Feature sets used for individualized predictions
UniMelb NLP CORE: Integrating predictions from multiple domains and feature sets for estimating semantic textual similarity
... [email protected] , [email protected] Abstract In this paper we present our systems for cal- culating the degree of semantic similarity be- tween two texts that we submitted to the Se- mantic Textual Similarity task at ...
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Improving the Character Ngram Model for the DSL Task with BM25 Weighting and Less Frequently Used Feature Sets
... In previous editions, named entities received much attention to such an extent that, in the 2015 edition, the documents of one of the test sets was preprocessed so as to mask them (Zampieri et al., 2015b). Their ...
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Scrutable Feature Sets for Stance Classification
... we used Mallet (McCallum, 2002), which im- plements topic modelling using Latent Dirich- let Allocation (LDA) (Blei et ...and used the default setting of the top 19 words for each ...
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Comparing default predictions in the rating industry for different sets of obligors
... Abstract We generalize the refinement ordering for well calibrated probability forecasters to the case were the debtors under consideration are not necessarily identical. This ordering is consistent with many well known ...
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Automatic Discovery of Feature Sets for Dependency Parsing
... 9 Feature Discovery on a Corpus of English The training and development sets of the CoNLL 2008 corpus contain text from the Wall Street Journal ...We used a beam width of 4 and the tests were ...
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Unsupervised feature selection for large data sets
... be used in very large data sets that may or may not fit in the memory of a computer, unlike ...data sets containing noise ...data sets with no noise features we set this parameter to ...data ...
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Feature Selection Used for Retreving and to Classifying Images
... Figure 4: Vocabulary construction Fig 5: BoW representation 6. Classification of Images Image classification is also an active area in the field of machine learning, in which it uses algorithms that map sets of ...
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Annotation-based feature extraction from sets of SBML models
... annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels ...these sets, we analyzed and extracted concepts ...
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Arabic Named Entity Recognition using Optimized Feature Sets
... each feature for each class and then the performance obtained when the best feature-set is ...automatic feature selection methods on the same ...
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Video anomaly detection with compact feature sets for online performance
... Results for scenes Peds1 and Peds2 of the UCSD dataset are tabulated in Tables IV and V, respectively. Results for the other compared methods are as reported in the corresponding referred publication. As expected, ...
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Focused training sets to reduce noise in NER feature models
... their feature set, cosine similarity is used to retrieve documents containing similar entity instances in an effort to achieve a more relevant feature set that will result in more likely output label ...
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BOUNCE: Sentiment Classification in Twitter using Rich Feature Sets
... Test 1572 601 1640 3813 Table 2: Number of instances used in TaskA and TaskB decide on the sentiment label of a tweet. For each tweet, the probabilities of belonging to the posi- tive class (P rob pos ) and ...
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The Impact of Task Oriented Feature Sets on HMMs for Dialogue Modeling
... different feature sets is an important step toward learning effective models of task-oriented ...be used when utilizing traditional goodness-of-fit metrics, which are appropriate for descriptive ap- ...
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Efficient Combination of Ranked Result Sets in Multi-Feature Applications
... workflow & process integration collaboration publishing & distribution A major drawback in recent knowledge management approaches has been the costly overhead of administration and management of data. As all the ...
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Feature Analysis of Coronary Artery Heart Disease Data Sets
... values above the threshold are dealt with at one child and the remaining at another child. For each attribute the gain ratio is calculated. The attribute with the maximum gain ratio will be the root node. In order to ...
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Annotation-based feature extraction from sets of SBML models
... annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels ...these sets, we analyzed and extracted concepts ...
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Assessing the Prosody of Non Native Speakers of English: Measures and Feature Sets
... then used to compute the gold standard using different methods, and a series of regression experiments is conducted to evaluate their impact on the performance of a regression model predicting the degree of ...
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Detection of Fast-Flux Networks Using Various DNS Feature Sets
... Jaccard Index over bigram distributions of 90 test domains between benign and fast-flux domain names, as well as domain names extracted from traditional botnets. Note that fast-flux domains used in our experiments ...
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Feature Subset Selection using Rough Sets for High Dimensional Data
... single feature and thus dimensionality is drastically ...proposed Feature Subset Selection can be enhanced to handle text and image ...be used with RVM for generating good classification ...
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An Exponential Kernel based Fuzzy Rough Sets Model for Feature Selection
... data sets are deleted and all the algorithms produce distinct subset of ...different feature subsets to produce the best classification ...is used for feature selection as compared to other ...
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