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Found 9 papers, 3 papers with code
A new perspective of paramodulation complexity by solving massive 8 puzzles
By doing this, we can distinguish the complexity of 8 puzzles with the number of generated with paramodulation.
A Curious New Result of Resolution Strategies in Negation-Limited Inverters Problem
Generally, negation-limited inverters problem is known as a puzzle of constructing an inverter with AND gates and OR gates and a few inverters.
A constrained recursion algorithm for batch normalization of tree-sturctured LSTM
In proposal method, we impose the constraint on the recursion algorithm for the depth-first search of binary tree representation of LSTM for which batch normalization is applied.
Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia
The embeddings of entities in a large knowledge base (e. g., Wikipedia) are highly beneficial for solving various natural language tasks that involve real world knowledge.
Representation Learning of Entities and Documents from Knowledge Base Descriptions
In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB).
Ranked #1 on
Entity Typing
on Freebase FIGER
Studio Ousia's Quiz Bowl Question Answering System
In this chapter, we describe our question answering system, which was the winning system at the Human-Computer Question Answering (HCQA) Competition at the Thirty-first Annual Conference on Neural Information Processing Systems (NIPS).
Learning Distributed Representations of Texts and Entities from Knowledge Base
Given a text in the KB, we train our proposed model to predict entities that are relevant to the text.
Ranked #2 on
Entity Disambiguation
on TAC2010
Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words.
Ranked #4 on
Entity Disambiguation
on TAC2010
