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Using Signals to Improve Automatic Classification of Temporal Relations

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Leon Derczynski, Robert Gaizauskas

Computation and Language · Submitted 22 Mar 2012

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In this paper, we use these signal words to improve the accuracy of a recent approach to classification of temporal links.

how it is stated · asserted 0.99 read the record →

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arXiv ID1203.5055 (version 1)
Pith IDIYQ6EL2F, Pith's permanent record of this paper and its review
CitationLeon Derczynski, Robert Gaizauskas. "Using Signals to Improve Automatic Classification of Temporal Relations." arXiv:1203.5055 (2012). https://pith.science/paper/IYQ6EL2F
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,672 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.09 · human participants 0.04 · controlled comparison 0.21 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.20 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Temporal information conveyed by language describes how the world around us changes through time. Events, durations and times are all temporal elements that can be viewed as intervals. These intervals are sometimes temporally related in text. Automatically determining the nature of such relations is a complex and unsolved problem. Some words can act as "signals" which suggest a temporal ordering between intervals. In this paper, we use these signal words to improve the accuracy of a recent approach to classification of temporal links.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Temporal information conveyed by language describes how the world around us changes through time. background 1.00 0.04 0.02 asserted 1.00
s1 Events, durations and times are all temporal elements that can be viewed as intervals. background 1.00 0.05 0.02 asserted 0.95
s2 These intervals are sometimes temporally related in text. background 1.00 0.05 0.02 hedged possibility 0.69
s3 Automatically determining the nature of such relations is a complex and unsolved problem. gap 0.65 0.05 0.03 asserted 1.00
s4 Some words can act as "signals" which suggest a temporal ordering between intervals. background 0.93 0.06 0.05 hedged possibility 0.63
s5 In this paper, we use these signal words to improve the accuracy of a recent approach to classification of temporal links. contribution 0.92 0.92 0.46 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.40.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Temporal information conveyed by language describes how the world around us changes through time. Events, durations and times are all temporal elements that can be viewed as intervals. These intervals are sometimes temporally related in text. Automatically determining the nature of such relations is a complex and unsolved problem. Some words can act as "signals" which suggest a temporal ordering between intervals. In this paper, we use these signal words to improve the accuracy of a recent approach to classification of temporal links.

Human review

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How the literature uses it

1 arXiv paper cite this in 1 sentence

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it

Uses its method or tool 1 paper
  • The features used are: POS tag, dependency relation, token and lemma of $e_1$($e_2$); dependency relations between $e_1$($e_2$) and their children; binary features indicating if $e_1$ and $e_2$ are related with the 'happens-before' or the 'similar' relation according to VerbOcean [ref] , if $e_1$ and $e_2$ have the same POS tag, or if $e_1$($e_2$) is the root and $e_1$ modifies (or governs) $e_2$; the dependency relation between $e_1$ and $e_2$ if they are directly connected in the dependency parse tree; prepositions that modify (or govern) $e_1$($e_2$); signal words [ref] and entity distance between $e_1$ and $e_2$.

    A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events · Evaluation · 0.86

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