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1
Automatic extraction of property norm-like data from large text corpora
In: http://www.cl.cam.ac.uk/%7Ealk23/cog-sci-2014.pdf (2013)
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2
Unsupervised sentiment analysis with emotional signals
In: http://www.public.asu.edu/~xiahu/papers/www13.pdf (2013)
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3
CroNER: Recognizing Named Entities in Croatian Using Conditional Random Fields
In: http://www.informatica.si/PDF/37-2/11_Karan - CroNER Recognizing Named Entities in Croatian Using Conditional Random Fields.pdf (2013)
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4
Speech-Centric Information Processing: An Optimization-Oriented Approach
In: http://research.microsoft.com/pubs/179540/ProcIEEE_He_deng_finalsub.pdf (2012)
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5
R.: WiSeNet: Building a Wikipedia-based semantic network with ontologized relations
In: http://wwwusers.di.uniroma1.it/~navigli/pubs/CIKM_2012_Moro_Navigli.pdf (2012)
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6
Recognizing Temporal Information in Korean Clinical Narratives through Text Normalization
In: ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/9c/37/Healthc_Inform_Res_2011_Sep_30_17(3)_150-155.tar.gz (2011)
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7
Ukp at crosslink: Anchor text translation for cross-lingual link discovery
In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings9/NTCIR/07-NTCIR9-CROSSLINK-KimJ.pdf (2011)
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8
Improved video categorization from text metadata and user comments
In: http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/pubs/archive/37092.pdf (2011)
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9
Folks in folksonomies: social link prediction from shared metadata
In: http://hal.archives-ouvertes.fr/docs/00/42/98/86/PDF/wsdm141-schifanella.pdf (2010)
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10
An Information-extraction system for Urdu—a resource-poor language
In: http://www.cedar.buffalo.edu/~rohini/Papers/ACM-TALIP.pdf (2010)
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11
Small-vocabulary speech recognition for resource-scarce languages
In: http://www.cs.cmu.edu/~roni/papers/sigdev2010-final7.pdf (2010)
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12
The Effect of Different Context Representations on Word Sense Discrimination in Biomedical Texts
In: http://www.d.umn.edu/~tpederse/Pubs/acm-ihi-2010-pedersen.pdf (2010)
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13
Transliteration for resource-scarce languages
In: http://www.cse.iitb.ac.in/~damani/papers/TALIP10/transliterationTALIP10.pdf (2010)
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14
Visual Sentiment Analysis of Text Document Streams · 27
In: http://bib.dbvis.de/uploadedFiles/authors_version.pdf (2010)
Abstract: This paper describes automatic methods and interactive visualizations that are tightly coupled with the goal to enable users to detect interesting portions of text document streams. In this scenario the interestingness is derived from the sentiment, temporal density and context coherence that comments about features for different targets (e.g. persons, institutions, product attributes, topics, etc.) have. Contributions are made at different stages of the visual analytics pipeline, including novel ways to visualize salient temporal accumulations for further exploration. Moreover, based on the visualization an automatic algorithm aims to detect and preselect interesting time interval patterns for different features in order to guide analysts. The main target group for the suggested methods are business analysts who want to explore time-stamped customer feedback to detect critical issues. Finally, application case studies on two different datasets and scenarios are conducted and an extensive evaluation is provided for the presented intelligent visual interface for
Keyword: Applica- tions—Text processing; Categories and Subject Descriptors; Content Analysis and Indexing—Linguistic processing; Document Capture—Document analysis; Document Time Series; General; H.1.2 [Models and Principles; H.3.1 [Information Storage and Retrieval; H.4.0 [Information Systems Applications; H.5.2 [Information Interfaces and Presentation; I.2.7 [Artificial Intelligence; I.3.6 [Computer Graphics; I.5.4 [Pattern Recognition; I.5.5 [Pattern Recognition; I.7.5 [Document and Text Processing; Implementation—Interactive systems; J.1 [Administrative Data Processing; Methodology and Techniques—Interaction Techniques; Natural Language Processing—Text analysis; Sentiment Analysis; Text Mining; User / Machine Systems— Human information processing; User Interfaces—Graphical user interfaces; Visual Analytics; —Business Additional Key Words and Phrases
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.690.5878
http://bib.dbvis.de/uploadedFiles/authors_version.pdf
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15
Towards natural question guided search
In: http://www.mathcs.emory.edu/~kotov/papers/kotov-www10.pdf (2010)
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16
G.: Textractor: A Framework for Extracting Relevant Domain Concepts from Irregular Corporate Textual Datasets
In: http://odur.let.rug.nl/gosse/papers/textractor.pdf (2010)
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17
Lexical Ambiguity in Cross-Language Image Retrieval: a Preliminary Analysis. CLEF 2009 worskhop notes
In: http://clef.isti.cnr.it/2009/working_notes/bnavarro-paperCLEF09.pdf (2009)
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18
Using semantic relatedness and word sense disambiguation for (cl)ir
In: http://clef.isti.cnr.it/2009/working_notes/agirre-robust-paperCLEF2009.pdf (2009)
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19
IXIR: A Statistical Information Distillation System
In: http://www.icsi.berkeley.edu/~levit/papers/CSL09_IXIR.pdf (2009)
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20
Near Real Time Information Mining in Multilingual News
In: http://www2009.org/proceedings/pdf/p1153.pdf (2009)
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