auto_phrase_stanford.sh

#!/bin/bash


MODEL=${MODEL:- "models/DBLP"}
# RAW_TRAIN is the input of AutoPhrase, where each line is a single document.
RAW_TRAIN=${RAW_TRAIN:- $1}
# When FIRST_RUN is set to 1, AutoPhrase will run all preprocessing. 
# Otherwise, AutoPhrase directly starts from the current preprocessed data in the tmp/ folder.
FIRST_RUN=${FIRST_RUN:- 1}
# When ENABLE_POS_TAGGING is set to 1, AutoPhrase will utilize the POS tagging in the phrase mining. 
# Otherwise, a simple length penalty mode as the same as SegPhrase will be used.
ENABLE_POS_TAGGING=${ENABLE_POS_TAGGING:- 1}
# A hard threshold of raw frequency is specified for frequent phrase mining, which will generate a candidate set.
MIN_SUP=${MIN_SUP:- 10}
# You can also specify how many threads can be used for AutoPhrase
THREAD=${THREAD:- 10}

### Begin: Suggested Parameters ###
MAX_POSITIVES=-1
LABEL_METHOD=DPDN
RAW_LABEL_FILE=${RAW_LABEL_FILE:-""}
### End: Suggested Parameters ###

green=`tput setaf 2`
reset=`tput sgr0`

echo ${green}===Compilation===${reset}

COMPILE=${COMPILE:- 1}
if [ $COMPILE -eq 1 ]; then
    bash compile.sh
fi

mkdir -p tmp
mkdir -p ${MODEL}

if [ $RAW_TRAIN == "data/DBLP.txt" ] && [ ! -e data/DBLP.txt ]; then
    echo ${green}===Downloading Toy Dataset===${reset}
    curl http://dmserv2.cs.illinois.edu/data/DBLP.txt.gz --output data/DBLP.txt.gz
    gzip -d data/DBLP.txt.gz -f
    iconv -f UTF8 -t ASCII//TRANSLIT data/DBLP.txt > data/DBLP_ascii.txt
    rm data/DBLP.txt
    mv data/DBLP_ascii.txt data/DBLP.txt
fi

### END Compilation###

echo ${green}===Tokenization===${reset}

TOKENIZER="-cp .:tools/tokenizer/lib/*:tools/tokenizer/resources/:tools/tokenizer/build/ Tokenizer"
TOKENIZED_TRAIN=tmp/tokenized_train.txt
CASE=tmp/case_tokenized_train.txt
TOKEN_MAPPING=tmp/token_mapping.txt

# hardcode English language
rm -f tmp/language.txt
echo "EN" > tmp/language.txt




LANGUAGE=`cat tmp/language.txt`
echo -ne "Detected Language: $LANGUAGE\033[0K\n"
TOKENIZED_STOPWORDS=tmp/tokenized_stopwords.txt
TOKENIZED_ALL=tmp/tokenized_all.txt
TOKENIZED_QUALITY=tmp/tokenized_quality.txt
STOPWORDS=data/$LANGUAGE/stopwords.txt
ALL_WIKI_ENTITIES=data/$LANGUAGE/wiki_all.txt
QUALITY_WIKI_ENTITIES=data/$LANGUAGE/wiki_quality.txt
LABEL_FILE=tmp/labels.txt





# New tokenization & POS tagging

source tokenize_raw.sh $1










### END Tokenization ###

if [[ $RAW_LABEL_FILE = *[!\ ]* ]]; then
	echo -ne "Current step: Tokenizing expert labels...\033[0K\n"
	java $TOKENIZER -m test -i $RAW_LABEL_FILE -o $LABEL_FILE -t $TOKEN_MAPPING -c N -thread $THREAD
else
	echo -ne "No provided expert labels.\033[0K\n"
fi



### END Part-Of-Speech Tagging ###

echo ${green}===AutoPhrasing===${reset}

if [ $ENABLE_POS_TAGGING -eq 1 ]; then
    time ./bin/segphrase_train \
        --pos_tag \
        --thread $THREAD \
        --pos_prune data/BAD_POS_TAGS.txt \
        --label_method $LABEL_METHOD \
		--label $LABEL_FILE \
        --max_positives $MAX_POSITIVES \
        --min_sup $MIN_SUP
else
    time ./bin/segphrase_train \
        --thread $THREAD \
        --label_method $LABEL_METHOD \
		--label $LABEL_FILE \
        --max_positives $MAX_POSITIVES \
        --min_sup $MIN_SUP
fi

echo ${green}===Saving Model and Results===${reset}

cp tmp/segmentation.model ${MODEL}/segmentation.model
cp tmp/token_mapping.txt ${MODEL}/token_mapping.txt
cp tmp/language.txt ${MODEL}/language.txt

### END AutoPhrasing ###

echo ${green}===Generating Output===${reset}
java $TOKENIZER -m translate -i tmp/final_quality_multi-words.txt -o ${MODEL}/AutoPhrase_multi-words.txt -t $TOKEN_MAPPING -c N -thread $THREAD
java $TOKENIZER -m translate -i tmp/final_quality_unigrams.txt -o ${MODEL}/AutoPhrase_single-word.txt -t $TOKEN_MAPPING -c N -thread $THREAD
java $TOKENIZER -m translate -i tmp/final_quality_salient.txt -o ${MODEL}/AutoPhrase.txt -t $TOKEN_MAPPING -c N -thread $THREAD

# java $TOKENIZER -m translate -i tmp/distant_training_only_salient.txt -o results/DistantTraning.txt -t $TOKEN_MAPPING -c N -thread $THREAD

### END Generating Output for Checking Quality ###