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CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring
Taxonomy is not only a fundamental form of knowledge representation, but...
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Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification
Due to the lack of structured knowledge applied in learning distributed ...
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Multi-Source Spatial Entity Linkage
Besides the traditional cartographic data sources, spatial information c...
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Classification of Questions and Learning Outcome Statements (LOS) Into Blooms Taxonomy (BT) By Similarity Measurements Towards Extracting Of Learning Outcome from Learning Mate
Blooms Taxonomy (BT) have been used to classify the objectives of learni...
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A Consolidated System for Robust Multi-Document Entity Risk Extraction and Taxonomy Augmentation
We introduce a hybrid human-automated system that provides scalable enti...
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CN-Probase: A Data-driven Approach for Large-scale Chinese Taxonomy Construction
Taxonomies play an important role in machine intelligence. However, most...
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Towards a Unified Taxonomy of Biclustering Methods
Being an unsupervised machine learning and data mining technique, biclus...
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Classification of entities via their descriptive sentences
Hypernym identification of open-domain entities is crucial for taxonomy construction as well as many higher-level applications. Current methods suffer from either low precision or low recall. To decrease the difficulty of this problem, we adopt a classification-based method. We pre-define a concept taxonomy and classify an entity to one of its leaf concept, based on the name and description information of the entity. A convolutional neural network classifier and a K-means clustering module are adopted for classification. We applied this system to 2.1 million Baidu Baike entities, and 1.1 million of them were successfully identified with a precision of 99.36
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