AmbiverseNLU: A Natural Language Understanding suite by Max Planck Institute for Informatics
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README.md

Try the demo at http://ambiversenlu.mpi-inf.mpg.de

Ambiverse Natural Language Understanding - AmbiverseNLU

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The multilingual Ambiverse Natural Language Understanding suite (AmbiverseNLU) combines a number of state-of-the-art components for language understanding tasks: named entity recognition and disambiguation (or entity linking), open information extraction, entity salience estimation, and concept linking, providing a basis for text-to-knowledge applications.

Take the example sentence below:

Jack founded Alibaba with investments from SoftBank and Goldman.

The AmbiverseNLU will produce the following outputs:

AmbiverseNLU Demo

AmbiverseNLU Demo

Quickly play with AmbiverseNLU without installing anything: demo at http://ambiversenlu.mpi-inf.mpg.de

Quick Start

Call the Web Service using Docker

Starting the AmbiverseNLU as web service (with PostgreSQL backend) using Docker is simple, using docker-compose:

docker-compose -f docker-compose/service-postgres.yml up

If your machine has less than 32 GB of main memory, run this configuration instead, which knows way fewer entities (some big companies and related entities )but is good enough to play around:

docker-compose -f docker-compose/service-postgres-small.yml up

Wait for some time (depending on your internet connection and CPU speed it can easily take more than an hour), then call the service:

curl --request POST \
  --url http://localhost:8080/factextraction/analyze \
  --header 'accept: application/json' \
  --header 'content-type: application/json' \
  --data '{"docId": "doc1", "text": "Jack founded Alibaba with investments from SoftBank and Goldman.", "extractConcepts": "true" }'

You can run AmbiverseNLU with different databases as backend, or also start the database backend alone. Check out the different configurations of the Docker files on https://github.com/ambiverse-nlu/dockerfiles for details.

Alternative Ways to Run

Start the Database Backend

Start the PostgreSQL backend with the fully multilingual knowledge graph:

docker run -d --name nlu-db-postgres \
  -p 5432:5432 \
  -e POSTGRES_DB=aida_20180120_cs_de_en_es_ru_zh_v18_db \
  -e POSTGRES_USER=ambiversenlu \
  -e POSTGRES_PASSWORD=ambiversenlu \
  ambiverse/nlu-db-postgres

If you have less than 32 GB of main memory, you can also start a PostgreSQL backend with a smaller knowledge graph, containing only a few companies and related entities, supporting only English and German:

docker run -d --name nlu-db-postgres \
  -p 5432:5432 \
  -e POSTGRES_DB=aida_20180120_b3_de_en_v18_db \
  -e POSTGRES_USER=ambiversenlu \
  -e POSTGRES_PASSWORD=ambiversenlu \
  ambiverse/nlu-db-postgres

Make sure to use aida_20180120_b3_de_en_v18_db as value for the AIDA_CONF exports below.

Start the Web Service using Maven and Jetty from Source Code

  1. Adapt the database configuration. You need to adapt the database_aida.properties of the AIDA_CONF you are using. For example, if you are using aida_20180120_cs_de_en_es_ru_zh_v18_db as configuration, adapt src/main/config/aida_20180120_cs_de_en_es_ru_zh_v18_db/database_aida.properties and make sure that the property dataSource.serverName points to the host of the machine (or linked docker image) that runs the database.
  2. Start the web service by executing the following script:
export AIDA_CONF=aida_20180120_cs_de_en_es_ru_zh_v18_db
./scripts/start_webservice.sh

You can the MAVEN_OPTS in the script if you want to change the port and the available memory. If you adapt AIDA_CONF, make sure that PostgreSQL backend started above uses the same configuration value. The database_aida.properties configuration must point to an existing database.

Run a Pipeline from the Command Line

Adapt the database configuration as explained in the section above (Starting the Web Service).

The main command line interface is de.mpg.mpi_inf.ambiversenlu.nlu.entitylinking.run.UimaCommandLineProcessor. Example call using a script:

export AIDA_CONF=aida_20180120_cs_de_en_es_ru_zh_v18_db
mkdir nlu-input
echo "Jack founded Alibaba with investments from SoftBank and Goldman." > nlu-input/doc.txt
./scripts/driver/run_pipeline.sh -d nlu-input -i TEXT -l en -pip ENTITY_SALIENCE

A list of existing pipelines can be found in de.mpg.mpi_inf.ambiversenlu.nlu.entitylinking.uima.pipelines.PipelineType, where you can also define new pipelines.

Database dumps

The database dumps can be downloaded from http://ambiversenlu-download.mpi-inf.mpg.de/. The database docker images will download them automatically.

Natural Language Understanding Components

KnowNER: Named Entity Recognition

Named Entity Recognition (NER) identifies mentions of named entities (persons, organizations, locations, songs, products, ...) in text.

KnowNER works on English, Czech, German, Spanish, and Russian texts.

AmbiverseNLU provides KnowNER [1] for NER.

AIDA: Named Entity Disambiguation

Named Entity Disambiguation (NED) links mentions recognized by NER (see above) to a unique identifier. Most names are ambiguous, especially family names, and entity disambiguation resolves this ambiguity. Together with NER, NED is often referred to as entity linking.

AIDA works on English, Chinese, Czech, German, Spanish, and Russian texts.

AmbiverseNLU provides an enhanced version of AIDA [2] for NED, mapping mentions to entities registered in the Wikipedia-derived YAGO [4,5] knowledge base.

ClausIE: Open Information Extraction

Open Information Extraction (OpenIE) is the task of generating a structured output from natural language text in the form of n-ary propositions, consisting of a subject, a relation, and one or more arguments. For example, in the sentence "Albert Einstein was born in Ulm", an open information extraction system will generate the extraction ("Albert Einstein", "was born in", "Ulm"), where the first argument is usually referred as the subject, the second as the relation, and the last one as the object or argument.

ClausIE works on English texts.

AmbiverseNLU provides an enhanced version of ClausIE [3] for OpenIE.

Concept Linking

Concept linking is similar to entity linking but with a focus on non-named entities (e.g., car, chair, etc.). It identifies relevant concepts in text and links them to a to concepts registered in the Wikipedia-derived YAGO [4,5] knowledge base.

Concept Linking works on English, Chinese, Czech, German, Spanish, and Russian texts.

AmbiverseNLU provides a new concept linking component based on the original AIDA entity disambiguation with knowledge-informed spotting.

Entity Salience

Entity Salience gives each entity in a document a score in [0,1], denoting its importance with respect to the document.

Our Entity Salience is fully multilingual.

Resource Considerations

Main Memory

The Entity/Concept Linking component has the largest main memory requirements. This is due to the large contextual and coherence models it needs to load in order to disambiguate with high accuracy.

Initially, Entity Linking loads static data in main memory, which requires (depending on the languages you are configuring it for), a couple of GB. We estimate 8 GB for all languages to be the upper bound.

The actual requirements per document vary depending on the density of mentions and the number of entites per mention, so it cannot be estimated just by the length of the document. To be on the safe side, plan 8 GB of main memory per document.

This means that if you want to disambiguate one document at a time, you need at least 16 GB of main memory. If you want to disambiguate 4 documents in parallel, you should be using 40 GB.

Throughput Analysis

Benchmarking setup: (multi-threaded) Entity Linking Service in a single Docker-container using 4 cores and 32 GB of main memory. Cassandra node running on the same physical machine.

For 1,000 news articles (2,531 chars on average, 26 named entities on average), with highest-quality setting (coherence):

  • Average time per article: 2.36 seconds
  • Throughput: 1.7 documents per second

Evaluation

The Entity Disambiguation accuracy on the widely used CoNLL-YAGO dataset [2] is as follows:

  • Micro-Accuracy: 84.61%
  • Macro-Accuracy: 82.67%

Advanced configuration

Configuring the environment

Most settings are bundled by folder in 'src/main/config'. Set the configuration you need using the AIDA_CONF environment variable, e.g.:

export AIDA_CONF=aida_20180120_cs_de_en_es_ru_zh_v18_db

AmbiverseNLU Pipeline

AmbiverseNLU has a flexible, based on UIMA and DKPro, which allows you to specify the components you want to run. A number of useful pipelines are preconfigured, new ones can be added easily.

Using pipelines programaticaly

In the web service:

Have a look at de.mpg.mpi_inf.ambiversenlu.nlu.entitylinking.service.web.resource.impl.AnalyzeResourceImpl.java which configures the web service.

As a stand alone application

Have a look at de.mpg.mpi_inf.ambiversenlu.nlu.drivers.test.Disambiguation and de.mpg.mpi_inf.ambiversenlu.nlu.drivers.test.OpenIE for examples.

Creating new pipelines

Pipelines are enums in de.mpg.mpi_inf.ambiversenlu.nlu.entitylinking.uima.pipelines.PipelineType. Each pipeline contains the order in which the components should be executed. The components are located in de.mpg.mpi_inf.ambiversenlu.nlu.entitylinking.uima.components.Component.

Building your own YAGO Knowledge Graph

AmbiverseNLU uses the YAGO knowledge base by default.

Building steps:

  1. Create the YAGO KG and AIDA repositories using scripts/repository_creation/createAidaRepository.py
  2. Build updated KnowNER models (optional), again using scripts/repository_creation/createAidaRepository.py passing --reuse-yago --stages KNOWNER_PREPARE_RESOURCES,KNOWNER_TRAIN_MODEL as additional parameters

Building a custom Knowledge Graph

The AmbiverseNLU architecture is knowledge base agnostic, allowing your to import your own concepts and entities, or combine them with YAGO. Have a look at de.mpg.mpi_inf.ambiversenlu.nlu.entitylinking.datapreparation.PrepareData and de.mpg.mpi_inf.ambiversenlu.nlu.entitylinking.datapreparation.conf.GenericPrepConf to get started.

Extending KnowNER

KnowNER provides means to add new languages. Have a look at docs/know-ner/new_corpus.md and docs/know-ner/new_language.md.

Further Information

Stay in Touch

Sign up for the AmbiverseNLU mailing list: Visit https://lists.mpi-inf.mpg.de/listinfo/ambiversenlu or send a mail to ambiversenlu-subscribe@lists.mpi-inf.mpg.de

AmbiverseNLU License

Apache License, Version 2.0

Maintainers and Contributors

Current Maintainers (in alphabetical order):

  • Dragan Milchevski
  • Ghazale Haratinezhad Torbati
  • Johannes Hoffart
  • Luciano Del Corro

Contributors (in alphabetical order):

  • Artem Boldyrev
  • Daniel Bär
  • Dat Ba Nguyen
  • Diego Ceccarelli
  • Dominic Seyler
  • Dragan Milchevski
  • Felix Keller
  • Ghazale Haratinezhad Torbati
  • Ilaria Bordino
  • Johannes Hoffart
  • Luciano Del Corro
  • Mohamed Amir Yosef
  • Tatiana Dembelova
  • Vasanth Venkatraman

References

  • [1] D. Seyler, T. Dembelova, L. Del Corro, J. Hoffart, and G. Weikum, “A Study of the Importance of External Knowledge in the Named Entity Recognition Task,” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 2018
  • [2] J. Hoffart, M. A. Yosef, I. Bordino, H. Fürstenau, M. Pinkal, M. Spaniol, B. Taneva, S. Thater, and G. Weikum, “Robust Disambiguation of Named Entities in Text,” Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, Edinburgh, Scotland, 2011
  • [3] L. Del Corro and R. Gemulla, “ClausIE - clause-based open information extraction.,” Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, Rio de Janerio, Brazil, 2013
  • [4] T. Rebele, F. M. Suchanek, J. Hoffart, J. Biega, E. Kuzey, and G. Weikum, “YAGO - A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames.,” Proceedings of the 15th International Semantic Web Conference, ISWC 2016, Kobe, Japan, 2016
  • [5] J. Hoffart, F. M. Suchanek, K. Berberich, and G. Weikum, “YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia,” Artificial Intelligence, vol. 194, pp. 28–61, 2013