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1
Using Empirically Constructed Lexical Resources for Named Entity Recognition. Biomed Inform Insights
In: http://www.la-press.com/redirect_file.php?fileId%3D5041%26fileType%3Dpdf%26filename%3D3738-BII-Using-Empirically-Constructed-Lexical-Resources-for-Named-Entity-Recog.pdf (2013)
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2
Lexicalised compositionality
In: http://www.cl.cam.ac.uk/~ah433/lc-semprag.pdf
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3
Lexicalised compositionality
In: http://www.cl.cam.ac.uk/~aac10/papers/lc3-0web.pdf
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4
Lexicalised compositionality
In: http://www.cl.cam.ac.uk/%7Eaac10/papers/lc1-0web.pdf
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5
technology in Italy
In: http://www.matecat.com/wp-content/uploads/2013/12/fulltext.pdf
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6
Evaluation of Simple Distributional Compositional Operations on Longer Texts
In: http://www.cl.cam.ac.uk/~lr346/pubs/lrec14.pdf
Abstract: Distributional semantic models have been effective at representing linguistic semantics at the word level, and more recently re-search has moved on to the construction of distributional representations for larger segments of text. However, it is not well understood how the composition operators that work well on short phrase-based models scale up to full-length sentences. In this paper we test several simple compositional methods on a sentence-length similarity task and discover that their performance peaks at fewer than ten operations. We also introduce a novel sentence segmentation method that reduces the number of compositional operations.
Keyword: distributional semantics; sentence segmentation; sentence similarity
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.650.5542
http://www.cl.cam.ac.uk/~lr346/pubs/lrec14.pdf
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7
1 Executive Summary
In: http://drops.dagstuhl.de/opus/volltexte/2014/4438/pdf/dagrep_v003_i011_p079_s13462.pdf
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