[PDF] Top 20 Context Feature Selection for Distributional Similarity
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Context Feature Selection for Distributional Similarity
... ing distributional similarity is that it easily yields a huge amount of unique ...of context space, often up to the order of tens or hundreds of thousands, which makes the calculation computationally ... See full document
8
Combining Supervised and Unsupervised Parsing for Distributional Similarity
... better distributional similarities, we use word bigrams and trigrams as n-gram-based ...term feature pairs according to the example in Section ...the context feature formed by the pair of ... See full document
12
A Predictive Model for Tweet Sentiment Analysis and Classification
... different feature ranking techniques were presented which helps to improve text classification accuracy and filtering ...as feature selection methods to reduce the features in the dataset with Naive ... See full document
10
Automatic Extraction of Synonyms for German Particle Verbs from Parallel Data with Distributional Similarity as a Re-Ranking Feature
... re-ranking feature, we used the distributional similarity between the particle verb and its synonym can- ...the context within a given win- dow as an indicator for the similarity of the ... See full document
8
Map reduce based bag of phrases representation and distributional features incorporation for text classification
... the feature to the category for feature selection [4], ...and context of the words but concentrated only on their frequency of ...consider distributional features of terms to categorize ... See full document
9
Finding Synonyms Using Automatic Word Alignment and Measures of Distributional Similarity
... Context vectors are compared with each other in order to calculate the distributional similarity between words. Several measures have been pro- posed. Curran and Moens (2002) report on a large- scale ... See full document
8
Finding Word Substitutions Using a Distributional Similarity Baseline and Immediate Context Overlap
... the similarity of 30 noun ...semantic similarity sys- tems (see Jarmasz and Szpakowicz, 2003; Lin, 1998; Resnik, 1995 and Hirst and St Onge, 1998 amongst ... See full document
9
Improving Distributional Semantic Vectors through Context Selection and Normalisation
... word similarity task to phrase pairs, using the dataset of Mitchell and Lapata ...the similarity between the phrase vectors using cosine and com- pare the resulting scores against the gold standard using ... See full document
9
Feature Vector Quality and Distributional Similarity
... Distributional Similarity has been an active re- search area for more than a decade (Hindle, 1990), (Ruge, 1992), (Grefenstette, 1994), (Lee, 1997), (Lin, 1998), (Dagan et ...Harris distributional ... See full document
7
Probabilistic Modeling of Joint context in Distributional Similarity
... single feature that could be retrieved this way for the target word like is “Children cookies and ...independent feature vec- tor approach on a subset of the WordSim353 test- set (Finkelstein et ... See full document
10
Measuring Distributional Similarity in Context
... Vector composition methods construct representa- tions that go beyond individual words (e.g., for phrases or sentences) and thus by default obtain word meanings in context. Mitchell and Lapata (2008) investigate ... See full document
11
Instances and concepts in distributional space
... a distributional to a symbolic knowledge source in an entailment task because the distributional component licensed unwarranted inferences (white man does not entail black man, even though the phrases are ... See full document
7
Exploring Entity Relations for Named Entity Disambiguation
... (CR) feature slightly decreases the overall ...CR feature shows that of- ten candidates cannot be distinguished by the clas- sifier because they are assigned the same PageRank ... See full document
6
A More Accurate Approach to Construct Numeric Clusters
... In 2017 Shaoning Li et al proposed “A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis”. They proposed a new method, cell-dividing hierarchical clustering (CDHC), based on convex hull retraction. ... See full document
5
Comparing Similarity Measures for Distributional Thesauri
... parameters: similarity measures, frequency thresholds and association ...than similarity measures, with more agreement found for increasing ...of distributional thesauri to ...of ... See full document
8
A survey on feature selection to perform classification using Meta Heuristic algorithms in Data Mining Domain
... sets. Feature selection can be found in many areas of data mining such as classification, clustering, association rules, and ...example, feature selection is called subset or variable ... See full document
12
Sketch Techniques for Scaling Distributional Similarity to the Web
... semantic similarity between words has five good ...semantic similarity between any word pairs that are stored in the CU ...the similarity between word pairs in time ... See full document
6
Reducing Semantic Drift with Bagging and Distributional Similarity
... As shown above, semantic drift still dominates the later iterations of bootstrapping even after bag- ging. In this section, we propose distributional similarity measurements over the extracted lexi- con to ... See full document
9
Matching the Blanks: Distributional Similarity for Relation Learning
... Harris’ distributional hypothesis (Harris, 1954) to relations, as well as recent advances in learning word representations from observations of their contexts (Mikolov et ... See full document
11
Evaluating Common Strategies for the Efficiency of Feature Selection in the Context of Microarray Analysis
... To find a genetic signature, an algorithm is applied which ultimately com- bines several features into a single risk score, associated with the outcome [1] [2] [3] [4] [5]. The strength of the association between the ... See full document
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