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- 771 type InProceedings.
- 771 label "Creating and Using a Correlated Corpus to Glean Communicative Commonalities".
- 771 sameAs 771.
- 771 abstract "This paper describes a collection of correlated communicative samples collected from the same individuals across six diverse genres. Three of the genres were computer mediated: email, blog, and chat, and three non-computer-mediated: essay, interview, and discussion. Participants were drawn from a college student population with an equal number of males and females recruited. All communication expressed opinion on six pre-selected, current topics that had been determined to stimulate communication. The experimental design including methods of collection, randomization of scheduling of genre order and topic order is described. Preliminary results for two descriptive metrics, word count and Flesch readability, are presented. Interesting and, in some cases, significant effects were observed across genres by topic and by gender of participant. This corpus will provide a resource to investigate communication stylistics of individuals across genres, the identification of individuals from correlated data, as well as commonalities and differences across samples that agree in genre, topic, and/or gender of participant.".
- 771 hasAuthorList authorList.
- 771 hasTopic Linguistics.
- 771 isPartOf proceedings.
- 771 keyword "Corpus (creation, annotation, etc.)".
- 771 keyword "Other".
- 771 keyword "Speech resource/database".
- 771 title "Creating and Using a Correlated Corpus to Glean Communicative Commonalities".
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- item-1 hasContent jade-goldstein-stewart.
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- item-2 hasContent kerri-goodwin.
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- 772 creator lauren-friedman.
- 772 creator linda-brandschain.
- 772 creator safa-ismael.
- 772 creator stephanie-strassel.
- 772 type InProceedings.
- 772 label "New Resources for Document Classification, Analysis and Translation Technologies".
- 772 sameAs 772.
- 772 abstract "The goal of the DARPA MADCAT (Multilingual Automatic Document Classification Analysis and Translation) Program is to automatically convert foreign language text images into English transcripts, for use by humans and downstream applications. The first phase the program focuses on translation of handwritten Arabic documents. Linguistic Data Consortium (LDC) is creating publicly available linguistic resources for MADCAT technologies, on a scale and richness not previously available. Corpora will consist of existing LDC corpora and data donations from MADCAT partners, plus new data collection to provide high quality material for evaluation and to address strategic gaps (for genre, dialect, image quality, etc.) in the existing resources. Training and test data properties will expand over time to encompass a wide range of topics and genres: letters, diaries, training manuals, brochures, signs, ledgers, memos, instructions, postcards and forms among others. Data will be ground truthed, with line, word and token segmentation and zoning, and translations and word alignments will be produced for a subset. Evaluation data will be carefully selected from the available data pools and high quality references will be produced, which can be used to compare MADCAT system performance against the human-produced gold standard.".
- 772 hasAuthorList authorList.
- 772 hasTopic Linguistics.
- 772 isPartOf proceedings.
- 772 keyword "Corpus (creation, annotation, etc.)".
- 772 keyword "Document Classification, Text categorisation".
- 772 keyword "Handwriting".
- 772 title "New Resources for Document Classification, Analysis and Translation Technologies".
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- item-1 hasContent stephanie-strassel.
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- 773 creator christian-federmann.
- 773 creator hans-uszkoreit.
- 773 creator torsten-marek.
- 773 creator ulrich-schaefer.
- 773 creator yajing-zhang.
- 773 type InProceedings.
- 773 label "Extracting and Querying Relations in Scientific Papers on Language Technology".
- 773 sameAs 773.
- 773 abstract "We describe methods for extracting interesting factual relations from scientific texts in computational linguistics and language technology taken from the ACL Anthology. We use a hybrid NLP architecture with shallow preprocessing for increased robustness and domain-specific, ontology-based named entity recognition, followed by a deep HPSG parser running the English Resource Grammar (ERG). The extracted relations in the MRS (minimal recursion semantics) format are simplified and generalized using WordNet. The resulting quriples are stored in a database from where they can be retrieved (again using abstraction methods) by relation-based search. The query interface is embedded in a web browser-based application we call the Scientist s Workbench. It supports researchers in editing and online-searching scientific papers.".
- 773 hasAuthorList authorList.
- 773 hasTopic Linguistics.
- 773 isPartOf proceedings.
- 773 keyword "Information Extraction, Information Retrieval".
- 773 keyword "Parsing Systems".
- 773 keyword "Semantic Web".
- 773 title "Extracting and Querying Relations in Scientific Papers on Language Technology".
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