Pun Detection and Location

“The Boating Store Had Its Best Sail Ever”: Pronunciation-attentive Contextualized Pun Recognition
Yichao Zhou, Jyun-yu Jiang, Jieyu Zhao, Kai-Wei Chang and Wei Wang
Computer Science Department, University of California, Los Angeles

Requirements

python3
pip3 install -r requirements.txt

Training for Pun Location

python cv_run_ner.py 
--data_dir data/semeval2017/data_with_pronunciation/pl-homo/ 
--bert_model bert-base-cased 
--task_name ner 
--output_dir out 
--max_seq_length 128 
--do_train 
--num_train_epochs 3 
--do_eval 
--pron_emb_size 16 
--do_pron

这里简述每个文件的作用：

run.sh 是用于批量跑本代码的脚本，用于比较系统性能等。
word2Sense.txt 是用于寻找出现在训练集中的双关词有多少个含义如: shot 8 就代表shot 这个单词，在训练集中有8个意思涉及到了双关意义
tools 文件夹下是工具文件

./tools/getSense.py 用于生成 /home/lawson/program/punLocation/data/pun_word_definition.txt 的文件
./tools/generateSenseEmb.py 是用于生成defi_emb_xxx.txt 文件

defi_emb_xxx.txt 是用于存储各个单词在 wordnet 中的不同含义
pun_word_definition.txt 是用于将每个双关词的各种含义使用bert处理得到的embedding
subtask3_v2 是用pun+gloss 对的方式解决pun interpretation 的问题

Pun Detection and Location

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Overview

Pun Detection and Location

Requirements

Training for Pun Location

Owner

lawson

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