This project uses a graph propagation method with pretrained word vectors (GloVe & Word2vec) to do the concept extraction and concept expansion tasks. It supports both Chinese and English.

• Concept extraction: Extract concepts from the input text with given seed words.
• Concept expansion: Expand concepts by the snippets from search engines (Baidu for Chinese and Google for English) with given seed words.

1. Decompress model.zip to get some basic model files:unzip model.zip
2. Decompress the particular HunPos package of hunpos-1.0-linux.tgz, hunpos-1.0-macosx.tgz, hunpos-1.0-win.zip according to the operating system, and rename the folder to hunpos.
3. Download the following model files, put them in folder model/ and unzip them:
   • Candidate concept lists: kp_list.zip
   • English word vector: glove.6B.zip You will find more information at GloVe
   • Chinese word vector: sgns.baidubaike.zip You will find more information at Chinese-Word-Vectors
4. modify some path lists in config.py if necessary.
5. Install the requirements in requirements.txt: pip install -r requirements.txt

You can run this project simply bypython main.py with additional command line parameters. Parameters can be specified in command line or in config.py.

Here are the parameters available in command line:

input_text: --text
input_seed: --seed, -s
language: --language, -l
task: --task, -t
iter_time: --iter_time, -i
max_num: --max_num, -m
threshold: --threshold, -th
decay: --decay, -d
no_seed: --no_seed, -ns

The following path lists can be modified in config.py:

zh_model, en_model: the word vectors files
jieba_dict
en_stopwords, zh_stopwords
zh_kp_list, en_kp_list: the whole candidate concepts
snippets_zh, snippets_en: the sqlite3 db files to store snippets
hunpos_model, hunpos_bin: nltk hunpos tagger
input, seed, tmp, result

To crawl Google search snippets, you need VPN (for users in Mainland China).

The crawler may be blocked by anti-crawler programs if you see get snippet timeoutinfomation too many times. Rerun the code will solve this problem most of the time.

You can use other word vectors by modify path lists in config.py or rewrite modules/model_load.py. It's normal taking a long time and consuming a lot of memory to load the word vector file.

Make sure there is at least one seed word in the input text.