This code is the implementation of Text Emotion Recognition (TER) with linguistic features

Related tags

Text Data & NLP python deep-learning pytorch text-emotion-recognition
Overview

APSIPA-TER

This code is the implementation of Text Emotion Recognition (TER) with linguistic features. The network model is BERT with a pretrained model parameter.

How to use

  1. Edit txt2tsv.py and preprocess your files
python3 txt2tsv.py
  1. Edit hyper_param.yaml
  2. Run main.py
python3 main.py

Reference

[1] GitHub_cl-tohoku/bert-japanese, https://github.com/cl-tohoku/bert-japanese (Last View: 2022-02-07)

[2] Huggingface_bert-japanese, https://huggingface.co/docs/transformers/model_doc/bert-japanese (Last View: 2022-02-07)

[3] Qiita-自然言語処理モデル（BERT）を利用した日本語の文章分類, https://qiita.com/takubb/items/fd972f0ac3dba909c293#bertforsequenceclassification (Last View: 2022-02-07)

Paper

Ryotaro Nagase, Takahiro Fukumori and Yoichi Yamashita: ``Speech Emotion Recognition with Fusion of Acoustic- and Linguistic-Feature-Based Decisions, '' Proc. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 725 -- 730, 2021.

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Comments
  • Splitting text-open for cross validation

    Splitting text-open for cross validation

    Hi @kenro515,

    As it was said in your paper, you performed 5-fold cross-validation for TER with text-open criteria. Could you point me to the part of this text-open splitting in this repository since I cannot find it? For SER, I can find the split for leave-one-group-out speaker-open in this link, should it be similar to this (changing speaker list to sentence list)?

    https://github.com/kenro515/APSIPA-SER/blob/main/dataset.py

    opened by bagustris 1
Owner
kenro515
Graduate Student / Research Interest: Speech Emotion Recognition, Deep Learning
kenro515
GitHub
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