Titlebook: Natural Language Processing and Information Systems; 20th International C Chris Biemann,Siegfried Handschuh,Elisabeth Métais Conference pro

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書目名稱Natural Language Processing and Information Systems影響因子(影響力)




書目名稱Natural Language Processing and Information Systems影響因子(影響力)學(xué)科排名




書目名稱Natural Language Processing and Information Systems網(wǎng)絡(luò)公開度




書目名稱Natural Language Processing and Information Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Natural Language Processing and Information Systems被引頻次




書目名稱Natural Language Processing and Information Systems被引頻次學(xué)科排名




書目名稱Natural Language Processing and Information Systems年度引用




書目名稱Natural Language Processing and Information Systems年度引用學(xué)科排名




書目名稱Natural Language Processing and Information Systems讀者反饋




書目名稱Natural Language Processing and Information Systems讀者反饋學(xué)科排名

ASSET

施加

In Defense of Word Embedding for Generic Text Representationmantic relations between words from a large text corpus. It has the advantage of not requiring any tagging while training. The prevailing view is, however, that it lacks the ability to capture semantics of word sequences and is virtually useless for most purposes, unless combined with heavy machiner

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Using Distributed Word Representations and mRMR Discriminant Analysis for Multilingual Text Summarizof such systems. They use statistical features to score and extract most relevant sentences to form a summary within a size limit. In this paper, we investigate recently released multilingual distributed word representations combined with mRMR discriminant analysis to score terms then sentences. We

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Combining Pattern-Based and Distributional Similarity for Graph-Based Noun Categorizationand sparse while distributional methods have a higher recall. Given these particular properties we use the prediction of distributional methods as a back-off to pattern-based similarity. Since our pattern-based approach is embedded into a semi-supervised graph clustering algorithm, we also examine h

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Acquiring a Large Scale Polarity Lexicon Through Unsupervised Distributional Methodsexts with a good accuracy. Many approaches that can be found in literature are based on hand-coded resources that model the . polarity of words or multi-word expressions. The construction of such resources is in general expensive and coverage issues arise with respect to the multiplicity of linguist

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Dedication

What was the Query? Generating Queries for Document Sets with Applications in Cluster Labelingdocument set to a short query. But the task also has a real-world application: cluster labeling (e.g., for faceted search). Our solution to cluster labeling is the usage of queries that approximately retrieve a cluster’s documents. To be generalizable, our approach does not require access to a searc