Cicling2005
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The document discusses techniques for discriminating between different meanings (senses) of words based on their usage context. It presents a methodology that clusters similar contexts of a target word based on lexical features. Contexts are represented as vectors, and similarities are measured to group contexts and label clusters. Experimental results show second-order representations that capture indirect relationships generally perform better, while first-order may be better for larger, more homogeneous data. Software tools described implement various natural language processing and word sense discrimination techniques.
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Ijcai 2007 Pedersen
The document describes language-independent methods for clustering similar contexts without using syntactic or lexical resources. It discusses representing contexts as vectors of lexical features and clustering them based on similarity. Feature selection involves identifying unigrams, bigrams, and co-occurrences based on frequency or association measures. Contexts can then be represented in first-order or second-order feature spaces and clustered. Applications include word sense discrimination, document clustering, and name discrimination.
Fusing semantic data
The document discusses different techniques for automatically fusing extracted annotations from multiple data sources. It outlines approaches for handling inconsistencies by applying uncertainty reasoning and overcoming schema heterogeneity. Specific techniques discussed include using a problem-solving method to decompose the fusion task, selecting methods based on their capabilities, propagating beliefs in a valuation network, and refining data using a neighborhood graph.
Maximum likelihood-set - introduction
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Fol
First-order logic (FOL) is a formal system used in mathematics, philosophy, linguistics, and computer science to represent knowledge about domains involving objects and relations. FOL extends propositional logic with quantifiers and predicates to describe properties of and relations between objects. Well-formed formulas in FOL involve constants, variables, functions, predicates, quantifiers, and logical connectives. The meaning and truth of FOL statements is determined with respect to a structure called a model that specifies a domain of objects and interpretations of symbols. FOL can be used to represent knowledge about many different domains and perform logical inference.
Mining Features from the Object-Oriented Source Code of Software Variants by ...
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Combining Lexical and Structural Similarity
Icon 2007 Pedersen
The document summarizes research on using different semantic techniques like contexts, co-occurrences, and ontologies to build a "semantic quilt" that can be used for natural language processing tasks. It discusses using n-gram statistics to identify associated words, sense clusters to identify similar contexts, and WordNet to measure conceptual similarity. The goal is to integrate these different semantic resources and methods to solve problems with less reliance on manually built resources.
similarities-knn.pptx
Installing and configuring computer systems involves competencies to assemble computer hardware, install operating systems and drivers for peripherals/devices, and install application software as well as to conduct testing and documentation.
This learning activity sheet will help you understand better the concepts and skills of installing and configuring computer systems. Try your best to go over the activities and answer the questions or items asked for.
Learning
20070702 Text Categorization
This document provides an overview of various techniques for text categorization, including decision trees, maximum entropy modeling, perceptrons, and K-nearest neighbor classification. It discusses the data representation, model class, and training procedure for each technique. Key aspects covered include feature selection, parameter estimation, convergence criteria, and the advantages/limitations of each approach.
Contextual Ontology Alignment - ESWC 2011
This document describes a new approach called BLOOMS+ for performing contextual ontology alignment of Linked Open Data datasets with an upper ontology. BLOOMS+ leverages contextual information from Wikipedia category hierarchies to compute similarities between concepts in different ontologies. It computes class similarity, contextual similarity between super classes, and an overall similarity to determine equivalence or subsumption relationships between concepts during alignment. The approach is evaluated on aligning several LOD ontologies to the PROTON upper ontology, outperforming existing solutions. Future work involves extending this approach to utilize more contextual sources and enable seamless querying across aligned datasets.
Similar to Cicling2005 (20)
Measuring Similarity Between Contexts and Concepts
Measuring Similarity Between Contexts and Concepts
Measuring Similarity Between Contexts and Concepts
Measuring Similarity Between Contexts and Concepts
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This document summarizes a tutorial on measuring the similarity and relatedness of concepts. It discusses the distinction between semantic similarity and relatedness. It describes several common measures of similarity that use information from ontologies, such as path-based measures, measures that incorporate path and depth, and measures that incorporate information content. It also discusses measures of relatedness that can be used for concepts that are not connected by ontological relations, such as definition-based measures and measures based on gloss vectors constructed from corpus data. Experimental results generally show that gloss vector measures perform best, followed by definition-based measures, with path-based measures performing the worst.Pedersen naacl-2013-demo-poster-may25
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The document summarizes a tutorial on measuring semantic similarity and relatedness between medical concepts. It introduces different types of measures, including path-based measures, measures using information content that incorporate concept specificity, and measures of relatedness that use definition overlaps or corpus co-occurrence information. The tutorial aims to explain the distinction between similarity and relatedness, describe available measures, and how to evaluate and apply them in clinical natural language processing tasks.
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The document discusses replicability and reproducibility in ACL conferences. It argues that empirical papers should include software and data so results can be reproduced. An analysis found that most papers from ACL 2011 did not include software or data. Generally descriptions were incomplete and few papers allowed true reproducibility. The author calls for higher standards, weighting replicability more in reviews, and removing blind submissions to improve transparency.
Pedersen naacl-2010-poster
This document summarizes research comparing different methods of measuring semantic similarity between concepts based on information content. It finds that using untagged text to derive information content, rather than the largest sense-tagged corpus, results in higher correlation with human judgments of similarity. Experiments showed no advantage to using sense-tagged text and that information content measures outperformed path-based measures, with estimates based just on taxonomy structure performing almost as well as using raw newspaper text.
Aaai 2006 Pedersen
The document discusses language independent methods for clustering similar contexts without using syntactic or lexical resources. It describes representing contexts as vectors of lexical features, reducing dimensionality, and clustering the vectors. Key methods include identifying unigram, bigram and co-occurrence features from corpora using frequency counts and association measures, and representing contexts in first or second order vectors based on feature presence.
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Cicling2005
- 1. Name Discrimination by Clustering Similar Contexts Ted Pedersen & Anagha Kulkarni University of Minnesota, Duluth Amruta Purandare Now at University of Pittsburgh Research Supported by National Science Foundation Faculty Early Career Development Award (#0092784)
- 16. 2 nd Order Context Vectors Context sea shore store
- 18. Limitations of 1 st or 2 nd order 0 52.27 0 0.92 0 4.21 0 28.72 0 3.24 0 1.28 0 2.53 Weapon Missile Shoot Fire Destroy Murder Kill 17.77 0 14.6 46.2 22.1 0 34.2 19.23 2.36 0 72.7 0 1.28 2.56 Execute Command Bomb Pipe Fire CD Burn
- 22. Evaluation (before mapping) c1 c2 c4 c3 2 1 15 2 C4 6 1 1 2 C3 1 7 1 1 C2 2 3 0 10 C1
- 23. Evaluation (after mapping) Accuracy=38/55=0.69 20 15 2 1 2 C4 17 1 1 0 55 11 12 15 10 6 1 2 C3 10 1 7 1 C2 15 2 3 10 C1
- 24. Majority Sense Classifier Maj. =17/55=0.31
- 27. Experimental Conclusions 2 nd order, RB Smaller Data (like SENSEVAL-2) 1 st order, UPGMA Large, Homogeneous (like Line, Hard, Serve) Recommendation Nature of Data