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CATENA

CAusal and TEmporal relation extraction from NAtural language texts

CATENA is a sieve-based system to perform temporal and causal relation extraction and classification from English texts, exploiting the interaction between the temporal and the causal model. The system requires pre-annotated text with EVENT and TIMEX3 tags according to the TimeML annotation standard, as these annotation are used as features to extract the relations.

Requirements

Maven

CATENA is now available on Maven Central. Please add the following dependency in your pom.xml.

<dependency>
  <groupId>com.github.paramitamirza</groupId>
  <artifactId>CATENA</artifactId>
  <version>1.0.3</version>
</dependency>

To build the fat (executable) JAR:

Text processing tools:

Other libraries:

Other resources:

Usage

! The input file(s) must be in the TimeML annotation format or CoNLL column format (one token per line) !

usage: Catena
 -i,--input <arg>        Input TimeML file/directory path
 -f,--col                (optional) Input files are in column format (.col)
 -tl,--tlinks <arg>      (optional) Input file containing list of gold temporal links
 -cl,--clinks <arg>      (optional) Input file containing list of gold causal links
 -gl,--gold              (optional) Gold candidate pairs to be classified are given
 -y,--clinktype          (optional) Output the type of CLINK (ENABLE, PREVENT, etc.) from the rule-based sieve
        
 -x,--textpro <arg>      TextPro directory path
 -l,--matelemma <arg>    Mate tools' lemmatizer model path   
 -g,--matetagger <arg>   Mate tools' PoS tagger model path
 -p,--mateparser <arg>   Mate tools' parser model path      
 
 -t,--ettemporal <arg>   CATENA model path for E-T temporal classifier    
 -d,--edtemporal <arg>   CATENA model path for E-D temporal classifier                       
 -e,--eetemporal <arg>   CATENA model path for E-E temporal classifier
 -c,--eecausal <arg>     CATENA model path for E-E causal classifier
 
 -b,--train              (optional) Train the models
 -m,--tempcorpus <arg>   (optional) Directory path (containing .tml or .col files) for training temporal classifiers
 -u,--causcorpus <arg>   (optional) Directory path (containing .tml or .col files) for training causal classifier

For example

java -Xmx2G -jar ./target/CATENA-1.0.2.jar -i ./data/example_COL/ --col --tlinks ./data/TempEval3.TLINK.txt --clinks ./data/Causal-TimeBank.CLINK.txt -l ./models/CoNLL2009-ST-English-ALL.anna-3.3.lemmatizer.model -g ./models/CoNLL2009-ST-English-ALL.anna-3.3.postagger.model -p ./models/CoNLL2009-ST-English-ALL.anna-3.3.parser.model -x ./tools/TextPro2.0/ -d ./models/catena-event-dct.model -t ./models/catena-event-timex.model -e ./models/catena-event-event.model -c ./models/catena-causal-event-event.model -b -m ./data/Catena-train_COL/ -u ./data/Causal-TimeBank_COL/

CoNLL column format

The input document must be in tab-separated 'one-token-per-line' format, with each column as: | token | token-id | sentence-id | lemma | event-id | event-class | event-tense+aspect+polarity | timex-id | timex-type | timex-value | signal-id | causal-signal-id | pos-tag | chunk | lemma | pos-tag | dependencies | main-verb |

See for example data/example_COL/.

Output format

The output will be a list of temporal and/or causal relations, one relation per line, in the format of:

filename  entity_1  entity_2  TLINK_type/CLINK/CLINK-R

System architecture

alt tag

CATENA contains two main modules:

  1. Temporal module, a combination of rule-based and supervised classifiers, with a temporal reasoner module in between.
  2. Causal module, a combination of a rule-based classifier according to causal verbs, and supervised classifier taken into account syntactic and context features, especially causal signals appearing in the text.

The two modules interact, based on the assumption that the notion of causality is tightly connected with the temporal dimension: (i) TLINK labels for event-event pairs, resulting from the rule-based sieve + temporal reasoner, are used for the CLINK classifier, and (ii) CLINK labels are used as a post-editing method for correcting the wrongly labelled event pairs by the Temporal module.

Publication

Paramita Mirza and Sara Tonelli. 2016. CATENA: CAusal and TEmporal relation extraction from NAtural language texts. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, December. [pdf]

Dataset

! Whenever making reference to this resource please cite the paper in the Publication section. !

Web Service

Soon!

Contact

For more information please contact Paramita Mirza (paramita135@gmail.com).