In: DTIC (2010) BASE Show details 19 Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques Ge, Ruifang In: DTIC (2010) BASE Show details 20 Gibbs Sampling for the Uninitiated Resnik, Philip; Hardisty, Eric In: DTIC (2010) BASE Show details Page: 1 2 3 4

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