Feedforward NeuralNet for Relation Extraction.
This repository puts together recent models and data sets for sentence-level relation extraction using knowledge bases (i.e., distant supervision). In particular, it contains the source code for WWW'17 paper CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases. Task: Given a text corpus with entity mentions detected and heuristically labeled using distant supervision, the task aims to identify relation types/labels between a pair of entity mentions based on the sentence context where they co-occur.
Performs entity detection, distant supervision, candidate generation, and produces JSON files for typing systems (PLE, AFET, CoType)
Given a text corpus with entity mentions detected and heuristically labeled by distant supervision, this code performs (1) label noise reduction over distant supervision, and (2) learning type classifiers over de-noised training data. For example, check out PLE's output on Tech news.
Given a text corpus (e.g., a collection of news articles), it performs automatically entity extraction and typing using distant supervision (i.e., examples from external knowledge bases like Freebase). For example, from the sentence "The best BBQ I’ve tasted in Phoenix" the system will recognize BBQ as food and phoenix as location. More background can be found in our WWW'16 tutorial.
Given a text corpus with entity mentions detected and heuristically labeled by distant supervision, this code performs training of a rank-based loss over distant supervision and predict the fine-grained entity types for each test entity mention. For example, check out AFET's output on WSJ news articles. An end-to-end tool (corpus to typed entities) is under development.