This code is an implementation of a simple frame identification approach (SimpleFrameId) described in the paper "Out-of-domain FrameNet Semantic Role Labeling". Please use the following citation:

@inproceedings{TUD-CS-2017-0011,
	title = {Out-of-domain FrameNet Semantic Role Labeling},
	author = {Hartmann, Silvana and Kuznetsov, Ilia and Martin, Teresa and Gurevych, Iryna},
	publisher = {Association for Computational Linguistics},
	booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017)},
	pages = {to appear},
	month = apr,
	year = {2017},
	location = {Valencia, Spain},
}

Abstract: Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain.

Contact persons: Teresa Martin, martin@aiphes.tu-darmstadt.de; Ilia Kuznetsov, kuznetsov@ukp.informatik.tu-darmstadt.de

https://www.ukp.tu-darmstadt.de/

https://www.tu-darmstadt.de/

Don't hesitate to send us an e-mail or report an issue, if something is broken (and it shouldn't be) or if you have further questions.

This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.

The implementation is a single package. Two most important modules are:

• main.py -- the entry point for experiments
• globals.py -- global variables used in experiments
• classifier.py -- the classifiers
• representation.py -- representation builders

The system requires a specific folder structure where the data is stored:

• ROOT -- your project root (just a folder somewhere on your disk)
• ROOT/srl_data -- source data
• ROOT/srl_data/corpora -- input corpora
• ROOT/srl_data/embeddings -- external VSMs
• ROOT/srl_data/lexicons -- external lexicons
• ROOT/out -- here the experiment results are stored

• Python 2.7
• Python dependencies: keras, lightfm, sklearn, numpy, networkx

Install the dependencies, adjust the paths in main.py and globals.py accordingly and run via python main.py

• to define in globals.py: filenames for
  • pretrained embeddings e.g, Levy dependency embeddings
  • FrameNet lexicon
  • train data
  • test data
• to define in main.py
  • vsms -- vector space model to use
  • lexicons -- lexicon to use (mind the all_unknown setting!)
  • multiword_averaging -- treatment of multiword predicates, false - use head embedding, true - use avg
  • all_unknown -- makes the lexicon treat all LU as unknown, corresponds to the no-lex setting
  • num_components -- for wsabie classifier: dimension for the learned latent representations
  • max_sampled -- for wsabie classifier: maximum number of negative samples used during WARP fitting 'warp'
  • num_epochs -- for wsabie classifier: number of epochs to train the model