Attributes in lexical acquisition

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Concept Learning and Categorization from the Web

In previous work, we found that a great deal of information about noun attributes can be extracted from the Web using simple text patterns, and that enriching vector-based models of concepts with this information about attributes led to drastic improvements in noun categorization. We extend this previous work in two ways: (i) by comparing concept descriptions extracted using patterns with descriptions extracted with a parser, and (ii) by developing an improved dataset balanced with respect to ambiguity, frequency, and WordNet unique beginners

Finding Attributes in the Web Using a Parser

In previous work, we found that a great deal of information about noun attributes can be extracted from the Web using simple text patterns, and that enriching vector-based models of concepts with this information about attributes led to drastic improvements in noun categorization. We extend this previous work by comparing concept descriptions extracted using patterns with descriptions extracted with a parser. Our results show that it is computationally more efficient to use simple text patterns than parsing text.

Recognition of Classical Arabic Poems

This work presents a novel method for recognizing and extracting classical Arabic poems found in textual sources. The method utilizes the basic classical Arabic poem features such as structure, rhyme, writing style, and word usage. The proposed method achieves a precision of 96.94 % while keeping a high recall value at 92.24%. The method was also used to build a prototype search engine for classical Arabic poems.

Recognition of classical Arabic poems

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