clulab / timenorm   1.0.5

Apache License 2.0 GitHub

The timenorm library provides models for finding natural language expressions of dates and times and converting them to a normalized form.

Scala versions: 2.13 2.12 2.11


The timenorm library provides models for finding natural language expressions of dates and times and converting them to a normalized form.

Text to time expressions with the neural parser

The primary entry point for the library is the TemporalNeuralParser class, which implements a character-based recurrent neural network for finding and normalizing time expressions, as described in:

Egoitz Laparra, Dongfang Xu, and Steven Bethard. 2018. From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations. In: Transactions of the Association for Computational Linguistics 2018, Vol. 6, pp. 343–356

Dongfang Xu, Egoitz Laparra and Steven Bethard. 2019. Pre-trained Contextualized Character Embeddings Lead to Major Improvements in Time Normalization: a Detailed Analysis. In: Proceedings of The Eighth Joint Conference on Lexical and Computational Semantics.

To use the parser, create an instance of TemporalNeuralParser, and provide as an anchor the time at which your text was written.

scala> import org.clulab.timenorm.scate._
import org.clulab.timenorm.scate._

scala> val parser = new TemporalNeuralParser
parser: org.clulab.timenorm.scate.TemporalNeuralParser = org.clulab.timenorm.scate.TemporalNeuralParser@44c2e8a8

scala> val anchor = SimpleInterval.of(2019, 5, 30)
anchor: org.clulab.timenorm.scate.SimpleInterval = SimpleInterval(2019-05-30T00:00,2019-05-31T00:00)

When you pass text to the parser, it will return the predicted time expressions. Each time expression contains the span of characters that evoked the time expression. Interval-type time expressions also contain their start and endpoints on the timeline.

scala> val text = "I have not seen her since last year. We hope to meet in the next two weeks."
text: String = I have not seen her since last year. We hope to meet in the next two weeks.
scala> for (timex <- parser.parse(text, anchor)) timex match {
     |   case interval: Interval =>
     |     val Some((charStart, charEnd)) = interval.charSpan
     |     println(s"${interval.start} ${interval.end} ${text.substring(charStart, charEnd)}")
     | }

2018-01-01T00:00 2019-05-31T00:00 since last year
2019-05-31T00:00 2019-06-14T00:00 next two weeks

The parser runs faster if you pass batches of texts via the parseBatch (instead of the parse) method. So if you know, say, the sentence segmentation for your text, you may prefer to use that method.

Semantically compositional time operators

The TimeExpression objects returned by the neural parser are based on the set of semantically compositional operators described in:

Steven Bethard and Jonathan Parker. 2016. A Semantically Compositional Annotation Scheme for Time Normalization. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). pp. 3779-3786.

If you would like to manually construct complex time expressions, the operators are available in the org.clulab.timenorm.scate package, and can be combined with fields and units from the java.time library:

scala> import org.clulab.timenorm.scate._, java.time.temporal.ChronoField._, java.time.temporal.ChronoUnit._
import org.clulab.timenorm.scate._
import java.time.temporal.ChronoField._
import java.time.temporal.ChronoUnit._

scala> // the 3-year period following the year 1985
scala> NextP(Year(1985), SimplePeriod(YEARS, 3))
res0: org.clulab.timenorm.scate.NextP = NextP(Year(1985,0,None),SimplePeriod(Years,IntNumber(3,None),None,None),None)

scala> (res0.start, res0.end)
res1: (java.time.LocalDateTime, java.time.LocalDateTime) = (1986-01-01T00:00,1989-01-01T00:00)

scala> // the Friday the 13th following the 15th day of the 3rd month of 1985
scala> NextRI(
     |   ThisRI(
     |     ThisRI(
     |       Year(1985),
     |       RepeatingField(MONTH_OF_YEAR, 3)),
     |     RepeatingField(DAY_OF_MONTH, 15)),
     |   IntersectionRI(Set(
     |     RepeatingField(DAY_OF_WEEK, 5),
     |     RepeatingField(DAY_OF_MONTH, 13))))
res2: org.clulab.timenorm.scate.NextRI = NextRI(ThisRI(ThisRI(Year(1985,0,None),RepeatingField(MonthOfYear,3,None,None),None),RepeatingField(DayOfMonth,15,None,None),None),IntersectionRI(Set(RepeatingField(DayOfWeek,5,None,None), RepeatingField(DayOfMonth,13,None,None)),None),<function1>,None)

scala> (res2.start, res2.end)
res3: (java.time.LocalDateTime, java.time.LocalDateTime) = (1985-09-13T00:00,1985-09-14T00:00)

Normalizing time expressions with a synchronous context free grammar

This portion of the library is no longer recommended, but it is still included in the distribution.

The scfg portion of the library can take a time expression and normalize it to TimeML format using a synchronous context free grammar, as described in:

Steven Bethard. 2013. A Synchronous Context Free Grammar for Time Normalization. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 821-826.

Note that the scfg model cannot find time expressions in text; it can only normalize them after they are found. Sample usage:

scala> import org.clulab.timenorm.scfg._, scala.util.Success
import org.clulab.timenorm.scfg._
import scala.util.Success

scala> val parser = TemporalExpressionParser.en // English, Italian, and also Spanish are available.
parser: org.clulab.timenorm.scfg.TemporalExpressionParser = org.clulab.timenorm.scfg.TemporalExpressionParser@d653e41

scala> val Success(temporal) = parser.parse("two weeks ago", TimeSpan.of(2013, 1, 4))
temporal: org.clulab.timenorm.scfg.Temporal = TimeSpan(2012-12-17T00:00Z,2012-12-24T00:00Z,Period(Map(Weeks -> 1),Exact),Exact)

scala> temporal.timeMLValue
res0: String = 2012-W51


The TemporalExpressionParser is available in three languages: English (en), Italian (it), Spanish (es). The last is thanks to contributions of @NGEscribano from the timenorm-es project which includes additional helpful information in its Spanish TimeNorm document.