Yoshikawa 2009 jointly identifying temporal relations with markov logic

Citation

Jointly Identifying Temporal Relations with Markov Logic, by K. Yoshikawa, J. NAIST, S. Riedel, M. Asahara, Y. Matsumoto. In Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, 2009.

Online version

This Paper is available online [1].

Summary

This paper is a follow up paper to Chambers and Jurafsky (2008) that focuses on using global inference to improve local, pairwise temporal ordering of events in text. Similarly in this paper, instead of predicting the three types of temporal relations: between events in adjacent sentences, between events and time expressions in the same sentence, and between events in a document and the document creation time (DCT), in isolation; the paper proposes to use Markov Logic Networks to jointly identify relations of all three relation types simultaneously while respecting logical constraints between these temporal relations.

The experiment is done on the TempEval-07 data, for the task of classifying temporal relations into one of the 6 classes: BEFORE (e.g. event A is before event B), OVERLAP, AFTER, BEFORE-OR-OVERLAP, OVERLAP-OR-AFTER, and VAGUE (unknown). The paper shows an accuracy increase of 2% for all three types of relations: event-event, event-time, event-DCT, compared to other machine learning based approaches.

Brief description of the method

The paper uses Markov Logic Network to represent constraints of temporal consistency. Three hidden predicates corresponding to the temporal relations to be predicted are:

• relE2T(e,t,r) representing the temporal relation r between an event e and a time expression t
• relDCT(e,r) representing the temporal relation r between an event e and DCT
• relE2E(e1,e2,r) representing the temporal relation r between two events of adjacent sentences e1 and e2

The observed predicates, corresponding to information that is given are:

• words, syntactic and lexical feature predicates. For example, the predicate tense(e,t) denotes the tense t for an event e
• relT2T(t1,t2,r) denoting the temporal relation r between two time expressions t1 and t2
• dctOrder(t,r) representing the temporal relation r beetween a time expression t and DCT.

The illustration of all temporal predicates are given in the figure below, where dashed lines indicate observed predicates:

From these predicates, several formulae that represent constraints of temporal consistency are constructed. These formulae are then input to Markov Logic Network. The formulae are divided into two classes:

• local formulae - formulae that only consider the predicates of a single event-event, event-time or event-DCT pair, for example:

$tense(e1,past)\land tense(e2,future)\Rightarrow relE2E(e1,e2,before)\,\!$

• global formulae - formulae that involve two or more predicates at the same time, and consider the three tasks: event-event, event-time, event-DCT temporal relations predictions, simultaneously. For example:

$dctOrder(t1,before)\land relDCT(e1,after)\Rightarrow relE2T(e1,t1,after)\,\!$

Experimental Result

The experiment is done using the data from TempEval temporal ordering challenge, with the tasks of classifying temporal relations between events and time expressions (Task A), between events and the DCT (Task B), and between events in two consecutive sentences (Task C). Temporal relations are classified into one of 6 classes: BEFORE, OVERLAP, AFTER, BEFORE-OR-OVERLAP, OVERLAP-OR-AFTER, and VAGUE. Training and inference algorithms are provided by Markov thebeast, a Markov Logic interpreter tailored for NLP applications. Accuracy for measuring performance is defined as:

${\frac {{C_{a}}+{C_{b}}+{C_{c}}}{{G_{a}}+{G_{b}}+{G_{c}}}}\,\!$

where ${C_{a}}$, ${C_{b}}$, ${C_{c}}$ are the number of correctly identified labels in each task, and ${G_{a}}$, ${G_{b}}$ and ${G_{c}}$ are the number of gold labels of each task.

The paper shows that by incorporating global constraints that hold between temporal relations predicted in Task A, B, and C, the accuracy for all three tasks can be improved significantly. For two out of the three tasks, the approach in this paper achieves the best accuracy by at least 2% more than other approaches. For task B, the approach's accuracy is less than that of rule-based approach; however it is better than all other machine learning approaches.

Related papers

The approach in this paper is similar to that of an earlier work by Chambers and Jurafsky (2008) that proposes to use global framework based on Integer Linear Programming (ILP) to jointly infer temporal relations between events. Chambers and Jurafsky (2008) show that adding global inference improves the accuracy of the inferred temporal relations. However they only focus on event-event temporal relations while this paper also jointly predicts temporal order between events and time expressions, and between events and document creation time.

Secondly, Chambers and Jurafsky (2008) combines the output of local classifiers using ILP framework while this paper uses Markov Logic Networks which represents global constraints through the addition of weighted first order logic formulae. The advantage is that it allows for representation of non-deterministic rules that tend to hold between temporal relations but do not always have to. For example, if A happens before B and B overlaps with C, then there is a good chance that A also happens before C, but this is not always the case. The learned weights of the rules allow for soft enforcement of the constraints.