Concordance

Query features 37 (1,725.0 per million)

	In this presentation , we describe the features of and <term> requirements </term> for a genuinely useful <term> software infrastructure </term> for this purpose .	#260 In this presentation, we describe the features of and requirements for a genuinely useful software infrastructure for this purpose.
	The key features of the <term> system </term> include : ( i ) Robust efficient <term> parsing </term> of <term> Korean </term> ( a <term> verb final language </term> with <term> overt case markers </term> , relatively <term> free word order </term> , and frequent omissions of <term> arguments </term> ) .	#441 The key features of the system include: (i) Robust efficient parsing of Korean (a verb final language with overt case markers, relatively free word order, and frequent omissions of arguments).
other,7-2-P01-1070,bq	These <term> models </term> , which are built from <term> shallow linguistic features </term> of <term> questions </term> , are employed to predict target variables which represent a <term> user 's informational goals </term> .	#2152 These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user's informational goals.
other,36-1-N03-1033,bq	We present a new <term> part-of-speech tagger </term> that demonstrates the following ideas : ( i ) explicit use of both preceding and following <term> tag contexts </term> via a <term> dependency network representation </term> , ( ii ) broad use of <term> lexical features </term> , including <term> jointly conditioning on multiple consecutive words </term> , ( iii ) effective use of <term> priors </term> in <term> conditional loglinear models </term> , and ( iv ) fine-grained modeling of <term> unknown word features </term> .	#2946 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features.
other,66-1-N03-1033,bq	We present a new <term> part-of-speech tagger </term> that demonstrates the following ideas : ( i ) explicit use of both preceding and following <term> tag contexts </term> via a <term> dependency network representation </term> , ( ii ) broad use of <term> lexical features </term> , including <term> jointly conditioning on multiple consecutive words </term> , ( iii ) effective use of <term> priors </term> in <term> conditional loglinear models </term> , and ( iv ) fine-grained modeling of <term> unknown word features </term> .	#2977 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features.
other,12-3-P03-1002,bq	It is based on : ( 1 ) an extended set of <term> features </term> ; and ( 2 ) <term> inductive decision tree learning </term> .	#3762 It is based on: (1) an extended set offeatures; and (2) inductive decision tree learning.
other,5-3-P03-1022,bq	We present a set of <term> features </term> designed for <term> pronoun resolution </term> in <term> spoken dialogue </term> and determine the most promising <term> features </term> .	#4003 We present a set offeatures designed for pronoun resolution in spoken dialogue and determine the most promising features.
other,18-3-P03-1022,bq	We present a set of <term> features </term> designed for <term> pronoun resolution </term> in <term> spoken dialogue </term> and determine the most promising <term> features </term> .	#4016 We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promisingfeatures.
other,13-3-C04-1035,bq	We then use the <term> predicates </term> of such <term> clauses </term> to create a set of <term> domain independent features </term> to annotate an input <term> dataset </term> , and run two different <term> machine learning algorithms </term> : <term> SLIPPER </term> , a <term> rule-based learning algorithm </term> , and <term> TiMBL </term> , a <term> memory-based system </term> .	#5197 We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system.
other,5-5-C04-1035,bq	The results show that the <term> features </term> in terms of which we formulate our <term> heuristic principles </term> have significant <term> predictive power </term> , and that <term> rules </term> that closely resemble our <term> Horn clauses </term> can be learnt automatically from these <term> features </term> .	#5245 The results show that thefeatures in terms of which we formulate our heuristic principles have significant predictive power, and that rules that closely resemble our Horn clauses can be learnt automatically from these features.
other,35-5-C04-1035,bq	The results show that the <term> features </term> in terms of which we formulate our <term> heuristic principles </term> have significant <term> predictive power </term> , and that <term> rules </term> that closely resemble our <term> Horn clauses </term> can be learnt automatically from these <term> features </term> .	#5275 The results show that the features in terms of which we formulate our heuristic principles have significant predictive power, and that rules that closely resemble our Horn clauses can be learnt automatically from thesefeatures.
other,6-3-C04-1068,bq	In this paper , we identify <term> features </term> of <term> electronic discussions </term> that influence the <term> clustering process </term> , and offer a <term> filtering mechanism </term> that removes undesirable influences .	#5431 In this paper, we identifyfeatures of electronic discussions that influence the clustering process, and offer a filtering mechanism that removes undesirable influences.
other,11-4-C04-1116,bq	According to our assumption , most of the words with similar <term> context features </term> in each author 's <term> corpus </term> tend not to be <term> synonymous expressions </term> .	#6170 According to our assumption, most of the words with similar context features in each author's corpus tend not to be synonymous expressions.
other,4-3-C04-1128,bq	We show that various <term> features </term> based on the structure of <term> email-threads </term> can be used to improve upon <term> lexical similarity </term> of <term> discourse segments </term> for <term> question-answer pairing </term> .	#6286 We show that variousfeatures based on the structure of email-threads can be used to improve upon lexical similarity of discourse segments for question-answer pairing.
other,3-3-N04-1024,bq	This system identifies <term> features </term> of <term> sentences </term> based on <term> semantic similarity measures </term> and <term> discourse structure </term> .	#6693 This system identifiesfeatures of sentences based on semantic similarity measures and discourse structure.
other,6-4-N04-1024,bq	A <term> support vector machine </term> uses these <term> features </term> to capture <term> breakdowns in coherence </term> due to relatedness to the <term> essay question </term> and relatedness between <term> discourse elements </term> .	#6711 A support vector machine uses thesefeatures to capture breakdowns in coherence due to relatedness to the essay question and relatedness between discourse elements.
other,38-4-N04-4028,bq	The <term> information extraction system </term> we evaluate is based on a <term> linear-chain conditional random field ( CRF ) </term> , a <term> probabilistic model </term> which has performed well on <term> information extraction tasks </term> because of its ability to capture arbitrary , overlapping <term> features </term> of the input in a <term> Markov model </term> .	#6849 The information extraction system we evaluate is based on a linear-chain conditional random field (CRF), a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlappingfeatures of the input in a Markov model.
other,12-4-I05-5003,bq	Our results show that <term> MT evaluation techniques </term> are able to produce useful <term> features </term> for <term> paraphrase classification </term> and to a lesser extent <term> entailment </term> .	#8409 Our results show that MT evaluation techniques are able to produce usefulfeatures for paraphrase classification and to a lesser extent entailment.
other,14-3-J05-1003,bq	A second <term> model </term> then attempts to improve upon this initial <term> ranking </term> , using additional <term> features </term> of the <term> tree </term> as evidence .	#8703 A second model then attempts to improve upon this initial ranking, using additionalfeatures of the tree as evidence.
other,19-4-J05-1003,bq	The strength of our <term> approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary set of <term> features </term> , without concerns about how these <term> features </term> interact or overlap and without the need to define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term> into account .	#8729 The strength of our approach is that it allows a tree to be represented as an arbitrary set offeatures, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account.


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