Computational Lexicology - Revision history

Pdturney: Reverted edits by Creek (talk) to last revision by ChristianPietsch

2012-06-25T11:14:09Z

Reverted edits by Creek (talk) to last revision by ChristianPietsch

← Older revision		Revision as of 11:14, 25 June 2012
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	First, collaborative activities between computational linguists and lexicographers led to an understanding of the role that '''corpora played in creating dictionaries'''. Most computational lexicologists moved on to build large corpora to gather the basic data that lexicographers had used to create dictionaries. The ACL/DCI (Data Collection Initiative) and the LDC (Linguistic Data Consortium) went down this path. The advent of markup languages led to the creation of tagged corpora that could be more easily analyzed to create computational linguistic systems. Part-of-speech tagged corpora and semantically tagged corpora were created in order to test and develop POS taggers and word semantic disambiguation technology.		First, collaborative activities between computational linguists and lexicographers led to an understanding of the role that '''corpora played in creating dictionaries'''. Most computational lexicologists moved on to build large corpora to gather the basic data that lexicographers had used to create dictionaries. The ACL/DCI (Data Collection Initiative) and the LDC (Linguistic Data Consortium) went down this path. The advent of markup languages led to the creation of tagged corpora that could be more easily analyzed to create computational linguistic systems. Part-of-speech tagged corpora and semantically tagged corpora were created in order to test and develop POS taggers and word semantic disambiguation technology.

	The second direction was toward the creation of [~~http://www.thai-sbobet.com sbo~~] (LKBs). A Lexical Knowledge Base was deemed to be what a dictionary should be for computational linguistic purposes, especially for computational lexical semantic purposes. It was to have the same information as in a print dictionary, but totally explicated as to the meanings of the words and the appropriate links between senses. Many began creating the resources they wished dictionaries were, if they had been created for use in computational analysis. [[WordNet]] can be considered to be such a development, as can the newer efforts at describing syntactic and semantic information such as the [http://~~www~~.~~thai-sbobet~~.~~com sbobet~~] work of Fillmore. Outside of computational linguistics, the Ontology work of artificial intelligence can be seen as an evolutionary effort to build a lexical knowledge base for AI applications.		The second direction was toward the creation of [[Lexical Knowledge Bases]] (LKBs). A Lexical Knowledge Base was deemed to be what a dictionary should be for computational linguistic purposes, especially for computational lexical semantic purposes. It was to have the same information as in a print dictionary, but totally explicated as to the meanings of the words and the appropriate links between senses. Many began creating the resources they wished dictionaries were, if they had been created for use in computational analysis. [[WordNet]] can be considered to be such a development, as can the newer efforts at describing syntactic and semantic information such as the [http://framenet.icsi.berkeley.edu/ FrameNet] work of Fillmore. Outside of computational linguistics, the Ontology work of artificial intelligence can be seen as an evolutionary effort to build a lexical knowledge base for AI applications.

	==See also==		==See also==

Creek at 07:54, 25 June 2012

2012-06-25T07:54:21Z

← Older revision		Revision as of 07:54, 25 June 2012
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	First, collaborative activities between computational linguists and lexicographers led to an understanding of the role that '''corpora played in creating dictionaries'''. Most computational lexicologists moved on to build large corpora to gather the basic data that lexicographers had used to create dictionaries. The ACL/DCI (Data Collection Initiative) and the LDC (Linguistic Data Consortium) went down this path. The advent of markup languages led to the creation of tagged corpora that could be more easily analyzed to create computational linguistic systems. Part-of-speech tagged corpora and semantically tagged corpora were created in order to test and develop POS taggers and word semantic disambiguation technology.		First, collaborative activities between computational linguists and lexicographers led to an understanding of the role that '''corpora played in creating dictionaries'''. Most computational lexicologists moved on to build large corpora to gather the basic data that lexicographers had used to create dictionaries. The ACL/DCI (Data Collection Initiative) and the LDC (Linguistic Data Consortium) went down this path. The advent of markup languages led to the creation of tagged corpora that could be more easily analyzed to create computational linguistic systems. Part-of-speech tagged corpora and semantically tagged corpora were created in order to test and develop POS taggers and word semantic disambiguation technology.

	The second direction was toward the creation of [~~[Lexical Knowledge Bases]~~] (LKBs). A Lexical Knowledge Base was deemed to be what a dictionary should be for computational linguistic purposes, especially for computational lexical semantic purposes. It was to have the same information as in a print dictionary, but totally explicated as to the meanings of the words and the appropriate links between senses. Many began creating the resources they wished dictionaries were, if they had been created for use in computational analysis. [[WordNet]] can be considered to be such a development, as can the newer efforts at describing syntactic and semantic information such as the [http://~~framenet~~.~~icsi~~.~~berkeley.edu/ FrameNet~~] work of Fillmore. Outside of computational linguistics, the Ontology work of artificial intelligence can be seen as an evolutionary effort to build a lexical knowledge base for AI applications.		The second direction was toward the creation of [http://www.thai-sbobet.com sbo] (LKBs). A Lexical Knowledge Base was deemed to be what a dictionary should be for computational linguistic purposes, especially for computational lexical semantic purposes. It was to have the same information as in a print dictionary, but totally explicated as to the meanings of the words and the appropriate links between senses. Many began creating the resources they wished dictionaries were, if they had been created for use in computational analysis. [[WordNet]] can be considered to be such a development, as can the newer efforts at describing syntactic and semantic information such as the [http://www.thai-sbobet.com sbobet] work of Fillmore. Outside of computational linguistics, the Ontology work of artificial intelligence can be seen as an evolutionary effort to build a lexical knowledge base for AI applications.

	==See also==		==See also==

ChristianPietsch: +cat

2009-02-08T11:58:07Z

+cat

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	* Amsler, R.A. (1980). ''[ftp://ftp.cs.utexas.edu/pub/techreports/tr80-164a.pdf The Structure of the Merriam-Webster Pocket Dictionary]'', Doctoral Dissertation, TR-164, University of Texas, Austin.		* Amsler, R.A. (1980). ''[ftp://ftp.cs.utexas.edu/pub/techreports/tr80-164a.pdf The Structure of the Merriam-Webster Pocket Dictionary]'', Doctoral Dissertation, TR-164, University of Texas, Austin.

			[[Category:Research]]

RAM: /* Successors to Computational Lexicology */

2007-02-08T17:09:19Z

Successors to Computational Lexicology

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	===Successors to Computational Lexicology===		===Successors to Computational Lexicology===

	First, collaborative activities between computational linguists and lexicographers led to an understanding of the role that '''corpora played in creating dictionaries'''. Most computational lexicologists moved on to build large corpora to gather the basic data that lexicographers had used to create dictionaries. The ACL/DCI (Data Collection Initiative) and the LDC (Linguistic Data Consortium) went down this path. The advent of markup languages led to the creation of tagged corpora that could be more easily analyzed to create computational linguistic systems. Part-of-speech tagged corpora and semantically tagged corpora were created in order to test and develop POS taggers and semantic disambiguation technology.		First, collaborative activities between computational linguists and lexicographers led to an understanding of the role that '''corpora played in creating dictionaries'''. Most computational lexicologists moved on to build large corpora to gather the basic data that lexicographers had used to create dictionaries. The ACL/DCI (Data Collection Initiative) and the LDC (Linguistic Data Consortium) went down this path. The advent of markup languages led to the creation of tagged corpora that could be more easily analyzed to create computational linguistic systems. Part-of-speech tagged corpora and semantically tagged corpora were created in order to test and develop POS taggers and word semantic disambiguation technology.

	The second direction was toward the creation of [[Lexical Knowledge Bases]] (LKBs). A Lexical Knowledge Base was deemed to be what a dictionary should be for computational linguistic purposes, especially for computational lexical semantic purposes. It was to have the same information as in a print dictionary, but totally explicated as to the meanings of the words and the appropriate links between senses. Many began creating the resources they wished dictionaries were, if they had been created for use in computational analysis. [[WordNet]] can be considered to be such a development, as can the newer efforts at describing syntactic and semantic information such as the [http://framenet.icsi.berkeley.edu/ FrameNet] work of Fillmore. Outside of computational linguistics, the Ontology work of artificial intelligence can be seen as an evolutionary effort to build a lexical knowledge base for AI applications.		The second direction was toward the creation of [[Lexical Knowledge Bases]] (LKBs). A Lexical Knowledge Base was deemed to be what a dictionary should be for computational linguistic purposes, especially for computational lexical semantic purposes. It was to have the same information as in a print dictionary, but totally explicated as to the meanings of the words and the appropriate links between senses. Many began creating the resources they wished dictionaries were, if they had been created for use in computational analysis. [[WordNet]] can be considered to be such a development, as can the newer efforts at describing syntactic and semantic information such as the [http://framenet.icsi.berkeley.edu/ FrameNet] work of Fillmore. Outside of computational linguistics, the Ontology work of artificial intelligence can be seen as an evolutionary effort to build a lexical knowledge base for AI applications.

RAM: /* History */

2007-02-08T17:07:34Z

History

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	==History==		==History==

	In any case, it was the appearance of machine-readable dictionaries (MRDs) that gave Computational Lexicology its start as a separate discipline within Computational Linguistics. The first widely distributed MRDs were the ''Merriam-Webster Seventh Collegiate'' (W7) and the ''Merriam-Webster New Pocket Dictionary'' (MPD). Both were produced by a government-funded project at Systems Development Corporation under the direction of John Olney. They were manually keyboarded as no typesetting tapes of either book were available. Originally each was distributed on multiple reels of magnetic tape as card images with each separate word of each definition on a separate punch card with numerous special codes indicating the details of its usage in the printed dictionary. Olney outlined a grand plan for the analysis of the definitions in the dictionary, but his project expired before the analysis could be carried out. Robert Amsler at the University of Texas at Austin resumed the analysis and completed a taxonomic description of the Pocket Dictionary under NSF funding, however his project expired before the taxonomic data could be distributed. Roy Byrd et al. at IBM Yorktown Heights resumed analysis of the ''Webster's Seventh Collegiate'' following Amsler's work. Finally, in the 1980s starting with initial support from Bellcore, George Miller and Christiene Fellbaum completed the creation and wide distribution of a dictionary and its taxonomy in the [[WordNet]] project, which today stands as the most widely distributed computational lexicology resource.		In any case, it was the appearance of machine-readable dictionaries (MRDs) that gave Computational Lexicology its start as a separate discipline within Computational Linguistics. The first widely distributed MRDs were the ''Merriam-Webster Seventh Collegiate'' (W7) and the ''Merriam-Webster New Pocket Dictionary'' (MPD). Both were produced by a government-funded project at Systems Development Corporation under the direction of John Olney. They were manually keyboarded as no typesetting tapes of either book were available. Originally each was distributed on multiple reels of magnetic tape as card images with each separate word of each definition on a separate punch card with numerous special codes indicating the details of its usage in the printed dictionary. Olney outlined a grand plan for the analysis of the definitions in the dictionary, but his project expired before the analysis could be carried out. Robert Amsler at the University of Texas at Austin resumed the analysis and completed a taxonomic description of the Pocket Dictionary under NSF funding, however his project expired before the taxonomic data could be distributed. Roy Byrd et al. at IBM Yorktown Heights resumed analysis of the ''Webster's Seventh Collegiate'' following Amsler's work. Finally, in the 1980s starting with initial support from Bellcore and later funded by NSF, ARDA, DARPA, DTO, and REFLEX, George Miller and Christiene Fellbaum at Princeton University completed the creation and wide distribution of a dictionary and its taxonomy in the [[WordNet]] project, which today stands as the most widely distributed computational lexicology resource.

	Computational lexicology has contributed to our understanding of the content and limitations of print dictionaries for computational purposes. Basically, almost every portion of a print dictionary entry has been studied ranging from:		Computational lexicology has contributed to our understanding of the content and limitations of print dictionaries for computational purposes. Basically, almost every portion of a print dictionary entry has been studied ranging from:

RAM: /* History */

2007-02-08T16:22:45Z

History

RAM: /* Successors to Computational Lexicology */

2007-02-08T16:13:09Z

Successors to Computational Lexicology

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	===Successors to Computational Lexicology===		===Successors to Computational Lexicology===

	First, collaborative activities between computational linguists and lexicographers led to an understanding of the role that '''corpora played in creating dictionaries'''. Most computational lexicologists moved on to build large corpora to gather the basic data that lexicographers had used to create dictionaries. The ACL/DCI (Data Collection Initiative) and the LDC (Linguistic Data Consortium) went down this path. The advent of markup languages led to the creation of tagged corpora that could be more easily analyzed to create computational linguistic systems. Part of speech ~~tagging~~ and ~~semantic tagging of~~ corpora were created in order to test and develop POS taggers and semantic disambiguation technology.		First, collaborative activities between computational linguists and lexicographers led to an understanding of the role that '''corpora played in creating dictionaries'''. Most computational lexicologists moved on to build large corpora to gather the basic data that lexicographers had used to create dictionaries. The ACL/DCI (Data Collection Initiative) and the LDC (Linguistic Data Consortium) went down this path. The advent of markup languages led to the creation of tagged corpora that could be more easily analyzed to create computational linguistic systems. Part-of-speech tagged corpora and semantically tagged corpora were created in order to test and develop POS taggers and semantic disambiguation technology.

	The second direction was toward the creation of [[Lexical Knowledge Bases]] (LKBs). A Lexical Knowledge Base was deemed to be what a dictionary should be for computational linguistic purposes, especially for computational lexical semantic purposes. It was to have the same information as in a print dictionary, but totally explicated as to the meanings of the words and the appropriate links between senses. Many began creating the resources they wished dictionaries were, if they had been created for computational analysis ~~purposes. Ontologies can be considered to be one interpretation of this goal, for artificial intelligence systems~~. [[WordNet]] can be considered to be such a development, as can the newer efforts at describing syntactic and semantic information such as the [http://framenet.icsi.berkeley.edu/ FrameNet] work of Fillmore. Outside of computational linguistics, the Ontology work of artificial intelligence can be seen as an evolutionary effort to build a lexical knowledge base for AI applications ~~that have to deal with language~~.		The second direction was toward the creation of [[Lexical Knowledge Bases]] (LKBs). A Lexical Knowledge Base was deemed to be what a dictionary should be for computational linguistic purposes, especially for computational lexical semantic purposes. It was to have the same information as in a print dictionary, but totally explicated as to the meanings of the words and the appropriate links between senses. Many began creating the resources they wished dictionaries were, if they had been created for use in computational analysis. [[WordNet]] can be considered to be such a development, as can the newer efforts at describing syntactic and semantic information such as the [http://framenet.icsi.berkeley.edu/ FrameNet] work of Fillmore. Outside of computational linguistics, the Ontology work of artificial intelligence can be seen as an evolutionary effort to build a lexical knowledge base for AI applications.

	==See also==		==See also==

Pdturney: /* Successors to Computational Lexicology */

2007-02-07T01:37:24Z

Successors to Computational Lexicology

← Older revision		Revision as of 01:37, 7 February 2007
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	First, collaborative activities between computational linguists and lexicographers led to an understanding of the role that '''corpora played in creating dictionaries'''. Most computational lexicologists moved on to build large corpora to gather the basic data that lexicographers had used to create dictionaries. The ACL/DCI (Data Collection Initiative) and the LDC (Linguistic Data Consortium) went down this path. The advent of markup languages led to the creation of tagged corpora that could be more easily analyzed to create computational linguistic systems. Part of speech tagging and semantic tagging of corpora were created in order to test and develop POS taggers and semantic disambiguation technology.		First, collaborative activities between computational linguists and lexicographers led to an understanding of the role that '''corpora played in creating dictionaries'''. Most computational lexicologists moved on to build large corpora to gather the basic data that lexicographers had used to create dictionaries. The ACL/DCI (Data Collection Initiative) and the LDC (Linguistic Data Consortium) went down this path. The advent of markup languages led to the creation of tagged corpora that could be more easily analyzed to create computational linguistic systems. Part of speech tagging and semantic tagging of corpora were created in order to test and develop POS taggers and semantic disambiguation technology.

	The second direction was toward the creation of [[Lexical Knowledge Bases]] (LKBs). A Lexical Knowledge Base was deemed to be what a dictionary should be for computational linguistic purposes, especially for computational lexical semantic purposes. It was to have the same information as in a print dictionary, but totally explicated as to the meanings of the words and the appropriate links between senses. Many began creating the resources they wished dictionaries were, if they had been created for computational analysis purposes. Ontologies can be considered to be one interpretation of this goal, for artificial intelligence systems.		The second direction was toward the creation of [[Lexical Knowledge Bases]] (LKBs). A Lexical Knowledge Base was deemed to be what a dictionary should be for computational linguistic purposes, especially for computational lexical semantic purposes. It was to have the same information as in a print dictionary, but totally explicated as to the meanings of the words and the appropriate links between senses. Many began creating the resources they wished dictionaries were, if they had been created for computational analysis purposes. Ontologies can be considered to be one interpretation of this goal, for artificial intelligence systems. [[WordNet]] can be considered to be such a development, as can the newer efforts at describing syntactic and semantic information such as the [http://framenet.icsi.berkeley.edu/ FrameNet] work of Fillmore. Outside of computational linguistics, the Ontology work of artificial intelligence can be seen as an evolutionary effort to build a lexical knowledge base for AI applications that have to deal with language.
	~~WORDNET~~ can be considered to be such a development, as can the newer efforts at describing syntactic and semantic information such as the ~~FRAMENET~~ work of Fillmore. Outside of computational linguistics, the Ontology work of artificial intelligence can be seen as an evolutionary effort to build a lexical knowledge base for AI applications that have to deal with language.

	==See also==		==See also==

Pdturney: /* External links */

2007-02-07T01:36:10Z

External links

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	==External links==		==External links==

			* [http://framenet.icsi.berkeley.edu/ FrameNet]
	* [http://atom.research.microsoft.com/mnex/ MindNet] - a semantic network made from a dictionary, by Microsoft Research, with an [http://research.microsoft.com/nlp/Projects/MindNet.aspx overview]		* [http://atom.research.microsoft.com/mnex/ MindNet] - a semantic network made from a dictionary, by Microsoft Research, with an [http://research.microsoft.com/nlp/Projects/MindNet.aspx overview]
	* [http://en.wikipedia.org/wiki/Lexicology Wikipedia: Lexicology]		* [http://en.wikipedia.org/wiki/Lexicology Wikipedia: Lexicology]

Pdturney: /* External links */

2007-02-07T00:55:04Z

External links

← Older revision		Revision as of 00:55, 7 February 2007
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	==External links==		==External links==

			* [http://atom.research.microsoft.com/mnex/ MindNet] - a semantic network made from a dictionary, by Microsoft Research, with an [http://research.microsoft.com/nlp/Projects/MindNet.aspx overview]
	* [http://en.wikipedia.org/wiki/Lexicology Wikipedia: Lexicology]		* [http://en.wikipedia.org/wiki/Lexicology Wikipedia: Lexicology]
	* [http://en.wikipedia.org/wiki/Lexicography Wikipedia: Lexicography]		* [http://en.wikipedia.org/wiki/Lexicography Wikipedia: Lexicography]

← Older revision		Revision as of 16:22, 8 February 2007
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	==History==		==History==

	In any case, it was the appearance of machine-readable dictionaries (MRDs) that gave Computational Lexicology its start as a separate discipline within Computational Linguistics. The first widely distributed MRDs were the ''Merriam-Webster Seventh Collegiate'' (W7) and the ''Merriam-Webster New Pocket Dictionary'' (MPD). Both were produced by a government-funded project at Systems Development Corporation under the direction of John Olney. They were manually keyboarded as no typesetting tapes of either book were available. Originally each was distributed on multiple reels of magnetic tape as card images with each separate word of each definition on a separate punch card with numerous special codes indicating the details of its usage in the printed dictionary. Olney outlined a grand plan for the analysis of the definitions in the dictionary, but his project expired before the analysis could be carried out. Robert Amsler at the University of Texas at Austin resumed the analysis and completed a taxonomic description of the Pocket Dictionary under NSF funding, however his project expired before the taxonomic data could be distributed. Roy Byrd et al. at IBM Yorktown Heights resumed analysis of the ''Webster's Seventh Collegiate'' following Amsler's work. Finally, in the 1980s at Bellcore, George Miller and Christiene Fellbaum completed the creation and wide distribution of a dictionary's in the [[WordNet]] project, which today stands as ~~among~~ the most widely distributed computational lexicology resource.		In any case, it was the appearance of machine-readable dictionaries (MRDs) that gave Computational Lexicology its start as a separate discipline within Computational Linguistics. The first widely distributed MRDs were the ''Merriam-Webster Seventh Collegiate'' (W7) and the ''Merriam-Webster New Pocket Dictionary'' (MPD). Both were produced by a government-funded project at Systems Development Corporation under the direction of John Olney. They were manually keyboarded as no typesetting tapes of either book were available. Originally each was distributed on multiple reels of magnetic tape as card images with each separate word of each definition on a separate punch card with numerous special codes indicating the details of its usage in the printed dictionary. Olney outlined a grand plan for the analysis of the definitions in the dictionary, but his project expired before the analysis could be carried out. Robert Amsler at the University of Texas at Austin resumed the analysis and completed a taxonomic description of the Pocket Dictionary under NSF funding, however his project expired before the taxonomic data could be distributed. Roy Byrd et al. at IBM Yorktown Heights resumed analysis of the ''Webster's Seventh Collegiate'' following Amsler's work. Finally, in the 1980s starting with initial support from Bellcore, George Miller and Christiene Fellbaum completed the creation and wide distribution of a dictionary and its taxonomy in the [[WordNet]] project, which today stands as the most widely distributed computational lexicology resource.

	Computational lexicology has contributed to our understanding of the content and limitations of print dictionaries for computational purposes. Basically, almost every portion of a print dictionary entry has been studied ranging from:		Computational lexicology has contributed to our understanding of the content and limitations of print dictionaries for computational purposes. Basically, almost every portion of a print dictionary entry has been studied ranging from:
	* what constitutes a headword, - used to generate spelling correction lists		* what constitutes a headword - used to generate spelling correction lists;
	* what variants and inflections the headword forms, - use to empirically understand morphology		* what variants and inflections the headword forms - use to empirically understand morphology;
	* how the headword is delimited into syllables,		* how the headword is delimited into syllables;
	* how the headword is pronounced, - used in speech generation systems		* how the headword is pronounced - used in speech generation systems;
	* the parts of speech the headword takes on, - used for POS taggers		* the parts of speech the headword takes on - used for POS taggers;
	* any special subject or usage codes assigned to the headword, - used to identify text document subject matter		* any special subject or usage codes assigned to the headword - used to identify text document subject matter;
	* the headword's definitions and their syntax, - used as an aid to disambiguation of word in context		* the headword's definitions and their syntax - used as an aid to disambiguation of word in context;
	* the etymology of the headword and its use to characterize vocabulary by languages of origin, - used to characterize text vocabulary as to its languages of origin		* the etymology of the headword and its use to characterize vocabulary by languages of origin - used to characterize text vocabulary as to its languages of origin;
	* the example sentences,		* the example sentences;
	* the run-ons (additional words and multi-word expressions that are formed from the headword), and		* the run-ons (additional words and multi-word expressions that are formed from the headword); and
	* related words such as synonyms and antonyms.		* related words such as synonyms and antonyms.