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Psycholinguistics of AI, Psycholinguistics versus Machine code ; Psicolinguística da AI, Psicolinguística versus código de máquina
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In: Signo; v. 47 n. 88 (2022): ISAPL ; 27-43 ; 1982-2014 ; 0101-1812 (2022)
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On The Subject of Thinking Machines
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In: https://hal.archives-ouvertes.fr/hal-01697125 ; 2018 (2018)
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Weighted tree automata and transducers for syntactic natural language processing ...
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Sociolinguistically Informed Natural Language Processing: Automating Irony Detection
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In: DTIC (2015)
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Structural Complexity in Linguistic Systems Research Topic 3: Mathematical Sciences
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In: DTIC (2015)
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Robust Speech Processing & Recognition: Speaker ID, Language ID, Speech Recognition/Keyword Spotting, Diarization/Co-Channel/Environmental Characterization, Speaker State Assessment
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In: DTIC (2015)
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Examining the Role of Religiosity in Moral Cognition, Specifically in the Formation of Sacred Values, and Researching Computational Models for Analyzing Sacred Rhetoric and its Consequential Emotions
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In: DTIC (2015)
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A Fast Variational Approach for Learning Markov Random Field Language Models
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In: DTIC (2015)
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Learning to Understand Natural Language with Less Human Effort
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In: DTIC (2015)
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Virtual sign : a real time bidirectional translator of portuguese sign language
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Intelligence Virtual Analyst Capability: Governing Concepts and Science and Technology Roadmap
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Towards a Simple and Efficient Web Search Framework
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In: DTIC (2014)
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Abstract:
The Web Track of 2014 Text REtrieval Conference (TREC) addresses the most fundamental problem of Information Retrieval. We did not intend to craft a system that beats the state-of-the-art search engines, but to design a light weight and cost-effective system with comparable performances. We introduce a twopass retrieval framework, with the first pass consisting of a simple and efficient retrieval model that focuses on recall, and the second pass a wave of feature extraction algorithms run on the set of top ranked documents, followed by Learning to Rank (LETOR) algorithms that provide different precision oriented rankings, and their outputs are combined using data fusion. We have focused on using statistical Language Models with novel and well-known smoothing techniques, different LETOR methods and various data fusion techniques. In addition, we have also tried using topic modelling with Hierarchical Dirichlet Allocation for query expansion in the hope of improving diversity of our results. However, the topic modelling approach has turned out to be unsuccessful, and we have not been able to spot the problem and benefit from it in this work. In addition we also present some further analyses demonstrating that our approach is robust against overfitting, and some general studies on overfitting in the context of LETOR. ; Presented at the Twenty-Third Text REtrieval Conference (TREC 2014) held in Gaithersburg, Maryland, November 19-21, 2014. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) and the Defense Advanced Research Projects Agency (DARPA).
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Keyword:
*DATA FUSION; *INFORMATION RETRIEVAL; *INFORMATION SYSTEMS; *LEARNING MACHINES; ALGORITHMS; BORDA COUNT; CLASSIFICATION; COMPUTATIONAL LINGUISTICS; CONDORCET METHOD; Cybernetics; DATA MINING; FEATURE EXTRACTION; Information Science; INTERNET; KNOWLEDGE MANAGEMENT; LANGUAGE MODELLING; LETOR(LEARNING TO RANK); NDCG(DISCOUNTED CUMULATIVE GAIN); PATTERN RECOGNITION; RANKING; RECIPROCAL RANK; SEARCH ENGINES; SEMANTICS; SMOOTHING(MATHEMATICS); STATISTICAL ANALYSIS; TEXT PROCESSING
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URL: http://www.dtic.mil/docs/citations/ADA618578
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA618578
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Distributed Non-Parametric Representations for Vital Filtering: UW at TREC KBA 2014
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In: DTIC (2014)
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Modelling Psychological Needs for User-dependent Contextual Suggestion
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In: DTIC (2014)
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Discovery of Deep Structure from Unlabeled Data
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In: DTIC (2014)
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ReaderBench, o platformă integrată pentru analiza complexității textuale și a strategiilor de lectură
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In: Proc. 10-a Conf. Nat. de Interactiune Om-Calculator (RoCHI 2013) ; https://hal.archives-ouvertes.fr/hal-01412573 ; Proc. 10-a Conf. Nat. de Interactiune Om-Calculator (RoCHI 2013), T. Stefanut; C. Rusu, 2013, Cluj, Romania. pp.39-46 (2013)
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MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic Sensing
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In: DTIC (2013)
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Using a Bayesian Model to Combine LDA Features with Crowdsourced Responses
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In: DTIC (2013)
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