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  • Category » word-sense disambiguation « @ The Clog
    list for tools that might help in analyzing preposition lexical samples In this request I indicated a need for software that would specifically provide enhanced word sketch analysis I only received a couple of replies one of which asked what I meant by this term Read the rest of this entry Prepositions word sense disambiguation word sketches Preposition Disambiguation State of the Art Posted in October 30 2009 12 17 pmh Ken No Comments Efforts to disambiguate prepositions have been increasing in the last few years with claims of precision reaching 0 80 All such efforts present results in statistical generalities with identification of the key factors related to the results Continued progress in these efforts requires a close examination of limitations that have been noted In addition the exploitation Read the rest of this entry Prepositions word sense disambiguation collocational features semantic role features syntactic features word sense disambiguation Electronic dictionaries of the future Posted in August 5 2009 4 42 pmh Ken 1 Comment Current electronic dictionaries are presently little more than transcriptions of paper dictionaries To be sure they have a lot more information than is present in the print versions But they are not really designed to support natural language processing The major needs of the future are 1 a set of instances illustrating each sense of Read the rest of this entry Construction patterns Electronic dictionaries Represenation of meaning Usage examples word sense disambiguation frame elements FrameNet semantic dependency graphs sentence dictionary Categories Select Category Construction patterns 2 Content Analysis 1 digraph analysis 7 Electronic dictionaries 4 Usage examples 2 Prepositions 5 Represenation of meaning 2 Semantic Analysis 1 Uncategorized 2 Semantic Primitives 1 word sense disambiguation 4 Tags cognitive neuroscience collocational features computational lexicons dictionary clues digraph analysis Firth maxim frame to frame relations

    Original URL path: http://www.clres.com/blog/?cat=6 (2016-02-11)
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  • Tag » Firth maxim « @ The Clog
    2 Content Analysis 1 digraph analysis 7 Electronic dictionaries 4 Usage examples 2 Prepositions 5 Represenation of meaning 2 Semantic Analysis 1 Uncategorized 2 Semantic Primitives 1 word sense disambiguation 4 Tags cognitive neuroscience collocational features computational lexicons dictionary clues digraph analysis Firth maxim frame to frame relations frame elements FrameNet inference steps MCCA meaning fragments natural language processing ontologies preposition classes preposition complements preposition meaning representation of meaning semantic

    Original URL path: http://www.clres.com/blog/?tag=firth-maxim (2016-02-11)
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  • Examining the Twitterverse with Content Analysis: A First Look @ The Clog
    can be used as a classifier To do this a file containing multiple texts is processed to serve as the reference or marker set In the case of the Twitter data the file was divided into two texts one for instances deemed to be real questions and one for instances deemed not to be real questions See the Dent Paul paper for a more complete description of these two sets Then instances to be classified are processed one by one with each instance first analyzed as an ordinary text with emphasis and context scoring and next compared to the reference texts using the nearest distance as the criterion for the classification In this first look at the Twitter data we simply put the two files questions txt and notquestions txt into one file with separators to reflect the two sets We then processed this file in about 4 seconds The size of this file is comparable to Hamlet about 30 000 tokens about 10 times as large as a smaller demonstration file of five texts The first observation about the data is the percentage of words that could be categorized For the demonstration file about 90 percent of the words were classified this is roughly what occurs for modern texts For Hamlet 83 percent are classified this reflects the change in English over 400 years For the Twitter data only 77 percent of the words were classified this is a clear indication that with Twitter a significant change in the language is occurring I next examined the various statistics generated by MCCA to attempt to discern any differences between the Questions and Questions There were non zero distances between the two sets for both emphasis scores and context scores it was not immediately clear whether these differences were important One of the result sets is a difference analysis that shows the emphasis categories that are most different between the two sets In this case two categories looked most different Move in Space forward close side and Who Where who which someone something these looked interesting The next step was to classify the instances In this initial examination I used the full Twitter data as the reference set and then classified each instance I did not create a subset of the data to use as a training set against which to classify the remaining instances as the test set This would have been more rigorous but I m not sure that it would have been necessary in this first look In this first test I used the emphasis score distance as the criterion for classification The results are shown in the following table MCCA Results MT Questions MT Questions MCCA Question 708 433 MCCA Question 444 717 For comparison the Dent Paul results are shown the following table Dent Paul Results MT Questions MT Questions Parser Question 898 486 Parser Question 254 666 The MCCA results have a precision of 0 62050 a recall of 0 61458 and an accuracy of

    Original URL path: http://www.clres.com/blog/?p=195 (2016-02-11)
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  • Category » Content Analysis « @ The Clog
    Select Category Construction patterns 2 Content Analysis 1 digraph analysis 7 Electronic dictionaries 4 Usage examples 2 Prepositions 5 Represenation of meaning 2 Semantic Analysis 1 Uncategorized 2 Semantic Primitives 1 word sense disambiguation 4 Tags cognitive neuroscience collocational features computational lexicons dictionary clues digraph analysis Firth maxim frame to frame relations frame elements FrameNet inference steps MCCA meaning fragments natural language processing ontologies preposition classes preposition complements preposition meaning

    Original URL path: http://www.clres.com/blog/?cat=36 (2016-02-11)
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  • Tag » MCCA « @ The Clog
    Construction patterns 2 Content Analysis 1 digraph analysis 7 Electronic dictionaries 4 Usage examples 2 Prepositions 5 Represenation of meaning 2 Semantic Analysis 1 Uncategorized 2 Semantic Primitives 1 word sense disambiguation 4 Tags cognitive neuroscience collocational features computational lexicons dictionary clues digraph analysis Firth maxim frame to frame relations frame elements FrameNet inference steps MCCA meaning fragments natural language processing ontologies preposition classes preposition complements preposition meaning representation of

    Original URL path: http://www.clres.com/blog/?tag=mcca (2016-02-11)
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  • Tag » natural language processing « @ The Clog
    Category Construction patterns 2 Content Analysis 1 digraph analysis 7 Electronic dictionaries 4 Usage examples 2 Prepositions 5 Represenation of meaning 2 Semantic Analysis 1 Uncategorized 2 Semantic Primitives 1 word sense disambiguation 4 Tags cognitive neuroscience collocational features computational lexicons dictionary clues digraph analysis Firth maxim frame to frame relations frame elements FrameNet inference steps MCCA meaning fragments natural language processing ontologies preposition classes preposition complements preposition meaning representation

    Original URL path: http://www.clres.com/blog/?tag=natural-language-processing (2016-02-11)
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  • Enhanced Word Sketches @ The Clog
    disambiguation WSD I ll stick here to work in preposition WSD since there is a smaller literature but will sufficiently illustrate the process The major contributions stem from the preposition disambiguation task of SemEval 2007 and a comprehensive treatment by O Hara Wiebe 2009 In SemEval 2007 there were three particpants Ye Baldwin Yuret and Popescu et al Hovy et al built upon these results to make further advances in preposition disambiguation While these efforts have identified and refined the set of features useful in preposition disambiguation up to 85 percent accuracy I don t think they have fully explored the potential feature space To consider how enhanced word sketches might contribute further we need to examine in detail what these features are and how they correspond to what s in a dictionary Collocation features the context are the most important in preposition disambiguation While the earlier studies focused on context windows Hovy et al found that the governor the word to which a prepositional phrase is attached and the object are of key importance both of which are generally found within context windows with the governor of greater importance than the object Dictionaries do not generally provide any information about the class of the governor One exception to this may be found in definitions of the preposition of see the online preposition project data where several senses characterize the governor Conversely the definitions of many verbs and nouns will identify explicitly or implicitly an association with a specific preposition For example move go in a specified direction links well with a sense of to expressing motion in the direction of a particular location this is the kind of linkage chain clarifying relationship investigated in Popescu et al More preposition definitions characterize the preposition object although some specify the semantic role of the object rather than properties of the object itself In the several studies syntactic and semantic features are determined to be of less importance However a significant problem with this conclusion is the issue of how well available tools characterize these features In these studies semantic characterizations have examined only WordNet based features Since WordNet makes no claims about semantic classes this conclusion must be held in abeyance The Preposition Project TPP has characterized many properties of each preposition sense These have not been fully investigated in the several studies TPP labels each sense according to its Quirk syntax Hovy et al used fronting capitalization as a feature such a feature could be important for some prepositions but not for others TPP identifies FrameNet frames and frame elements associated with each sense based on the available corpus these constitute an additional type of semantic characterization possible relevant features that could be investigated TPP also identifies other prepositions that can substitute these were used by Yuret who found that substitutions while useful for disambiguation did not work as well as for verbs and nouns Potentially these substitutions could be examined in conjunction with the preposition classes built from the

    Original URL path: http://www.clres.com/blog/?p=179 (2016-02-11)
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  • Category » Prepositions « @ The Clog
    the lexicographer These types were grouped together into 20 larger classes The assignment of these two labels was a local decision that is without any a priori theoretical perspective Once completed the overall collection of these classifications are amenable to more Read the rest of this entry digraph analysis Prepositions digraph analysis frame elements preposition classes semantic relations Preposition Disambiguation State of the Art Posted in October 30 2009 12 17 pmh Ken No Comments Efforts to disambiguate prepositions have been increasing in the last few years with claims of precision reaching 0 80 All such efforts present results in statistical generalities with identification of the key factors related to the results Continued progress in these efforts requires a close examination of limitations that have been noted In addition the exploitation Read the rest of this entry Prepositions word sense disambiguation collocational features semantic role features syntactic features word sense disambiguation Preposition meaning syntactic reflex and semantic relation coverage Posted in August 18 2009 5 21 pmh Ken No Comments The Oxford Dictionary of English defines preposition as a word governing and usually preceding a noun or pronoun and expressing a relation to another word or element in the clause A few years ago Bill Dolan quoting Lucy Vanderwende both of the Microsoft NLP group suggested that prepositions are essentially just syntactic reflexes that have Read the rest of this entry Prepositions preposition complements preposition meaning semantic relations Page 1 of 2 1 2 Next Categories Select Category Construction patterns 2 Content Analysis 1 digraph analysis 7 Electronic dictionaries 4 Usage examples 2 Prepositions 5 Represenation of meaning 2 Semantic Analysis 1 Uncategorized 2 Semantic Primitives 1 word sense disambiguation 4 Tags cognitive neuroscience collocational features computational lexicons dictionary clues digraph analysis Firth maxim frame to frame relations frame elements

    Original URL path: http://www.clres.com/blog/?cat=3 (2016-02-11)
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