---
title: "Inferring Sentence-internal Temporal Relations — Lapata 2004"
description: "Much temporal information in text is implicit. A reader can tell which of two events came first even without an explicit cue. Lapata and Lascarides ask…"
authors: ["Mirella Lapata", "Alex Lascarides"]
year: 2004
venue: "Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004"
source: "https://aclanthology.org/N04-1020/"
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/papers/lapata-2004-inferring-sentence-internal-temporal-relations/"
---
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# Inferring Sentence-internal Temporal Relations

**Authors:** [Mirella Lapata](https://md.research.elamur.ai/free/authors/mirella-lapata/), [Alex Lascarides](https://md.research.elamur.ai/free/authors/alex-lascarides/) · **Year:** 2004 · **Venue:** Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004 · **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/N04-1020/) · [PDF](https://aclanthology.org/N04-1020.pdf) · [OpenAlex](https://openalex.org/W42350778)

**Citations:** 62 citations (openalex-snapshot · 2026-10-14) · [21](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Much temporal information in text is implicit. A reader can tell which of two events came first even without an explicit cue. Lapata and Lascarides ask whether such ordering knowledge can be learned without hand-annotated temporal data. They use sentences in which a main clause and a subordinate clause are linked by an overt temporal conjunction, harvested from the machine-parsed BLLIP corpus. The marker then serves as a free label. A naive-Bayes-style probabilistic model learns, from features of both clauses, which marker connects them. Features include a "temporal signature" covering finiteness, modality, aspect, voice and polarity, along with lexical and syntactic cues.

The model is tested on two tasks:
- **Interpretation:** recovering the deleted marker.
- **Sentence fusion:** deciding which clause the marker attaches to, and so how the clauses are ordered.

The best configuration reaches 70.7% on interpretation and 97.4% on fusion. An ensemble of models agrees with the gold standard about as well as human judges do. The work shows that unannotated parsed corpora can support temporal inference and text generation for summarisation and question answering.

## Key points
- The data comes from BLLIP, a 30M-word Treebank-style parse of WSJ text (1987–89) produced with Charniak's (2000) parser. Clause pairs were extracted via SBAR-TMP nodes and their dominating S nodes.
- Eight temporal conjunctions make up the confusion set: since, until, once, as, when, while, before and after. Markers rarer than 10 per million were excluded. Clause features are assumed conditionally independent given the marker, and add-k smoothing handles zero counts.
- Human studies were run online: 198 native English speakers did the interpretation task and 100 did the fusion task.
- On interpretation, ensemble–gold Kappa was .413, compared with .421 for humans against gold. On fusion, the ensemble scored .489 against .522 for humans.
- Humans struggled most with *as* and *once* clauses, matching gold for only 33% and 35% of items respectively. Both lexical and syntactic features proved useful.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

21 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 16 not yet.

- [Acquiring the Meaning of Discourse Markers](https://md.research.elamur.ai/free/papers/hutchinson-2004-acquiring-meaning-discourse-markers/)
- [What Causes the Failure of Explicit to Implicit Discourse Relation Recognition?](https://md.research.elamur.ai/free/papers/liu-2024-what-causes-failure-explicit-implicit/)
- [Annotation upon Annotation: Adding Signalling Information to a Corpus of Discourse Relations](https://md.research.elamur.ai/free/papers/taboada-2013-annotation-upon-annotation-adding-signalling/)
- [The Effects of Discourse Connectives Prediction on Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/zhou-2010-effects-discourse-connectives-prediction-implicit/)
- [Predicting Discourse Connectives for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/papers/zhou-2010-predicting-discourse-connectives-implicit-discourse/)

## Cite

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

```bibtex
@inproceedings{lapata2004inferring,
  title = {{Inferring Sentence-internal Temporal Relations}},
  author = {Lapata, Mirella and Lascarides, Alex},
  booktitle = {Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004},
  year = {2004},
  url = {https://aclanthology.org/N04-1020/}
}
```

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