/alto-boot

ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling

Primary LanguageJupyter Notebook

Introduction

This file walks through the process for using ALTO (http://aclweb.org/anthology/P/P16/P16-1110.pdf) for label induction and document labeling. The code also allows you to use the other three conditions in the paper.

Check out

git clone https://github.com/Foroughp/ALTO-ACL-2016.git

Requirements

Java 1.8

Docker

Docker Compose

Lombok Plugin (Intellij or Eclipse)

Compiling and Running

Let BASEDIR be the directory of ALTO code.

Please try the synthetic data first:

Set constants:

Copy local configuration file template src/main/resources/config/application-local.properties.template to src/main/resources/config/application-local.properties and set corpus_name, base_dir, source_text_dir and num_topics to correct local values.

Compile:

  • ./gradlew assemble

Run server:

You can choose to run the application with your own JRE, or build using the dockerfile provided you have a native docker installation.

####Using spring boot directly:

  • Start the server: ./gradlew bootRun

-or-

Using docker:

  • build the docker image: docker build -t alto-boot -f docker/Dockerfile .

  • run the docker image: docker-compose -f docker/docker-compose.yml up

  • Open your browser: http://localhost:8080/alto-boot

  • Start using ALTO (see next step)

Stopping ALTO:

  • ctrl-c or kill will work if launching with bootRun

  • if using docker docker kill alto-boot

    ​

Try ALTO:

check the ALTO paper in ACL 2016 for details about this interface ​

  • Type user name, choose a condition, and click "Start".
  • You can click the documents to see the actual content.
  • If in TA or TR conditions, check the topics: the top words and top related documents for each topic are displayed. You can click the documents to see the actual content.
  • You can assign a label to the document either from the existing labels or creating a new one.
  • Once you have at least two labels, the system will suggest a document to label by scrolling to that document and drawing a red box around it.
  • The logs are available in the results directory

Trying a new dataset:

Here are some steps for trying a new dataset with ALTO: Assume your data name is CORPUS. Put the data in the $BASEDIR/text_data/$CORPUS folder. Each document needs to be in a separate file.

  • mkdir $BASEDIR/WebContent/results/$CORPUS
  • mkdir $BASEDIR/WebContent/results/$CORPUS/input

Generate the files in the data folder:

  • $CORPUS.html: Follow the synthetic.html format. Keep the headers the same, and follow the same way to format your documents. Note that you don't have to make all your documents in one html file, you can split them to several small ones, as long as they are consistent with the url defined in $CORPUS.url.
  • $CORPUS.titles: Follow the synthetic.titles format. The content will be used for display in the interface. For synthetic data, the titles are the same as actual content. However, for a real-world data, titles will be shorter than content.

Generate the files in the input folder:

  • The mallet input data $CORPUS-topic-input.mallet: ./bin/mallet import-dir --input $BASEDIR/text_data/$CORPUS --output $BASEDIR/WebContent/results/$CORPUS/input/$CORPUS-topic-input.mallet --keep-sequence (Please refer to mallet website for more details.)

  • $CORPUS.url: Follow the synthetic.url format. Notice this is related with the *.html in $BASEDIR/WebContent/data/. The url basically defines the path to look for the document in the synthetic.html file.

  • $CORPUS.gold: the gold labels associated with documents. Follow the format in synthetic.gold. Note that this file is optional. If you don't have gold label standards for your dataset, purity will be calculated as -1.

Generate the files in the output folder:

  • The model.topics and model.docs files: ./bin/mallet train-topics --input $BASEDIR/WebContent/results/$CORPUS/input/$CORPUS-topic-input.mallet --num-topics $NUMTOPICS --topic-word-weights-file $BASEDIR/WebContent/results/$CORPUS/output/model.topics --output-doc-topics $BASEDIR/WebContent/results/$CORPUS/output/model.docs (Please refer to mallet website for more details.)

Setup new data in the interface:

  • Change the CORPUS_NAME and TEXT_DATA_DIR variables in $BASEDIR/WebContent/src/util/Constants.java.
  • Change the topic model setting in $BASEDIR/src/util/Constants.java.
  • If need be, change the counting down time: Look for "var start_itm = 40;" in WebContent/ui.html and change the number "15" to the time you want.