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CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring

The source code used for our KDD'2020 paper.

Requirements

Datasets

Due to the constraint of size, we provide the link of our datasets in the following links, please copy the files to ${dataset}/.

Run the Code

Preprocessing

If you are using our own datasets, you can skip this step. Otherwise, please first use this preprocessing tool to extract a sentences.json for your own corpus, and then run

bash preprocess.sh

to generate the index files.

Concept Learning for topic nodes

cd c
bash run_emb_part_tax.sh

This step compiles the source file and trains embedding for concept learning. The --topic_file in the script is used to specify the seed taxonomy. As an example, you can set ${topic_file} to be topics_field.txt for dataset dblp and topics_des.txt for dataset yelp. These topic files are already provided in the datasets. If you want to specify your own seed taxonomy, just feel free to create a new file using the format topics_{xxx}.txt. Each line starts with a parent node (with the root node being ROOT), and then followed by a tab. The children nodes of this parent is appended and separated by space. Generated embedding file is stored under ${dataset}.

Relation Transferring

A jupyter notebook version is available for this step. You can change the dataset and the topic_file name in main.ipynb. As another option, you can use the following python programme to generate the results.

cd ..
python main.py --dataset ${dataset} --topic_file ${topic_file}.txt

This step completes the taxonomy structure and outputs keywords for each node in the taxonomy. As an example, for DBLP dataset, you can run

python main.py --dataset dblp --topic_file topics_field.txt

Concept learning for all nodes

python generate_bash.py

This command generates a script from c/template.sh that can recursively run embedding training for all topics and subtopics.

cd c
bash run_emb_full_tax.sh

This command will generate the final topical taxonomy under a result directory.

Results

Results for each topics are generated at result\${dataset}\${topic}\subtopics_for_${topic}.txt. E.g., each line in result\DBLP\data_mining\subtopics_for_data_mining.txt is one subtopic of data mining (including a cluster of words).

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