Outlier Detection in Automatic Collocation Extraction

Suárez, Octavio Santana; Berriel, Isabel Sánchez; Aguiar, José Pérez; Rodríguez, Virginia Gutiérrez

doi:10.1016/j.sbspro.2015.07.463

Cited by 2 publications

(1 citation statement)

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“…Then, for better coverage of collocations existing in one language, collocation in this bank which had a nominal role of morphological have been generalized by a semantic dictionary in order to cover words which are in their semantic category. [13] has considered the issue of the effect of corpus size on threshold of separating collocations form candidate compounds after comparison of Association Measures and has provided a method for automatic extraction of collocations which are independent of the corpus size using outlier data identification in Statistics.…”

Section: Related Workmentioning

confidence: 99%

Improving the Collocation Extraction Method Using an Untagged Corpus for Persian Word Sense Disambiguation

Riahi¹,

Sedghi²

2016

JCC

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Word sense disambiguation is used in many natural language processing fields. One of the ways of disambiguation is the use of decision list algorithm which is a supervised method. Supervised methods are considered as the most accurate machine learning algorithms but they are strongly influenced by knowledge acquisition bottleneck which means that their efficiency depends on the size of the tagged training set, in which their preparation is difficult, time-consuming and costly. The proposed method in this article improves the efficiency of this algorithm where there is a small tagged training set. This method uses a statistical method for collocation extraction from a big untagged corpus. Thus, the more important collocations which are the features used for creation of learning hypotheses will be identified. Weighting the features improves the efficiency and accuracy of a decision list algorithm which has been trained with a small training corpus.

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Section: Related Workmentioning

confidence: 99%