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

Pytorch implementation of ACL 2016 paper, Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification (Zhou et al., 2016)

• 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

Dataloading

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

Train

python train.py

• 20190909 run: 71.2% on semeval

Evaluate

python train.py -load_model 'tmp/model'

Dataset: SemEval-2010 Task #8

• 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)

The Inventory of Semantic Relations

1. Cause-Effect(CE): An event or object leads to an effect(those cancers were caused by radiation exposures)
2. Instrument-Agency(IA): An agent uses an instrument(phone operator)
3. Product-Producer(PP): A producer causes a product to exist (a factory manufactures suits)
4. 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)
5. Entity-Origin(EO): An entity is coming or is derived from an origin, e.g., position or material (letters from foreign countries)
6. Entity-Destination(ED): An entity is moving towards a destination (the boy went to bed)
7. Component-Whole(CW): An object is a component of a larger whole (my apartment has a large kitchen)
8. Member-Collection(MC): A member forms a nonfunctional part of a collection (there are many trees in the forest)
9. Message-Topic(CT): An act of communication, written or spoken, is about a topic (the lecture was about semantics)
10. OTHER: If none of the above nine relations appears to be suitable.

Distribution for Dataset

• 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%)

Reference

• Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification (ACL 2016), P Zhou et al.
• SeoSangwoo's Tensorflow implementation