TextCube provides a critical information organization structure, enhancing text
exploration and analysis for various applications.
We focus on new TextCube construction methods that are scalable, weakly-supervised, domain-independent, language-agnostic, and effective (i.e., generating quality TextCubes from large corpora of various domains).
Module I. Mining Structural Primitives from Text: Phrases, Entities and Relations
AutoPhrase
AutoNER
ReMine [58] which extracts high-confidence relational phrases from domain-specific texts in an end-to-end manner.
Module II. Automated Construction of TextCubes
Taxonomy construction: Taxonomy construction clusters similar concepts and generates a hierarchy of “concept clusters” from massive corpus。 模型：TaxoGen [53], a recursive framework that leverages word distributional representations and constructs cluster-based taxonomy using adaptive spherical clustering and local embedding
Embedding learning: serve as the preliminary to document classification and TextCube construction. 模型：JoSE. , an unsupervised text embedding framework that jointly learns word embedding and paragraph embedding by incorporating both local and global contexts to capture more complete text semantics, and present TopicMine [24], a category-name guided word embedding framework that endows word embedding with discriminative power over the specific set of categories
Supervised methods: for text cube construction。 We present how to adapt the supervised methods for text cube construction along with their strength and drawbacks.
Weakly-supervised methods: WeSTClass [25] and WeSHClass [26], which generate pseudo training data for neural classifier pre-training, and then bootstrap the classifier by selftraining on unlabeled documents.
Module III. Multi-Dimensional Exploration of TextCubes
TextCube facilitates multidimensional text analysis
Summary:
提出了一个TextCube的数据结构框架。讲了为了做到自动化构建这个框架，用到了哪些技术。#275 的团队。
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TextCube provides a critical information organization structure, enhancing text exploration and analysis for various applications.
We focus on new TextCube construction methods that are scalable, weakly-supervised, domain-independent, language-agnostic, and effective (i.e., generating quality TextCubes from large corpora of various domains).
Module I. Mining Structural Primitives from Text: Phrases, Entities and Relations
Module II. Automated Construction of TextCubes
Taxonomy construction: Taxonomy construction clusters similar concepts and generates a hierarchy of “concept clusters” from massive corpus。 模型：TaxoGen [53], a recursive framework that leverages word distributional representations and constructs cluster-based taxonomy using adaptive spherical clustering and local embedding
Embedding learning: serve as the preliminary to document classification and TextCube construction. 模型：JoSE. , an unsupervised text embedding framework that jointly learns word embedding and paragraph embedding by incorporating both local and global contexts to capture more complete text semantics, and present TopicMine [24], a category-name guided word embedding framework that endows word embedding with discriminative power over the specific set of categories
Supervised methods: for text cube construction。 We present how to adapt the supervised methods for text cube construction along with their strength and drawbacks.
Weakly-supervised methods: WeSTClass [25] and WeSHClass [26], which generate pseudo training data for neural classifier pre-training, and then bootstrap the classifier by selftraining on unlabeled documents.
Module III. Multi-Dimensional Exploration of TextCubes
TextCube facilitates multidimensional text analysis
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