Comments on: Why Transparent Search In E-Discovery Is The Answer To Victor Stanley http://www.clearwellsystems.com/e-discovery-blog/2008/08/26/why-transparent-search-in-e-discovery-is-the-answer-to-victor-stanley/ thoughts about the evolution of e-discovery Tue, 06 Jan 2009 08:11:14 +0000 http://wordpress.org/?v=2.6.1 By: NYC924 http://www.clearwellsystems.com/e-discovery-blog/2008/08/26/why-transparent-search-in-e-discovery-is-the-answer-to-victor-stanley/#comment-1118 NYC924 Tue, 16 Sep 2008 06:58:09 +0000 http://www.clearwellsystems.com/e-discovery-blog/?p=123#comment-1118 Great post Will! I believe this article clearly supports the need for Semantic Search and NLP! Semantics is the sub-field of linguistics that is devoted to the study of meaning, as expressed by words, phrases, sentences and even larger units of speech or text. Natural Language Processing (NLP) is a sub-field of artificial intelligence and computational linguistics. It studies the problems of automated generation and understanding of natural human languages by computers. Together Semantic NLP understands: Word stems – the roots of words Words & Phrases – with individual meanings of ambiguous words and phrases listed out Morphological properties of word/phrase meaning – what type of plural does it take, what type of past tense, how does it combine with affixes like “re” and “ation”, etc. Disambiguation of word senses – picking the correct word meaning of ambiguous words in context Synonymy – different words have identical or similar meanings Ontological relations between word meanings – a hierarchical grouping of meanings or a gigantic “family tree of English” with mothers, daughters and cousins Syntactic and semantic properties of words – how words act on one another. This is particularly useful with verbs in an understanding the types of objects different verb meanings can occur with. Take a look at Cognition which can be used in support of (to augment) current search tools, (such as Attenex, Autonomy, Engenium, etc). There are answers to this problem. Great post Will! I believe this article clearly supports the need for Semantic Search and NLP!

Semantics is the sub-field of linguistics that is devoted to the study of meaning, as expressed by words, phrases, sentences and even larger units of speech or text.

Natural Language Processing (NLP) is a sub-field of artificial intelligence and computational linguistics. It studies the problems of automated generation and understanding of natural human languages by computers.

Together Semantic NLP understands:
Word stems – the roots of words
Words & Phrases – with individual meanings of ambiguous words and phrases listed out
Morphological properties of word/phrase meaning – what type of plural does it take, what type of past tense, how does it combine with affixes like “re” and “ation”, etc.
Disambiguation of word senses – picking the correct word meaning of ambiguous words in context
Synonymy – different words have identical or similar meanings
Ontological relations between word meanings – a hierarchical grouping of meanings or a gigantic “family tree of English” with mothers, daughters and cousins
Syntactic and semantic properties of words – how words act on one another. This is particularly useful with verbs in an understanding the types of objects different verb meanings can occur with. Take a look at Cognition which can be used in support of (to augment) current search tools, (such as Attenex, Autonomy, Engenium, etc).

There are answers to this problem.

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