(Pytorch) Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification

Dataset: Relation Extraction Challenge(SemEval-2010 Task #8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals)
Performance: This code repo approached 71% F1. Please feel free to fork and refine it and get the paper's reported 84%.
Model Structure: Attention-based BiLSTM

Quick Start

python data/re_semeval/reader.py
python preprocess.py

python train.py

python train.py -load_model 'tmp/model'

Given: a pair of nominals
Goal: recognize the semantic relation between these nominals.
Example:
- "There were apples, pears and oranges in the bowl."
  → CONTENT-CONTAINER(pears, bowl)
- “The cup contained tea from dried ginseng.”
  → ENTITY-ORIGIN(tea, ginseng)

Cause-Effect(CE): An event or object leads to an effect(those cancers were caused by radiation exposures)
Instrument-Agency(IA): An agent uses an instrument(phone operator)
Product-Producer(PP): A producer causes a product to exist (a factory manufactures suits)
Content-Container(CC): An object is physically stored in a delineated area of space (a bottle full of honey was weighed) Hendrickx, Kim, Kozareva, Nakov, O S´ eaghdha, Pad ´ o,´ Pennacchiotti, Romano, Szpakowicz Task Overview Data Creation Competition Results and Discussion The Inventory of Semantic Relations (III)
Entity-Origin(EO): An entity is coming or is derived from an origin, e.g., position or material (letters from foreign countries)
Entity-Destination(ED): An entity is moving towards a destination (the boy went to bed)
Component-Whole(CW): An object is a component of a larger whole (my apartment has a large kitchen)
Member-Collection(MC): A member forms a nonfunctional part of a collection (there are many trees in the forest)
Message-Topic(CT): An act of communication, written or spoken, is about a topic (the lecture was about semantics)
OTHER: If none of the above nine relations appears to be suitable.

SemEval-2010 Task #8 Dataset [Download]

Relation	Train Data	Test Data	Total Data
Cause-Effect	1,003 (12.54%)	328 (12.07%)	1331 (12.42%)
Instrument-Agency	504 (6.30%)	156 (5.74%)	660 (6.16%)
Product-Producer	717 (8.96%)	231 (8.50%)	948 (8.85%)
Content-Container	540 (6.75%)	192 (7.07%)	732 (6.83%)
Entity-Origin	716 (8.95%)	258 (9.50%)	974 (9.09%)
Entity-Destination	845 (10.56%)	292 (10.75%)	1137 (10.61%)
Component-Whole	941 (11.76%)	312 (11.48%)	1253 (11.69%)
Member-Collection	690 (8.63%)	233 (8.58%)	923 (8.61%)
Message-Topic	634 (7.92%)	261 (9.61%)	895 (8.35%)
Other	1,410 (17.63%)	454 (16.71%)	1864 (17.39%)
Total	8,000 (100.00%)	2,717 (100.00%)	10,717 (100.00%)