SUTime: A library for recognizing and normalizing time expressions

By Angel X. Chang, Christopher D. Manning. 2012

472 citations recorded by the source corpus.

We describe SUTIME, a temporal tagger for recognizing and normalizing temporal expressions in English text. SUTIME is available as part of the Stanford CoreNLP pipeline and can be used to annotate documents with temporal information. It is a deterministic rule-based system designed for extensibility. Testing on the TempEval-2 evaluation corpus shows that this system outperforms state-of-the-art techniques.

Subfields: Computer Networks and Communications, Natural Language Processing, Signal Processing

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Citations in the corpus

Semantic Parsing on Freebase from Question-Answer Pairs (2013, methodology): “In addition, we normalized dates with SUTime (Chang and Manning, 2012).”
News events prediction using Markov logic networks (2018, methodology): “A temporal tagger for recognising and normalising temporal expressions (SUTIME [47]) was also used to annotate documents with temporal information.”
TempQuestions: A Benchmark for Temporal Question Answering (2018, methodology): “First, we ran temporal expression taggers SUTime [11] and HeidelTime [26] over all questions.”
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (2018, methodology): “While detecting explicit time expressions can be done reasonably well using existing time taggers (Chang and Manning, 2012), identifying implicit ones is difficult.”
More Accurate Question Answering on Freebase (2015, methodology): “We also identify dates and values using SUTime [8].”
Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis (2015, methodology): “For instance, hybrid approaches combining rule-based systems such as HeidelTime [69], SUTIME [70] and GUTIME [67] with CRF or SVM machine learning models proved useful for time expression classification (up to 90% span F1), CRF models for event span detection (up to 92% F1), SVM models for event…”
Fine-grained location extraction from tweets with temporal awareness (2014, background): “, SUTIME [2] TARSQI [28]) becomes less practical.”
Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach (2019, background): “, 2005), a date/time extraction library (Chang and Manning, 2012), and a Wikidata entity linker (Pappu et al.”
Neural Models of Factuality (2018, methodology): “Their scoring method involves the use of ﬁne-grained POS tags, the Stanford Temporal Tag-ger (Chang and Manning, 2012), and a number of heuristic rules, which resulted in a conﬁrma-tion that tense agreement statistics are predictive of implicativeness, illustrated in part by observing a near…”
Extracting Human Temporal Orientation from Facebook Language (2015, methodology): “Of the numerous TempEval tasks, we build upon those which identify time expressions and resolve their expressed time and date relative to the time of writing (e.g. the time expression ‘yesterday’ in a document written on January 15, 2014 is resolved as January 14, 2014 ).”
NAREOR: The Narrative Reordering Problem (2021, background): “Timex detectors like SUTime (Chang and Manning 2012) only mark strict timexes (e.g. last Sunday ) but not others (e.g. before midsems ).”
T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples (2018, methodology): “We use the Stanford temporal tagger SUtime [Chang and Manning2012] to extract temporal expressions and their locations in documents.”
Efficient Temporal-aware Matryoshka Adaptation for Temporal Information Retrieval (2026, unknown): “We use SUTime (Chang and Manning, 2012) to identify temporal expressions at the sentence level.”