View source for Das Sarma, Jain, and Yu, Dynamic Relationship and Event Discovery, WSDM 2011

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== Citation ==

{{MyCiteconference | booktitle = Proceedings of the fourth ACM international conference on Web search and data mining| coauthors = A. Jain, C. Yu| conference = ACM| date = 2011| first = A.| last = Das Sarma| pages = 207-216| title = Dynamic relationship and event discovery| http://www.eecs.umich.edu/~congy/work/wsdm11.pdf }}

== Online version ==

This [[category::paper]] is available online [http://www.eecs.umich.edu/~congy/work/wsdm11.pdf].

== Summary ==

This [[Category::paper]] presents a different perspective to the works of [[AddressesProblem::temporal information extraction]]. Unlike other works that focus on assigning time to categories or relations that are extracted according to a schema, this paper aims to extract relations that are not predefined by an existing schema, rather relations that are dynamic and temporally defined (i.e. co-bursting relationships). The paper also identifies temporally constrained events underlying these relationships by consolidating binary dynamic relations between entities into an n-ary relation involving a set of entities connected at a given time period. Two formal notions are introduced for detecting these dynamic events: ''global temporal constraint cluster'' and ''local temporal constraint cluster''. The motivation behind this paper is to extract dynamic events (n-ary relations) that cannot be predefined - for example, the majority of relations associated with news events: boating accident, plane crash landing due to landing gear failure, etc. 

== Brief description of the method ==

The model has two components: (1) pairwise classifier between events, (2) global constraint satisfaction layer that maximizes the confidence scores from the classifier. 

In the first component, [[Support_Vector_Machines|Support Vector Machine]] (SVM) classifier is used. Using features varying from POS tags and lexical features surrounding the event to tense, grammatical aspect features of the events, probabilities of temporal relations between pairwise events are computed. These scores are then used as confidence scores to choose an optimal global ordering. 

In the second component, the ILP uses the following objective function:

::<math> max \sum_{i} \sum_{j} p_{ij} x_{ij} \,\! </math> 

with the constraints:

::<math> \forall_{i} \forall_{j} {x_{ij}} \in {0, 1} </math>

::<math> \forall_{i} {x_{i1}} + {x_{i2}} + ... + {x_{im}} = 1\,\!</math>

::<math> {x_{ia}} + {x_{jb}} - {x_{kc}} <= 1 \,\!</math>

where <math>{x_{ij}}\,\!</math> represents the ith pair of events classified into the jth relation of ''m'' relations. 

The first constraint simply says that each variable must be 0 or 1. The second constraint says that a pair of events cannot have two relations at the same time. The third constraint is added for connected pairs of events <math>i, j, k \,\!</math>, for each transitivity condition that infers relation <math>c\,\!</math> given <math>a\,\!</math> and <math>b\,\!</math>.

Prior to running the two components, the set of training relations is expanded to create a more well-connected network of events. One way to expand it is to perform temporal reasoning over the document's time expression (e.g. ''yesterday'' is before ''today'') to add new relations between times. Once new time-time relations are added, transitive closure is conducted through transitive rules that creates new connections in the network, such as:

''A simultaneous B'' <math>\and</math> ''A before C'' <math>\to</math> ''B before C''

== Experimental Result ==

The experiment is done on the task of classifying temporal relations between events into ''before'', ''after'', or ''vague'' (unknown) relations on the [[UsesDataset::TimeBank Corpus]]. These are the core relations in the [http://www.timeml.org/tempeval/ TempEval-07] temporal ordering challenge. 

Training and testing are done using 10-fold cross validation and micro-averaged accuracies. The method shows 3.6% absolute increase in the accuracy of ''before/after'' classification over the local, pairwise classification model. This improvement is statistically significant (p < 0.000001, Mc-Nemar's test, 2-tailed).

Using time expression normalization to create new relations between time expressions, transitive closure over the original set of temporal relations in the corpus, and additional event-event temporal information from [[RelatedPaper::Bethard et. al., Timelines from text: Identification of syntactic temporal relations, 2007|Berthard et al. (2007)]], the method shows an 81% increase in the number of relations in the corpus to train on.

== Discussion ==

Both the increased connectivity of the corpus and the global inference contributed to the improved performance. Global inference alone on the original set of temporal relations in the corpus shows no improvement over pairwise classification model. This is due to the sparseness of the corpus (since tagging is done manually, the vast majority of possible relations are untagged). Global constraints cannot assist local decisions if the graph is not connected. This highlights the importance of time expression normalization and transitive closure to make the corpus more well connected prior to conducting global inference.

Unfortunately, this paper does not differentiate between improvement in performance contributed by the increased connectivity and the improvement contributed by the global inference. Could the increased performance be contributed solely by the increased connectivity of the graph? There is a need for a separate evaluation on the accuracy of relations inferred using only pairwise classification and transitive closure (without global inference).

== Related papers ==

An interesting follow-up paper is [[RelatedPaper::Yoshikawa 2009 jointly identifying temporal relations with markov logic]] which uses Markov Logic instead of Integer Linear Programming to do a ''softer'' (non-deterministic) joint inference. 

Another related paper is [[RelatedPaper::Denis and Muller, Predicting Globally-Coherent Temporal Structures from Texts via Endpoint Inference and Graph Decomposition, IJCAI 2011]] which attempts to classify all types of temporal relations (not just ''before''/''after'') in TimeBank Corpus by first translating these 13 temporal interval relations to their end points, making the set of constraints much smaller for the Integer Linear Programming to deal with, while preserving the same temporal information.