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). Given a document in a KB consisting of words and entity annotations, we train our model to predict the entity that the document describes and map the document and its target entity close to each other in a continuous vector space... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Text Classification 20NEWS TextEnt-full Accuracy 84.5 # 10
F-measure 83.9 # 3
Entity Typing Freebase FIGER TextEnt-full Accuracy 37.4 # 1
BEP 94.8 # 1
Macro F1 84.2 # 1
Micro F1 85.7 # 1
P@1 93.2 # 1
Text Classification R8 TextEnt-full Accuracy 96.7 # 8
F-measure 91 # 2

Methods used in the Paper


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