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Visual Sentiment Analysis of Text Document Streams · 27
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In: http://bib.dbvis.de/uploadedFiles/authors_version.pdf (2010)
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| 42 |
Do grammars minimize dependency length
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In: http://theory.esm.rochester.edu/temperley/papers/gildea-temperley-cs10.pdf (2010)
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| 43 |
G.: Textractor: A Framework for Extracting Relevant Domain Concepts from Irregular Corporate Textual Datasets
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In: http://odur.let.rug.nl/gosse/papers/textractor.pdf (2010)
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| 44 |
A Hybrid Approach to Unsupervised Relation Discovery Based on Linguistic Analysis and Semantic Typing ...
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| 46 |
Annotating named entities in Twitter data with crowdsourcing ...
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| 47 |
Statistical Techniques for Language Recognition: An Introduction and Guide for Cryptanalysts ...
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| 48 |
Unsupervised techniques for discovering ontology elements from Wikipedia article links ...
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| 49 |
Design and development of a lattice structure dependency parser for under-resourced languages ...
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| 51 |
Analyzing Networked Learning Texts
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Abstract:
Social interactions are essential in understanding the collaborative processes in networked learning environments. Although individuals may learn by retrieving information from online archives, dictionaries and encyclopaedia, it is the interaction with others with similar, perhaps narrowly enjoyed interests that fuels the benefits of networked learning. This paper presents our ongoing work on a novel, automated method for extracting interaction data from threaded discussions of networked learning groups. Using natural language processing, the proposed method reduces large text-based datasets to community and conversational essentials that show the relations of importance to group members. By studying these relations, we hope to identify what matters in terms of learning in the online interaction space and to provide useful representations of online conversations to help networked learners (instructors and students) better understand the social environment in which they are participants. To do so also requires making accurate determinations of who is talking to whom. This paper discusses the methodological issues associated with extracting names from networked learning texts and our procedures for enhancing network information through new techniques of name extraction. ; Proceedings of Networked Learning Conference, Halkidiki, Greece, May 5-6, 2008
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Keyword:
collaborative learning; natural language processing; social networks
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URL: http://hdl.handle.net/10222/12828
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| 52 |
Automated Discovery and Analysis of Social Networks from Threaded Discussions
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| 53 |
Email Thread Summarization with Conditional Random Fields
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In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1268159269 (2010)
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| 54 |
Efficient development of grammars for multilingual rule-based applications ...
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| 55 |
Visual Salience and Reference Resolution in Situated Dialogues: A Corpus-based Evaluation.
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In: Conference papers (2010)
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| 56 |
Searching Semantic Resources for Complex Selectional Restrictions to Support Verb Sense Disambiguation
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In: DTIC (2010)
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| 57 |
Measuring the Non-compositionality of Multiword Expressions
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| 59 |
Figure 1. Linguistic Analysis - Adx – Agent For Morphologic Analysis Of Lexical Entries In A Dictionary ...
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| 60 |
Figure 2. The Structures Of The Two Tables From The Dex Online Database-Adx – Agent For Morphologic Analysis Of Lexical Entries In A Dictionary ...
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