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A phrase mining framework for recursive construction of a topical hierarchy
This paper proposes an algorithm for recursively constructing a hierarchy of topics from a collection of content-representative documents, characterized each topic in the hierarchy by an integrated ranked list of mixed-length phrases.
Automatic Construction and Ranking of Topical Keyphrases on Collections of Short Documents
A framework for topical keyphrase generation and ranking, based on the output of a topic model run on a collection of short documents, is introduced, able to directly compare and rank phrases of different lengths.
Constructing topical hierarchies in heterogeneous information networks
- Chi Wang, Jialu Liu, Nihit Desai, Marina Danilevsky, Jiawei Han
- Computer ScienceIEEE 13th International Conference on Data Mining
- 1 December 2013
This work presents an algorithm for recursively constructing multi-typed topical hierarchies by a newly designed clustering and ranking algorithm for heterogeneous network data, as well as mining and ranking topical patterns of different types.
Plead or Pitch ? The Role of Language in Kickstarter Project Success
An analysis of over 26000 projects from Kickstarter shows that successful project pitches are, on average, more emotive, thoughtful and colloquial than unsuccessful projects.
KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles
A topical keyphrase ranking function is constructed which implements the four criteria that represent high quality topical keyphrases (coverage, purity, phraseness, and completeness).
EventCube: multi-dimensional search and mining of structured and text data
This proposed EventCube demo will show the power of the system not only on the originally designed ASRS (Aviation Safety Report System) data sets, but also on news datasets collected from multiple news agencies, and academic datasets constructed from the DBLP and web data.
AMETHYST: a system for mining and exploring topical hierarchies of heterogeneous data
AMETHYST is a system for exploring and analyzing a topical hierarchy constructed from a heterogeneous information network (HIN), which reflects a domain-specific ontology, interacts with multiple types of linked entities, and can be tailored for both free text and OLAP queries.
Entity Role Discovery in Hierarchical Topical Communities
A new problem of mining entity roles in hierarchical topical communities is studied, which is able to discover topical roles of dierent types of entities in both large communities encompassing more general topics, and small, focused subcommunities.
Deep Reinforcement Learning to play Space Invaders
In this project, algorithms that use reinforcement learning to play the game space invaders are explored and an extension of Q-learning known as Double Q learning is looked at, to explore optimal architectures for learning.
AMETHYST: A System for Mining and Exploring Topical Hierarchies in Information Networks