---
title: "Automatic Detection of Causal Relations for Question Answering"
description: "The author also proposes a taxonomy of cause-effect questions by ambiguity: explicit, ambiguous… 12 references, cited by 78 papers here."
authors: ["Roxana Girju"]
year: 2003
venue: "Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering"
source: "https://aclanthology.org/W03-1210/"
doi: "10.3115/1119312.1119322"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/girju-2003-automatic-detection-causal-relations-question/"
---
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# Automatic Detection of Causal Relations for Question Answering

**Authors:** [Roxana Girju](https://md.research.elamur.ai/free/en/authors/roxana-girju/) · **Year:** 2003 · **Venue:** Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/W03-1210/) · [PDF](https://aclanthology.org/W03-1210.pdf) · [DOI 10.3115/1119312.1119322](https://doi.org/10.3115/1119312.1119322) · [OpenAlex](https://openalex.org/W2142321231)

**Citations:** 372 citations (OpenAlex, 2026-10-14) · [78](https://md.research.elamur.ai/free/en/papers/girju-2003-automatic-detection-causal-relations-question/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Girju addresses the automatic extraction of explicit causal relations within a sentence. Earlier systems relied on hand-written, domain-bound patterns. Here the focus is on noun–verb–noun constructions (NP1 verb NP2) where the verb is a simple causative that is often ambiguous, such as *produce* or *generate*.

The method has two stages:
1. **Pattern discovery.** Causally linked noun pairs are harvested from WordNet (its CAUSE-TO relation and noun glosses). Sentences containing those pairs are then searched to collect 60 candidate causal verbs and verbal expressions.
2. **Constraint learning.** Matched instances from the LA TIMES section of TREC 9 are parsed with the Charniak parser and labelled by hand as causal or not. A C4.5 decision tree then learns lexical and semantic constraints on the verb and its noun arguments.

The author also proposes a taxonomy of cause-effect questions by ambiguity: explicit, ambiguous and implicit. Adding the causation module to an existing QA system raised precision on causal questions from 36% to 61%. This shows that relation-level semantics can help answer a question type that QA systems of the time handled poorly.

## Key points
- WordNet 1.7 glosses yielded 429 noun–noun causal links. About 58.28% of them come from the medical domain.
- Pattern discovery produced 60 ambiguous causal verbs and expressions (Table 1), for example *trigger*, *lead to*, *stem from* and *give rise to*.
- For each verb, 120 sentences were taken from LA TIMES / TREC 9. The resulting split was a 6,000-sentence training corpus and a 1,200-sentence test corpus.
- The training data held 6,523 pattern instances, of which 2,101 were causal and 4,422 were not. The classifier used 19 lexical and semantic features with C4.5.
- On 50 causation questions (explicit and ambiguous) run on the Harabagiu et al. QA system, precision rose from 36% to 61% with the causal module.

## Interesting ideas
- **Lexical-resource seeding.** Seed pairs come from a lexical resource's definitions, by pairing a headword with a noun in its own gloss. Mining these for a relation gives cheap, high-confidence seeds for Hearst-style pattern bootstrapping, and would work for any relation that glosses describe.
- **Ambiguity is resolved separately from pattern finding.** Generic patterns are harvested first, and a classifier then decides when an instance truly expresses the relation. This lets recall-oriented discovery coexist with precision-oriented filtering instead of demanding unambiguous hand-written patterns.
- **Argument normalisation.** Each noun phrase is trimmed to the longest span that exists as a concept in the lexicon, such as "earthquake" from "a 7.1 magnitude earthquake". This maps noisy NPs onto ontology nodes so that semantic-class features can be computed.
- **Ontology-driven feature design.** Following Kim's philosophical argument, the method assumes only certain entity types can be causes or effects: events, states, processes, phenomena and the like. Using such typed constraints on arguments is a principled way to choose features for other semantic relations.
- **Measuring only the component's contribution.** The QA evaluation counted only answers delivered through the causal pattern, even though other correct answers existed. This isolates exactly what the new component adds, a useful design for ablation-style evaluations of pipeline modules.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

Also on the byline: [Baylor University](https://ror.org/005781934).

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🧭 Topics (1): [❓ Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/) · 242 works

## Cite

Cite as: Roxana Girju. 2003. Automatic Detection of Causal Relations for Question Answering. Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{girju2003automatic,
  title = {{Automatic Detection of Causal Relations for Question Answering}},
  author = {Roxana Girju},
  booktitle = {Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering},
  year = {2003},
  doi = {10.3115/1119312.1119322},
  url = {https://doi.org/10.3115/1119312.1119322}
}
```

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## References

12 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. D. Blaheta and E. Charniak, Assigning Function Tags to Parsed Text. In Proceedings of the 1st Annual Meeting of the North American Chapter of the Association for Computational Linguistics, Seattle, May 2000, pp. 234–240. [find](https://scholar.google.com/scholar?q=D%2E+Blaheta+and+E%2E+Charniak%2C+Assigning+Function+Tags+to+Parsed+Text%2E+In+Proceedings+of+the+1st+Annual+Meeting+of+the+North+American+Chapter+of+the+Association+for+Computational+Linguistics%2C+Seattle%2C+Ma)
2. E. Charniak, [A maximum-entropy-inspired parser](https://md.research.elamur.ai/free/en/papers/charniak-2000-maximum-entropy-inspired-parser/). In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL 2000), Seattle, WA.
3. B. Comrie. Causative constructions In Language Universals and Linguistic Typology, University of Chicago Press, Chicago, 1981. [find](https://scholar.google.com/scholar?q=B%2E+Comrie%2E+Causative+constructions+In+Language+Universals+and+Linguistic+Typology%2C+University+of+Chicago+Press%2C+Chicago%2C+1981%2E)
4. S. Harabagiu, D. Moldovan, M. Pasca, M. Surdeanu, R. Mihalcea, R. Girju, V. Rus, F. Lacatusu, P. Moraescu, and R. Bunescu. 2001. Answering Complex, List and Context Questions with LCC’s Question-Answering Server. In Proceedings of the TExt Retrieval Conference for Question Answering (TREC 10). [find](https://scholar.google.com/scholar?q=S%2E+Harabagiu%2C+D%2E+Moldovan%2C+M%2E+Pasca%2C+M%2E+Surdeanu%2C+R%2E+Mihalcea%2C+R%2E+Girju%2C+V%2E+Rus%2C+F%2E+Lacatusu%2C+P%2E+Moraescu%2C+and+R%2E+Bunescu%2E+2001%2E+Answering+Complex%2C+List+and+Context+Questions+with+LCC%E2%80%99s+Question%2DAnswe)
5. M. Hearst. Automated Discovery of WordNet Relations. In WordNet: An Electronic Lexical Database and Some of its Applications, editor Fellbaum, C., MIT Press, 1998. [find](https://scholar.google.com/scholar?q=M%2E+Hearst%2E+Automated+Discovery+of+WordNet+Relations%2E+In+WordNet%3A+An+Electronic+Lexical+Database+and+Some+of+its+Applications%2C+editor+Fellbaum%2C+C%2E%2C+MIT+Press%2C+1998%2E)
6. D. Garcia. COATIS, an NLP system to locate expressions of actions connected by causality links. In Knowledge Acquisition, Modeling and Mangement, The Tenth European Workshop, 1997. [find](https://scholar.google.com/scholar?q=D%2E+Garcia%2E+COATIS%2C+an+NLP+system+to+locate+expressions+of+actions+connected+by+causality+links%2E+In+Knowledge+Acquisition%2C+Modeling+and+Mangement%2C+The+Tenth+European+Workshop%2C+1997%2E)
7. D. Gildea and D. Jurafsky. [Automatic Labeling of Semantic Roles](https://md.research.elamur.ai/free/en/papers/gildea-2002-automatic-labeling-semantic-roles/). In Proceedings of the 38th Annual Conference of the Association for Computational Linguistics (ACL-00), pages 512-520, Hong Kong, October 2000.
8. R. Girju. Text Mining for Semantic Relations. Ph.D. Dissertation, University of Texas at Dallas, May 2002. [find](https://scholar.google.com/scholar?q=R%2E+Girju%2E+Text+Mining+for+Semantic+Relations%2E+Ph%2ED%2E+Dissertation%2C+University+of+Texas+at+Dallas%2C+May+2002%2E)
9. L. Joskowiscz, T. Ksiezyk and R. Grishman. Deep domain models for discourse anaysis. In The Annual AI Systems in Government Conference. R.M. Kaplan, and G. Berry-Rogghe. Knowledge-based acquisition of causal relationships in text. In Knowledge Acquisition, 3(3), 1991. [find](https://scholar.google.com/scholar?q=L%2E+Joskowiscz%2C+T%2E+Ksiezyk+and+R%2E+Grishman%2E+Deep+domain+models+for+discourse+anaysis%2E+In+The+Annual+AI+Systems+in+Government+Conference%2E+R%2EM%2E+Kaplan%2C+and+G%2E+Berry%2DRogghe%2E+Knowledge%2Dbased+acquisition+of)
10. C. Khoo, S. Chan and Y. Niu. Extracting Causal Knowledge from a Medical Database Using Graphical Patterns In Proceedings of ACL, Hong Kong, 2000. [find](https://scholar.google.com/scholar?q=C%2E+Khoo%2C+S%2E+Chan+and+Y%2E+Niu%2E+Extracting+Causal+Knowledge+from+a+Medical+Database+Using+Graphical+Patterns+In+Proceedings+of+ACL%2C+Hong+Kong%2C+2000%2E)
11. J. Kim. Causes and Events: Mackie on Causation. In Causation, ed. Ernest Sosa, and Michael Tooley, Oxford University Press, 1993. [find](https://scholar.google.com/scholar?q=J%2E+Kim%2E+Causes+and+Events%3A+Mackie+on+Causation%2E+In+Causation%2C+ed%2E+Ernest+Sosa%2C+and+Michael+Tooley%2C+Oxford+University+Press%2C+1993%2E)
12. V.P. Nedjalkov and G. Silnickij. The topology of causative constructions. In Folia Linguistica (6). J.R. Quinlan. [C4.5: Programs for Machine Learning](https://md.research.elamur.ai/free/en/papers/quinlan-1993-c45-programs-machine-learning/). Morgan Kaufmann.

*Updated 24 October 2026*