1 |
Classifying Bias in Large Multilingual Corpora via Crowdsourcing and Topic Modeling
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
A random forest system combination approach for error detection in digital dictionaries ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
A random forest system combination approach for error detection in digital dictionaries
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Citation Handling: Processing Citation Texts in Scientific Documents
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Correcting Errors in Digital Lexicographic Resources Using a Dictionary Manipulation Language
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Citation Handling for Improved Summarization of Scientific Documents
|
|
|
|
Abstract:
In this paper we present the first steps toward improving summarization of scientific documents through citation analysis and parsing. Prior work (Mohammad et al., 2009) argues that citation texts (sentences that cite other papers) play a crucial role in automatic summarization of a topical area, but did not take into account the noise introduced by the citations themselves. We demonstrate that it is possible to improve summarization output through careful handling of these citations. We base our experiments on the application of an improved trimming approach to summarization of citation texts extracted from Question-Answering and Dependency-Parsing documents. We demonstrate that confidence scores from the Stanford NLP Parser (Klein and Manning, 2003) are significantly improved, and that Trimmer (Zajic et al., 2007), a sentence-compression tool, is able to generate higher-quality candidates. Our summarization output is currently used as part of a larger system, Action Science Explorer (ASE) (Gove, 2011).
|
|
URL: http://hdl.handle.net/1903/11822
|
|
BASE
|
|
Hide details
|
|
14 |
Error Correction for Arabic Dictionary Lookup
|
|
|
|
In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), Valetta, 17 - 23 May 2010 (2010), 263-268
|
|
IDS OBELEX meta
|
|
Show details
|
|
15 |
Multiple Alternative Sentene Compressions as a Tool for Automatic Summarization Tasks
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Headline Generation for Written and Broadcast News
|
|
|
|
In: DTIC (2005)
|
|
BASE
|
|
Show details
|
|
17 |
Hedge Trimmer: A Parse-and-Trim Approach to Headline Generation
|
|
|
|
In: DTIC (2003)
|
|
BASE
|
|
Show details
|
|
|
|