Concordance

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other,23-7-J05-1003,bq	evidence from an additional 500,000 <term>	features	</term> over <term> parse trees </term> that	#8822 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000features over parse trees that were not included in the original model.
other,14-3-J05-1003,bq	<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,11-5-E06-1018,bq	<term> sentence co-occurrences </term> as <term>	features	</term> allows for accurate results . Additionally	#10177 The combination with a two-step clustering process using sentence co-occurrences asfeatures allows for accurate results.
	identities themselves , e.g. block bigram	features	. Our <term> training algorithm </term> can	#9624 We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g. a language model score) as well as binary features based on the block identities themselves, e.g. block bigram features.
other,27-3-P05-1069,bq	model score </term> ) as well as <term> binary	features	</term> based on the <term> block </term> identities	#9613 We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g. a language model score) as well as binary features based on the block identities themselves, e.g. block bigram features.
other,21-2-E06-1022,bq	utterance </term> and <term> conversational context	features	</term> . Then , we explore whether information	#10276 First, we investigate how well the addressee of a dialogue act can be predicted based on gaze, utterance and conversational context features.
other,11-4-C04-1116,bq	most of the words with similar <term> context	features	</term> in each author 's <term> corpus </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,51-5-E06-1035,bq	<term> lexical-cohesion and conversational	features	</term> performs best , and ( 3 ) <term> conversational	#10580 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task.
other,19-6-E06-1035,bq	<term> lexical-cohesion and conversational	features	</term> , but do not change the general preference	#10630 We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks.
	</term> to fit . One of the distinguishing	features	of a more <term> linguistically sophisticated	#20006 One of the distinguishing features of a more linguistically sophisticated representation of documents over a word set based representation of them is that linguistically sophisticated units are more frequently individually good predictors of document descriptors (keywords) than single words are.
	conversation transcripts </term> etc. , have	features	that differ significantly from <term> neat	#12995 However, a great deal of natural language texts e.g., memos, rough drafts, conversation transcripts etc., have features that differ significantly from neat texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, missing periods, etc.
other,3-3-N04-1024,bq	essays </term> . This system identifies <term>	features	</term> of <term> sentences </term> based on <term>	#6693 This system identifiesfeatures of sentences based on semantic similarity measures and discourse structure.
other,6-3-C04-1068,bq	</term> . In this paper , we identify <term>	features	</term> of <term> electronic discussions </term>	#5431 In this paper, we identifyfeatures of electronic discussions that influence the clustering process, and offer a filtering mechanism that removes undesirable influences.
other,13-3-C04-1035,bq	create a set of <term> domain independent	features	</term> to annotate an input <term> dataset	#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.
	called a <term> semantic frame </term> . The key	features	of the <term> system </term> include : ( i	#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,36-1-N03-1033,bq	</term> , ( ii ) broad use of <term> lexical	features	</term> , including <term> jointly conditioning	#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,7-2-P01-1070,bq	which are built from <term> shallow linguistic	features	</term> of <term> questions </term> , are employed	#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,5-5-E06-1035,bq	</term> . Examination of the effect of <term>	features	</term> shows that <term> predicting top-level	#10531 Examination of the effect offeatures shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task.
other,8-4-P05-1069,bq	</term> can easily handle millions of <term>	features	</term> . The best system obtains a 18.6	#9634 Our training algorithm can easily handle millions offeatures.
other,5-3-P03-1022,bq	non-NP-antecedents </term> . We present a set of <term>	features	</term> designed for <term> pronoun resolution	#4003 We present a set offeatures designed for pronoun resolution in spoken dialogue and determine the most promising features.


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