xcit'ed

- paper management system

xcit'ed

- paper management system

 

by matton

tag search result for 'precision' return

search: 
add new paper
James R. Curran and Marc Moens.
Improvements in Automatic Thesaurus Extraction.
In Proceedings of the Workshop of the ACL Special Interest Group on the Lexicon (SIGLEX),
pp. 59-66,
2002.
Abstract: The use of semantic resources is common in modern NLP systems, but methods to extract lexical semantics have only recently begun to perform well enough for practical use. We evaluate existing and new similarity metrics for thesaurus extraction, and experiment with the tradeoff between extraction performance and efficiency. We propose an approximation algorithm, based on canonical attributes and coarse- and fine-grained matching, that reduces the time complexity and execution time of thesaurus extraction with only a marginal performance penalty.
thesaurus extraction systems -> differ in the definition of "context"
used a statistical shallow parser
frequency cutoff speeds up the calculation, but doesn't decrease the performance
misc. topics: weights, measures, cutoff frequency, speed-up by canonical vectors

canonical vectors: subj+dobj+iobj, TTestLog + maximum frequency cutoff
precision recall  recall  precision dice  cosine  weight function  function  weight  jaccard coefficient  coefficient  jaccard  vector space model  space model  vector space  model  space  vector  similarity measure  measure  similarity  context extraction  extraction  context  automatic thesaurus construction  thesaurus construction  automatic thesaurus  construction  thesaurus  automatic  frequency  cutoff  frequency cutoff  canonical  attribute  canonical attribute  canonical  vector  canonical vector  maximum  cutoff  maximum cutoff 
document  edit
updated at: 2007/07/07 17:25:42
James R. Curran and Marc Moens.
Scaling Context Space.
In Proceedings of the 40the Annual Meeting of the Association for Computational Linguistics (ACL),
pp. 231-238,
2002.
Abstract: Context is used in many NLP systems as an indicator of a term's syntactic and semantic function. The accuracy of the system is dependent on the quality and quantity of contextual information available to describe each term. However, the quantity variable is no longer fixed by limited corpus resources. Given fixed training time and computational resources, it makes sense for systems to invest time in extracting high quality contextual information from a fixed corpus. However, with an effectively limitless quantity of text available, extraction rate and representation size need to be considered. We use thesaurus extraction with a range of context extracting tools to demonstrate the interaction between context quantity, time and size on a corpus of 300 million words.
corpus size is not longer a limiting factor
W(L1R1), W(L12) give reasonable results
log-linear relation between corpus size and performance
"It is a phenomenon common to many NLP tasks that the quality or accuracy of a system increases loglinearly with the size of the corpus."
"it could well be that far simpler but scalable learning algorithms significantly outperform existing systems."

used 300M words corpus! (c.f. WordBank = 3.5M)
up to now people have typically worked with corpora of around one million words (up to one billion!)
thesaurus extraction is a task where success has been limited when using small corpora
shallow  syntactic relation  relation  syntactic  word position  position  word  surrounding word  word  surrounding  window  t-test  vector space model  space model  vector space  model  space  vector  automatic thesaurus extraction  thesaurus extraction  automatic thesaurus  extraction  thesaurus  automatic  contextual information  information  contextual  context  parsing  shallow parsing  window  size  window size  precision recall  precision recall  corpus  size  corpus size 
document  edit
updated at: 2007/05/13 09:53:53
寺田昭, 吉田稔, 中川裕志
文脈情報による同義語辞書作成支援ツール
IPSJ SIG Technical Report, NL176, pp. 87-94,
2006.
To improve the proficiency of text processing such as information retrieval or text mining, it is necessary to construct a synonym dictionary, but it is very tiresome to make it by hands. In some fields, such as aviation, synonym nouns are mixed with kanji/hiragana, katakana, alphabet and their abbreviations. As new words always come to be used, the dictionary update is a big issue. In this paper, we propose a tool for constructing a synonym dictionary. The system will return synonym candidates against a query. A synonym can be easily registered in dictionary by looking the synonym candidates. We experimented the system performance by aviation pilot report and evaluated it by average precision.
"frequency is sometimes adjusted as log(x_i + 1)" -> effective
window[2,2] was the best
spiral construction -> not better
abbreviation  constructing dictionary  dictionary  constructing  context information  information  context  synonym dictionary  dictionary  synonym  window  width  window width  vector  space  model  vector space  space model  vector space model  cosine  tf.idf  frequency  cutoff  frequency cutoff  local  context  local context  compound  noun  compound noun  spiral  construction  spiral construction  context  normalization  context normalization 
document  edit
updated at: 2007/01/23 10:01:54
© 2006 lilyx.net. Template design by Arcsin