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  • Preposition Disambiguation: State of the Art @ The Clog
    maximum entropy with three types of features collocational features open class words WordNet synsets named entities surrounding words and surrounding supersenses syntactic features parts of speech chunk tags and types and parse tree features and semantic role features semantic role tags attached verbs and verb relative positions They found that collocational features played the most significant role Tratz Hovy also used maximum entropy but focused on syntactic structures for identifying words of interest They found the verb noun dominating the prepositional phrase the noun verb object of the preposition the subject of the dominating verb neighboring prepositional phrases and words within 2 positions of the target For each word so identified they then constructed feature sets consisting of the word itself the lemma part of speech synset members hypernyms and capitalization They achieved an 8 percent improvement in disambiguation and concluded that words bearing some syntactic relation to the target preposition were responsible for the improvement Yuret examines the context of the target preposition using a statistical language model His method is applied more generally to content words where he looks at the general task of word sense disambiguation using possible substitutes as a way of selecting an applicable sense The method depends on a rich set of substitutes which is not the case for prepositions He makes the point that good quality substitutes for prepositions are unlikely since they play a unique role in language Notwithstanding his results are sufficiently above the baseline and are supportive of the Ye Baldwin conclusions that collocational features are important In addition his results suggest that the TPP data for other prepositions associated with each sense might allow corpus instances for the different prepositions to be studied together Popescu et al also examine the context of the preposition but with the hypothesis that

    Original URL path: http://www.clres.com/blog/?p=103 (2016-02-11)
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  • Preposition Classes: General @ The Clog
    phrases The 20 classes of prepositions are Activity Agent Backdrop Barrier Cause Doubles Exception Means Medium Membership Party Possession Quantity Scalar Spatial Substance Tandem Target Temporal Topic and Void The 12 primitives in the frame element hierarchy are Cause State Degree Entity Role Purpose Instrument Phenomenon Time Path Reason and Topic In the draft paper Analysis of Preposition Classes I provide a detailed examination of each class providing a link to the digraph for the class and identifying the FrameNet frames and frame elements evoked by the class digraph analysis Prepositions digraph analysis frame elements preposition classes semantic relations You can follow any responses to this entry through the RSS 2 0 feed You can leave a response or trackback from your own site Leave a Reply Click here to cancel reply Name required Mail will not be published required Website XHTML You can use these tags a href title abbr title acronym title b blockquote cite cite code del datetime em i q cite strike strong 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

    Original URL path: http://www.clres.com/blog/?p=120 (2016-02-11)
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  • Preposition meaning: syntactic reflex and semantic relation coverage @ The Clog
    of the sentence filling the Profiled item frame element Lest one attribute the triggering to the special case of an idiomatic phrasal preposition note that above and over have similar senses that would likewise trigger this frame without any sufficient context where this could be called a syntactic reflex However the syntactic reflex of preposition meaning is also clearly present The UMLS Specialist Lexicon includes a vast number of prepositions that are closely related to specific verbs nouns and adjectives almost 28 000 There are 30 instances of between shown as a preposition complement of verbs such as alternate choose distinguish oscillate and waver Now what is interesting is to look at the definitions of these verbs The verb alternate has one definition change repeatedly between two contrasting conditions and oscillate has a definition vary or fluctuate between two states limits opinions etc Notice that the definitions contain the preposition between with an object that serves as a placeholder These senses therefore seem to demand the use of this preposition in their use The Oxford sentence dictionary shows 16 of 20 examples for alternate and 17 of 20 examples for oscillate using between An open question is whether these verb senses dictate which of the 9 senses of between should be selected Similarly a search of verb definitions in the Oxford dictionary shows a total of 66 cases where between appears in a similar form of a slot with a placeholder object Thus the UMLS set constructed by hand could easily be extended to include these other verbs that may be said to take between as a syntactic reflex O Hara and Wiebe 2009 spend considerable effort in attempting to piece together a workable semantic relations inventory In addition to FrameNet they examine semantic relations from the Penn Treebank the Factotum knowledge base Cyc and Conceptual Graphs While they eventually develop an inventory using primarily FrameNet the kinds of semantic relations introduced by the other sources seem to be not relevant to prepositions Semantic relations used as the backbone of WordNet such as synonymy antonymy and meronymy are also relatively rare as meanings of prepositions The core WordNet relation of hypernymy may be found in one sense of of However in general the presence of these relations as meanings of prepositions is rare I suggest that the main reason that many semantic relations are not to be found among preposition meanings is that prepositions only encode a small subset of all the semantic relations I suggest that the richness of the English language is embodied in the verbs each of which also constitutes a semantic relation between a subject and an object This seems to me to be the main reason why Hobbs observation is true The conclusion I reach from all of this is that there is meaning to be contributed to an utterance by prepositions Frequently this meaning is strongly tied to the meanings of other words And finally we cannot expect prepositions to cover all the semantic

    Original URL path: http://www.clres.com/blog/?p=53 (2016-02-11)
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  • Category » Prepositions « @ 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/?cat=3&paged=2 (2016-02-11)
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  • Category » digraph analysis « @ The Clog
    i e nodes that are mutually reachable In the frame element hierarchy ten strong components or circularities have arisen In an effort to provide a strict hierarchy for frame elements these circularities need to be broken In doing this it is instructive to Read the rest of this entry digraph analysis digraph analysis frame elements frame to frame relations FrameNet strong components Analyzing the frame element digraph Initial steps Posted in August 13 2009 12 31 pmh Ken 2 Comments The main objectives of analyzing the frame element digraph whose derivation was described in a previous post are to identify the primitive frame elements and to show the derivational hierarchy of each of the other frame elements There are 1015 frame elements in the frame element dictionary Based on the hypernym relationships this yields a Read the rest of this entry digraph analysis digraph analysis frame elements FrameNet Page 2 of 2 Previous 1 2 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

    Original URL path: http://www.clres.com/blog/?cat=16&paged=2 (2016-02-11)
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  • Lexicologic Insights from Cognitive Neuroscience @ The Clog
    e g Chinese words for numbers are much shorted to pronounce than English numbers leading to greater efficiency in making simple calculations The point of all this is that it is very easy to get hung up on and locked into mathematical formalisms which may become problematic because of some ultimate inconsistency Mathematicians particularly around the early 1900s were very keen on building a logical structure for mathematics Most notable was Bertrand Russell s Principia Mathematica These efforts were derailed by Gödel s incompleteness theorems which carried over to Turing machines and the advent of the digital computer Lessons from these efforts should carry over to those who are attempting to develop ontologies They will always be incomplete In addition the efforts to develop ontologies may obscure important aspects of our attempts to build dictionaries They are focused too much on hierarchical representations i e following the hypernymic backbone and do not take into account all the many activations that may occur when we are confronted with bringing to bear knowledge about a word See the Suggested Upper Merged Ontology SUMO the Cyc ontology WordNet and the Semantic Web The problem is that there are too many pieces of information associated with a word all the context that needs to be brought to bear i e corpus linguistics syntactic knowledge semantic knowledge relations with other parts of the lexicon culture etc In the parser I use the lexicon is designed for rapid access to syntactic information It uses a hashing technique to access a word i e it does not proceed by alphabetic lookup and stores the word s information in lists These lists are nested with syntactic categories at the first level and possibly other information as sublists providing limited amounts of context subcategorization patterns or various irregularities There is a growing field of computational neurolinguistics see upcoming workshop as well as attention being paid to optimal organization of the lexicon another upcoming workshop At the moment it seems that cognitive neuroscience is focused primarily on comparing models of the neural activity with various language resources Important studies in this area include Mitchell et al 2008 and Murphy et al 2009 The latter study particularly makes use of EEG data but it was designed primarily to determine whether a priori semantic features were correlated with activation of particular brain regions There are many fragments of meaning associated with individual words so this kind of study is only a first step As I continue to investigate developments in these areas I will be attempting to identify mechanisms that can be used in the design of computational lexicons As an example of this consider the first step of recognizing words the hashing step I mentioned above In my parser the look up phase computes a hash value for each word and accesses the location of its definition in the dictionary The first step is to create an intermediate memory of all the parts of speech associated with the word including the

    Original URL path: http://www.clres.com/blog/?p=132 (2016-02-11)
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  • Semantic Analysis @ The Clog
    a sentence and beyond e g in full texts These tasks include such things as semantic role labeling semantic relation analysis and coreference resolution Now what we d really like here is a contribution from the lexicon i e a representation that can be plugged into these analyses There has been some beginning of this task with frame semantics via the FrameNet project where many lexical units trigger a frame consisting of frame elements and where the frame definition and frame elements may be viewed as definitional in form With its foray into full text analysis there is some beginning of intersentential relations Another useful formalism is the lexicon development environment for use with unification based linguistic formalisms e g the LKB system which incorporates lexical items in HPSG systems Rhetorical Structure Theory provides another way of examining a text in its totality but this theory has not been developed much of late Importantly as John Sowa points out in The Role of Logic and Ontology in Language and Reasoning Forty years of research in logic linguistics and AI has not produced a successful implementation no computer system based on that approach can read one page of a high school textbook and use the results to answer the questions and solve the problems as well as a B student Semantic analysis plays a large role in what may be considered its ultimate application areas such as information extraction question answering document summarization machine translation paraphrasing and recognizing textual entailment RTE The contributions of semantic analysis are difficult to assess in these tasks Each has developed its own methods and there doesn t seem to be any overarching analysis that identifies the specific contribution of semantic components In many of these areas investigators have begun to perform ablation analyses that seek to identify the relative contributions of its components In RTE the situation has become somewhat dire where investigators do not have a clear idea of how results are being achieved Sammons et al 2010 in Ask Not What Textual Entailment Can Do for You have proposed a community wide effort to annotate RTE examples with the inference steps required to reach a decision about the example This indicates the scale of the effort Semantic Analysis inference steps representation of meaning word sense disambiguation You can follow any responses to this entry through the RSS 2 0 feed You can leave a response or trackback from your own site 1 Comment Deniz Yuret says December 30 2010 at 10 10 pm You make a distinction between dictionary issues and characterization issues whose definitions were not very clear to me In my simplified view of the language world there are words phrases and what they refer to on the one hand and relations between them on the other The first involves figuring out named entities word sense and co reference the second involves figuring out syntax semantic roles and relations etc At first I thought your dictionary characterization dichotomy was similar but

    Original URL path: http://www.clres.com/blog/?p=159 (2016-02-11)
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  • Category » Semantic Analysis « @ The Clog
    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 meaning

    Original URL path: http://www.clres.com/blog/?cat=31 (2016-02-11)
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