Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Mary McGee Wood"'
Autor:
Richard Craggs, Mary McGee Wood
Publikováno v:
Computational Linguistics, Vol 31, Iss 3 (2021)
Externí odkaz:
https://doaj.org/article/16dc45729e764515b271805566e9ef30
Autor:
Mary McGee Wood
In the last few years categorial grammars have been the focus of dramatically expanded interest and activity, both theoretical and computational. This book, the first introduction to categorical grammars, is written as an objective critical assessmen
Autor:
Mary McGee Wood, Richard Craggs
Publikováno v:
Computational Linguistics. 31:289-296
Agreement statistics play an important role in the evaluation of coding schemes for discourse and dialogue. Unfortunately there is a lack of understanding regarding appropriate agreement measures and how their results should be interpreted. In this a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::09ca051cdee4f6a50b1cf365b0994062
https://doi.org/10.1162/089120105774321109
https://doi.org/10.1162/089120105774321109
Publikováno v:
Systematics and Biodiversity. 1:151-157
An analysis of conventional paper‐based botanical descriptions from Floras was undertaken as part of the development of MultiFlora, a system for the automatic production of a queryable database from such legacy data. The descriptions of five specie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::120ec7bb82c9001ddd2af4cd67b3276f
https://doi.org/10.1017/s1477200003001129
https://doi.org/10.1017/s1477200003001129
Publikováno v:
2009 Eighth International Symposium on Natural Language Processing.
In this paper we have addressed the qualitative (human evaluation) and quantitative (ROUGE) evaluation of computer generated summaries of the students' essays. The experimental results show that there is a positive high correlation between ROUGE scor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b838c777c09c61ae256b0b15f2f657a
https://doi.org/10.1109/snlp.2009.5340933
https://doi.org/10.1109/snlp.2009.5340933
Autor:
Seemab Latif, Mary McGee Wood
Publikováno v:
CSIE (5)
In this paper, experiments have addressed the calculation of inter-annotator inconsistency in selecting the content in both manual and automatic summarization of sample TOEFL essays. A new finding is that the linguistic quality of source essay has a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8420094a88a841029bb5bd21463f347a
https://doi.org/10.1109/csie.2009.777
https://doi.org/10.1109/csie.2009.777
Autor:
Mary McGee Wood, Seemab Latif
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540698579
NLDB
NLDB
The processing performance of vector-based document clustering methods is improved if automatic summarisation is used in addition to established forms of text pre-processing.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::49738fd5c0bdc5ddd99c204f9ffc0a43
https://doi.org/10.1007/978-3-540-69858-6_44
https://doi.org/10.1007/978-3-540-69858-6_44
Autor:
Richard Craggs, Mary McGee Wood
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540221432
ADS
ADS
If we wish to implement dialogue systems which express emotion, dialogue corpora annotated for emotion would be a valuable resource. In order to develop such corpora we require a reliable annotation scheme. Here we describe an annotation scheme for e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b00fa0d6d14e1582d53f24c59e7984c
https://doi.org/10.1007/978-3-540-24842-2_9
https://doi.org/10.1007/978-3-540-24842-2_9
Publikováno v:
Natural Language Processing and Information Systems ISBN: 9783540225645
NLDB
NLDB
Legacy data in many mature descriptive sciences is distributed across multiple text descriptions. The challenge is both to extract this data, and to correlate it once extracted. The MultiFlora system does this using an established Information Extract
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c9375beb0ddb41565d91cd78a7dba52c
https://doi.org/10.1007/978-3-540-27779-8_22
https://doi.org/10.1007/978-3-540-27779-8_22