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Rule Based Transliteration Scheme for English to Punjabi [<Journal>]
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| 3 |
Improving the quality of Gujarati-Hindi Machine Translation through part-of-speech tagging and stemmer-assisted transliteration [<Journal>]
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Part of Speech Tagging of Marathi Text Using Trigram Method [<Journal>]
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| 6 |
The Latent Relation Mapping Engine: Algorithm and Experiments [<Journal>]
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| 7 |
Similarity of Semantic Relations [<Journal>]
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Abstract:
There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.
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Keyword:
Artificial Intelligence; Computational Linguistics; Language; Machine Learning; Semantics
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URL: http://cogprints.org/5098/
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| 8 |
Experiments on predictability of word in context and information rate in natural language [<Journal>]
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Manin, Dmitrii. - : Keldysh Institute of Applied Mathematics (KIAM) RAS
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| 9 |
LLC : the journal of digital scholarship in the humanities ; journal of the Association for Literary and Linguistic Computing and The Association for Computers and the Humanities [<Journal>]
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Oxford : Oxford Univ. Press
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BLLDB
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OLC Linguistik
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UB Frankfurt Linguistik
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| 10 |
LLC : the journal of digital scholarship in the humanities ; journal of the Association for Literary and Linguistic Computing and The Association for Computers and the Humanities [<Journal>]
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Oxford : Oxford Univ. Press
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IDS Mannheim
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| 11 |
Corpus-based Learning of Analogies and Semantic Relations [<Journal>]
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| 12 |
Measuring praise and criticism: Inference of semantic orientation from association [<Journal>]
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| 15 |
The adaptive advantage of symbolic theft over sensorimotor toil: Grounding language in perceptual categories [<Journal>]
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| 16 |
Bootstrapping grounded symbols by minimal autonomous robots [<Journal>]
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| 17 |
Book Review--Ronald Cole (editor-in-chief), Joseph Mariani, Hans Uszkoreit, Annie Zaenen, and Victor Zue, eds., Survey of the State of the Art in Human Language Technology [<Journal>]
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| 19 |
An Analysis of English Punctuation: The Special Case of Comma [<Journal>]
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