dictionary-annotator

Dictionary Annotator is inspired by DKPro's dictionary-annotator and UIMA Ruta's MARKTABLE action

Features

• Annotates JCas with phrases from CSV file (supported by DKPro and MARKTABLE)
• Supports multiple annotations with different features on the same block of text (not supported by DKPro nor MARKTABLE)
• Configurable case sensitivity (supported by MARKTABLE)
• Supports unlimited number of annotation features (supported by MARKTABLE)
• Configurable tokenizer (not supported by DKPro nor MARKTABLE)

Performance

Simple performance benchmark was done to compare with other alternatives. Numbers are averages from 3 trials. 20 Newsgroups texts were used.

	Tokenization	Time (Tokenization+Dictionary)	Tokens/sec
DkPro dictionary-annotator	OpenNlp Simple Tokenizer	368.2 sec	8 724
Ruta MARKTABLE	OpenNlp Simple Tokenizer for dictionary, Ruta tokenizer for texts	21.9 sec	146 684
This dictionary annotator	OpenNlp Simple Tokenizer	1.7 sec	1 889 637

However this benchmark might be inaccurate because of following differences between annotators:

• DkPro requires text to be segmented into senteces an tokens. While testing text was marked as single sentence
• Ruta has its own rich tokenizer which takes significant amount of time

Benchmarking can be done by running ./benchmark.sh

Usage

Maven dependency

<dependency>
    <groupId>lt.tokenmill.uima</groupId>
    <artifactId>dictionary-annotator</artifactId>
    <version>0.1.1</version>
</dependency>

Dictionary (leaders.csv)

Barack Obama,US,2009-01-20,2017-01-20,president,100023
Dalia Grybauskaite,Lithuania,2009-06-12,,president,100049
Dalia Grybauskaite,EU,2004-11-22,2009-06-01,commissioner,100050

Configuration

AnalysisEngineDescription description = AnalysisEngineFactory.createEngineDescription(DictionaryAnnotator.class,
        DictionaryAnnotator.PARAM_DICTIONARY_LOCATION, "classpath:leaders.csv",
        DictionaryAnnotator.PARAM_ANNOTATION_TYPE, Person.class.getName(),
        DictionaryAnnotator.PARAM_DICTIONARY_CASE_SENSITIVE, true,
        DictionaryAnnotator.PARAM_FEATURE_MAPPING, asList(
                "1 -> country", "2 -> from", "3 -> to", "5 -> id", "4 -> role"));

Running it on text Barack Obama met Dalia Grybauskaite in Vilnius would produce 3 annotations:

Person(id=100023, from="2009-01-20", to="2017-01-20", country="US", role="president"),
Person(id=100049, from="2009-06-12", to=null, country="Lithuania", role="president"),
Person(id=100050, from="2004-11-22", to="2009-06-01", country="EU", role="commissioner")

A working example can be found in DictionaryAnnotatorTest

Configuration

Basic Example

AnalysisEngineDescription description = AnalysisEngineFactory.createEngineDescription(DictionaryAnnotator.class,
        DictionaryAnnotator.PARAM_DICTIONARY_LOCATION, "classpath:dictionary.csv",
        DictionaryAnnotator.PARAM_ANNOTATION_TYPE, DictionaryEntry.class.getName(),
        DictionaryAnnotator.PARAM_DICTIONARY_CASE_SENSITIVE, false,
        DictionaryAnnotator.PARAM_FEATURE_MAPPING, asList(
                "1 -> feature1", "2 -> feature2"));

Tokenizer

By default whitespace tokenizer is used for dictionary entries tokenization. But you can provide a custom one (usually you want your text and dictionary tokenized by the same tokenizer)

AnalysisEngineDescription description = AnalysisEngineFactory.createEngineDescription(DictionaryAnnotator.class,
        DictionaryAnnotator.PARAM_DICTIONARY_LOCATION, "classpath:dictionary.csv",
        DictionaryAnnotator.PARAM_TOKENIZER_CLASS, YourDictionaryTokenizer.class.getName(),
        DictionaryAnnotator.PARAM_ANNOTATION_TYPE, DictionaryEntry.class.getName(),
        DictionaryAnnotator.PARAM_DICTIONARY_CASE_SENSITIVE, false,
        DictionaryAnnotator.PARAM_FEATURE_MAPPING, asList(
                "1 -> feature1", "2 -> feature2"));

NOTE: Tokenizer must implement lt.tokenmill.uima.dictionaryannotator.DictionaryTokenizer

Accent-insensitive matching

Dictionary annotator can match text ignoring letter accents. To enable this feature set following configuration property to false:

DictionaryAnnotator.PARAM_DICTIONARY_ACCENT_SENSITIVE

Known issues

If some line in a long CSV doesn't have a closing quote character then the CSV reader might strugle to finish its job. If you know that one line corresponds to exactly one dictionary entry then check if there are lines that have exactly one quote character and fix those lines. One possible solution is to get rid of the problematic linee altogether, e.g. the quote character is " and e.g. with sed delete those lines in the same file:

sed -i -e '/^[^\"]*\"[^\"]*$/d' input-file.csv

TODO

• Phrase matching using stemmed tokens
• Configurable CSV separator
• Configurable ignored characters (as in MARKTABLE)

License

Copyright © 2019 TokenMill UAB.

Distributed under the The Apache License, Version 2.0.