Please cite the following two papers if you are using our tools. Thanks!
-
Jingbo Shang, Jialu Liu, Meng Jiang, Xiang Ren, Clare R Voss, Jiawei Han, "Automated Phrase Mining from Massive Text Corpora", accepted by IEEE Transactions on Knowledge and Data Engineering, Feb. 2018.
-
Jialu Liu*, Jingbo Shang*, Chi Wang, Xiang Ren and Jiawei Han, "Mining Quality Phrases from Massive Text Corporaโ, Proc. of 2015 ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'15), Melbourne, Australia, May 2015. (* equally contributed, slides)
- Updates docker image with the git master.
- Fix a few bugs during the pre-processing and post-processing, i.e.,
Tokeninzer.java
. Previously, when the corpus contains characters like/
, the results could be wrong or errors may occur. - When the phrasal segmentation is serving new text, for the phrases (every token is seen in the traning corpus) provided in the knowledge base (
wiki_quality.txt
), the score is set as1.0
. Previously, it was kind of infinite.
- Support extremely large corpus (e.g., 100GB or more). Please comment out the
// define LARGE
in the beginning ofsrc/utils/parameters.h
before you run AutoPhrase on such a large corpus. - Quality phrases (every token is seen in the raw corpus) provided in the knowledge base will be incorporated during the phrasal segmentation, even their frequencies are smaller than
MIN_SUP
. - Stopwords will be treated as low quality single-word phrases.
- Model files are saved separately. Please check the variable
MODEL
in bothauto_phrase.sh
andphrasal_segmentation.sh
. - The end of line is also a separator for sentence splitting.
(compared to SegPhrase)
- Minimized Human Effort. We develop a robust positive-only distant training method to estimate the phrase quality by leveraging exsiting general knowledge bases.
- Support Multiple Languages: English, Spanish, and Chinese. The language in the input will be automatically detected.
- High Accuracy. We propose a POS-guided phrasal segmentation model incorporating POS tags when POS tagger is available. Meanwhile, the new framework is able to extract single-word quality phrases.
- High Efficiency. A better indexing and an almost lock-free parallelization are implemented, which lead to both running time speedup and memory saving.
Linux or MacOS with g++ and Java installed.
Ubuntu:
- g++ 4.8
$ sudo apt-get install g++-4.8
- Java 8
$ sudo apt-get install openjdk-8-jdk
- curl
$ sudo apt-get install curl
MacOS:
- g++ 6
$ brew install gcc6
- Java 8
$ brew update; brew tap caskroom/cask; brew install Caskroom/cask/java
$ ./auto_phrase.sh
The default run will download an English corpus from the server of our data
mining group and run AutoPhrase to get 3 ranked lists of phrases as well as 2 segmentation model files under the
MODEL
(i.e., models/DBLP
) directory.
AutoPhrase.txt
: the unified ranked list for both single-word phrases and multi-word phrases.AutoPhrase_multi-words.txt
: the sub-ranked list for multi-word phrases only.AutoPhrase_single-word.txt
: the sub-ranked list for single-word phrases only.segmentation.model
: AutoPhrase's segmentation model (saved for later use).token_mapping.txt
: the token mapping file for the tokenizer (saved for later use).
You can change RAW_TRAIN
to your own corpus and you may also want change MODEL
to a different name.
We also provide an auxiliary function to highlight the phrases in context based on our phrasal segmentation model. There are two thresholds you can tune in the top of the script. The model can also handle unknown tokens (i.e., tokens which are not occurred in the phrase mining step's corpus).
In the beginning, you need to specify AutoPhrase's segmentation model, i.e., MODEL
. The default value is set to be consistent with auto_phrase.sh
.
$ ./phrasal_segmentation.sh
The segmentation results will be put under the MODEL
directory as well (i.e., model/DBLP/segmentation.txt
). The highlighted phrases will be enclosed by the phrase tags (e.g., <phrase>data mining</phrase>
).
If domain-specific knowledge bases are available, such as MeSH terms, there are two ways to incorporate them.
- (recommended) Append your known quality phrases to the file
data/EN/wiki_quality.txt
. - Replace the file
data/EN/wiki_quality.txt
by your known quality phrases.
In fact, our tokenizer supports many different languages, including Arabics (AR), German (DE), English (EN), Spanish (ES), French (FR), Italian (IT), Japanese (JA), Portuguese (PT), Russian (RU), and Chinese (CN). If the language detection is wrong, you can also manually specify the language by modify the TOKENIZER
command in the bash script auto_phrase.sh
using the two-letter code for that language. For example, the following one forces the language to be English.
TOKENIZER="-cp .:tools/tokenizer/lib/*:tools/tokenizer/resources/:tools/tokenizer/build/ Tokenizer -l EN"
We also provide a default tokenizer together with a dummy POS tagger in the tools/tokenizer
.
It uses the StandardTokenizer in Lucene, and always assign a tag UNKNOWN
to each token.
To enable this feature, please add the -l OTHER"
to the TOKENIZER
command in the bash script auto_phrase.sh
.
TOKENIZER="-cp .:tools/tokenizer/lib/*:tools/tokenizer/resources/:tools/tokenizer/build/ Tokenizer -l OTHER"
If you want to incorporate your own tokenizer and/or POS tagger, please create a new class extending SpecialTagger in the tools/tokenizer
. You may refer to StandardTagger as an example.
You may try to search online or create your own list.
Meanwhile, you have to add two lists of quality phrases in the data/OTHER/wiki_quality.txt
and data/OTHER/wiki_all.txt
.
The quality of phrases in wiki_quality should be very confident, while wiki_all, as its superset, could be a little noisy. For more details, please refer to the tools/wiki_enities.
sudo docker run -v $PWD/models:/autophrase/models -it \
-e ENABLE_POS_TAGGING=1 \
-e MIN_SUP=30 -e THREAD=10 \
remenberl/autophrase
./auto_phrase.sh
The results will be available in the models
folder. Note that all of the environment variables above have their default values--leaving the assigments out here would produce exactly the same results. (However, in this case, using default values, the results of phrasal_segmentation.txt
would be saved to the
internal default_models
directory--this is unavoidable, since the phrasal segmentation app reads from and writes to the same model directory.)
Assuming the path to input file is ./data/input.txt.
sudo docker run -v $PWD/data:/autophrase/data -v $PWD/models:/autophrase/models -it \
-e RAW_TRAIN=data/input.txt \
-e ENABLE_POS_TAGGING=1 \
-e MIN_SUP=30 -e THREAD=10 \
-e MODEL=models/MyModel \
-e TEXT_TO_SEG=data/input.txt \
remenberl/autophrase
./auto_phrase.sh
"RAW_TRAIN" is the training corpus, and "TEXT_TO_SEG" is a corpus whose phrases are to be highlighted--typically, this is the same corpus, but training and phrasal segmentation use two different scripts. When the user wants to segment a new corpus with an existing model, only the latter script need be used (and setting "RAW_TRAIN" isn't necessary).
Note that, in a Docker deployment, the (default) data
and models
directories are renamed to default_data
and default_models
, respectively, to avoid conflicts with
mounted external directories with the same names. It should be noted as well that there's litle point in saving a model to the default models directory, since all new files are erased when
the container is exited (and if an external directory is mounted as "models", and no value is specified for "MODEL", the results will be saved in the "models/DBP" subdirectory). The same
wrinkle also means that there's little point to running a container with the "FIRST_RUN" variable set to 0.
Because the original data directory will have been been renamed, it's perfectly fine for the user to mount an external directory called "data" and read the corpus from there--and in most cases, there's no need for a user to change the supplied files stored in the default data directory. If such a change is necessary, though, the environment variable that specifies the directory in question is "DATA_DIR".
The sudo
command won't work in a Windows bash shell, and in any case isn't needed in an elevated window--replace it with winpty
.
In addition, the PWD
variable works a little oddly in MinGW (the Git bash shell), appending ";C" to the end of the path. To prevent this, replace $PWD/models:/autophrase/models
with "/${PWD}/models":/autophrase/models
, and $PWD/data/autophrase/data
with "/${PWD}/data:/autophrase/data
.