SegPhrase

Please check out our new tool AutoPhrase, which is significantly more efficient and can support multiple languages.

Publication

• Jialu Liu*, Jingbo Shang*, Chi Wang, Xiang Ren and Jiawei Han, "Mining Quality Phrases from Massive Text Corpora”, Proc. of 2015 ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'15), Melbourne, Australia, May 2015. (* equally contributed, slides)

Notes

The current results support quality unigram mining, which is not covered in the original paper. We plan to improve this part in the future updates.

Automatic labeling is another addon feature based on Wikipedia entities. We suggest you to provide your own labels in order to achieve the best performance.

Requirements

We will take Ubuntu for example.

• g++ 4.8 $ sudo apt-get install g++-4.8
• python 2.7 $ sudo apt-get install python
• scikit-learn $ sudo apt-get install pip $ sudo pip install sklearn
• nltk (required only when WORDNET_NOUN=1) $ sudo pip install nltk

Build

SegPhrase can be easily built by Makefile in the terminal. $ make

Default Run

$ ./train_toy.sh  #train a toy segmenter and output phrase list as results/unified.csv
$ ./train_dblp.sh  #train a segmenter and output phrase list for DBLP data
$ ./parse.sh  #use the segmenter to parse new documents

Parameters - training

RAW_TEXT=data/DBLP.5K.txt

RAW_TEXT is the input of SegPhrase, where each line is a single document.

AUTO_LABEL=1
DATA_LABEL=data/wiki.label.auto

When AUTO_LABEL is set to 1, SegPhrase will automatically generate labels and save it to DATA_LABEL. Otherwise, it will load labels from DATA_LABEL.

WORDNET_NOUN=1

when WORDNET_NOUN is set to 1, SegPhrase will resort to wordnet synsets to keep only noun candidates as the last step of training. This requires you to install nltk in python.

KNOWLEDGE_BASE=data/wiki_labels_quality.txt
KNOWLEDGE_BASE_LARGE=data/wiki_labels_all.txt

We have two knowledge bases, the smaller one contains high quality phrases for positive labels while the larger one is used to exclude medium quality phrases for negative labels.

SUPPORT_THRESHOLD=10

A hard threshold of raw frequency is specified for frequent phrase mining, which will generate a candidate set.

OMP_NUM_THREADS=4

You can also specify how many threads can be used for SegPhrase

DISCARD_RATIO=0.00

The discard ratio (between 0 and 1) controls how many positive labels can be broken. It is typically small, for example, 0.00, 0.05, or 0.10. It should be EXACTLY 2 digits after decimal point.

MAX_ITERATION=5

This is the number of iterations of Viterbi training.

ALPHA=0.85

Alpha is used in the label propagation from phrases to unigrams.

Parameters - parse.sh

./bin/segphrase_parser results/segmentation.model results/salient.csv 50000 ./data/test.txt ./results/parsed.txt 0

The first parameter is the segmentation model, which we saved in training process. The second parameter is the high quality phrases ranking list (together with unigrams). The third one determines the ratio of top ranked phrases (unigrams) will be considered in this run of segmentation. This parameter should be dataset and application specific. The later two are the input and the output of corpus. The last one is a debug flag and you can just leave it as 0.