Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision

UPD 30'th december 2020: distance between terms feature has been commented

UPD 17'th december 2020: distance between terms parameter (thetta, which equals 10) may discard some pairs from further consideration which affects on results. [issue]

Figure: Application of Distant Supervision in model training process results in a weight distribution biasing: frames in between subject and object of attitude got more weight values; the latter reflects the pattern of frame-based approach, utilized in RuAttitudes collection development.

This repository provides a source code of models and related experiments, conducted as a part of the following paper:

• Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision [ACM-DOI] / [presentation]
  • Rusnachenko Nicolay, Loukachevitch Natalia
  • WIMS-2020

Dependencies

• Python-2.7
• arekit-0.20.3 [WIMS-2020 edition]

Installation

• Core library installation: All the implementation depends on core library for sentiment attitude extraction, dubbed as arekit-0.20.3:

NOTE: it is important to download in arekit directory.

# Download arekit-0.20.3
git clone --single-branch --branch 0.20.3-wims-rc https://github.com/nicolay-r/AREkit arekit

# Install dependencies
pip install -r arekit/dependencies.txt

• Resources: Since arekit-0.20.0 all the resources such as collections (RuAttitudes, RuSentRel) and lexicons (RuSentiLex) are a part of the related library.

• Word2Vec: installation assumes to run a download.sh script:

cd data && ./download.sh

Usage

1. Select the appropriate Exteperiment format (TwoScale or ThreeScale) by uncommenting one of the line at io_utils.py:

# You may manually select three-scale mode by commenting one of these two lines below. 
# self.__neutral_annotator = self.__init_two_scale_neutral_annotator() # <- 2-scale
# self.__neutral_annotator = self.__init_three_scale_neutral_annotator() # <- 3-scale

2. Proceed with the run_*.sh scripts in the following directories:
   • Supervised Learning, Fixed Train/Test separation
   • Supervised Learning, CV-based separation
   • SL+DS, Fixed Train/Test separation
   • SL+DS, CV-based separation

References

@inproceedings{rusnachenko-2020-attention-ds,
    title = "Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision",
    author = "Rusnachenko, Nicolay  and Loukachevitch, Natalia",
    booktitle = "The 10th International Conference on Web Intelligence, Mining and Semantics (WIMS 2020), 
      June 30-July 3, 2020, Biarritz, France",
    year = "2020",
    url = "https://doi.org/10.1145/3405962.3405985",
    doi = "10.1145/3405962.3405985",
}