Browse State-of-the-Art › Temporal Information Extraction
Temporal Information Extraction
20 papers with code · 2 benchmarks · 3 datasets archive 2025-07-28
Temporal information extraction is the identification of chunks/tokens corresponding to temporal intervals, and the extraction and determination of the temporal relations between those. The entities extracted may be temporal expressions (timexes), eventualities (events), or auxiliary signals that support the interpretation of an entity or relation. Relations may be temporal links (tlinks), describing the order of events and times, or subordinate links (slinks) describing modality and other subordinative activity, or aspectual links (alinks) around the various influences aspectuality has on event structure.
The markup scheme used for temporal information extraction is well-described in the ISO-TimeML standard, and also on www.timeml.org.
<?xml version="1.0" ?>
<TimeML xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="http://timeml.org/timeMLdocs/TimeML_1.2.1.xsd">
<TEXT>
PRI20001020.2000.0127
NEWS STORY
<TIMEX3 tid="t0" type="TIME" value="2000-10-20T20:02:07.85">10/20/2000 20:02:07.85</TIMEX3>
The Navy has changed its account of the attack on the USS Cole in Yemen.
Officials <TIMEX3 tid="t1" type="DATE" value="PRESENT_REF" temporalFunction="true" anchorTimeID="t0">now</TIMEX3> say the ship was hit <TIMEX3 tid="t2" type="DURATION" value="PT2H">nearly two hours </TIMEX3>after it had docked.
Initially the Navy said the explosion occurred while several boats were helping
the ship to tie up. The change raises new questions about how the attackers
were able to get past the Navy security.
<TIMEX3 tid="t3" type="TIME" value="2000-10-20T20:02:28.05">10/20/2000 20:02:28.05</TIMEX3>
<TLINK timeID="t2" relatedToTime="t0" relType="BEFORE"/>
</TEXT>
</TimeML>
To avoid leaking knowledge about temporal structure, train, dev and test splits must be made at document level for temporal information extraction.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
2 leaderboard tables shown for this task, 2 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| TempEval-3 (2 rows) | Ning et al. | A Structured Learning Approach to Temporal Relation Extraction | — | — | Compare |
| TimeBank (2 rows) | Catena | CATENA: CAusal and TEmporal relation extraction from NAtural language texts | code | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
3 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
1 subtask in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
20 shown of 20 papers with code (86 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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1 Oct 2018 2 repositories listedNatural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these.
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7 Dec 2024 1 repository listedAccurate flood prediction is crucial for disaster prevention and mitigation.
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27 Jun 2024 1 repository listedOur method encapsulates the reasoning processes inherent to surgical activities enabling it to outperform the state-of-the-art by 4.
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9 May 2024 1 repository listed Syntology ran 5 of 8 samples · 3 unverifiedIn contrast, Point Cloud is a popular representation for processing 3-dimensional data and serves as an alternative method to exploit local and global spatial features.
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1 Mar 2024 1 repository listedWe introduce SoftTiger, a clinical large language model (CLaM) designed as a foundation model for healthcare workflows.
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29 May 2023 1 repository listed Syntology ran 8 of 12 samples · 4 unverifiedFinally, we use LLMs to augment and transform a large amount of audio-label data into audio-text datasets to alleviate the problem of scarcity of temporal data.
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11 Jan 2023 1 repository listedAll in all, these problems have limited the fair comparison between approaches and consequently, the development of temporal extraction systems.
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1 Jun 2022 1 repository listedHowever, the amount of training data for tasks like duration question answering, i.
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28 Sep 2020 1 repository listedTemporal information extraction is a challenging but important area of automatic natural language understanding.
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5 Aug 2020 1 repository listed Syntology ran 2 of 2 samples · 0 unverifiedIn the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.
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2 May 2020 1 repository listed Syntology ran 3 of 12 samples · 9 unverified · 12 pointer-only (licence)Temporal knowledge bases associate relational (s, r, o) triples with a set of times (or a single time instant) when the relation is valid.
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6 Sep 2019 1 repository listedInspired by the recent social movement of #MeToo, we are building a chatbot to assist survivors of sexual harassment cases (designed for the city of Maastricht but can easily be extended).
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28 Aug 2018 1 repository listedThe current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3)…
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1 Jun 2018 1 repository listedConventional annotation of judging temporal relations puts a heavy load on annotators.
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15 May 2018 1 repository listedIn this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model.
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1 Aug 2017 1 repository listedIn this paper, we describe the system of the KULeuven-LIIR submission for Clinical TempEval 2017.
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1 Apr 2017 1 repository listedWe propose a scalable structured learning model that jointly predicts temporal relations between events and temporal expressions (TLINKS), and the relation between these events and the document creation time (DCTR).
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1 Dec 2016 1 repository listedThe effects of the interaction between the temporal and the causal components, although limited, yield promising results and confirm the tight connection between the temporal and the causal dimension of texts.
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1 May 2016 1 repository listedGATE is a widely used open-source solution for text processing with a large user community.
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1 Sep 2015 1 repository listed
Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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