Eun Y. Ha, Alok Baikadi , Carlyle Licata, Bradford Mott, James Lester SIGDIAL 2010

1. Exploring the Effectiveness of Lexical Ontologies for Modeling Temporal Relations with Markov Logic Eun Y. Ha, AlokBaikadi, Carlyle Licata, Bradford Mott, James Lester SIGDIAL 2010

2. Introduction • Correctly recognize the temporal aspects of events in discourse. • Applies to question answering and document summarization. • Account for local and global relation of events.

3. Tools and Toolkits • Markov Logic • Lexical Ontology • TempEval-2 (Implicit and Explicit Temporal location and order of these events: English, Chinese, Italian, Spanish, Korean, and French) • Use English: 163 news articles and 53,000 tokens for training. • 11 news articles for test. • Wordnet, VerbOcean, NLTK, Markov TheBeast

4. Related Work • Boguraev and Ando (2005) semi-supervised learning to recognize events and infer temporal relations. • Mani (2006) temporal relations between events and time expressions with MaxEnt. • Mostly use local pairwise classifier. (Lack of global consistency. • Chamber and Jurafsky (2008) add global constraints. • Yoshikawa (2009) use Markov Logic. (This paper extends their work).

5. Goal • Build on Yoshikawa (2009): • Lexical Relation Features • Address new task in TempEval2 • Phrase-base syntactic features • Temporal Relations: before, after, overlap, before-or-overlap, overlap-or-after, and vague. • Identification Goal: • Event and time in same sentence • Two main events in consecutive sentences • Two events where one dominates syntactically

6. Annotation t1 and e1 are overlap. e1 occurs after e2. e2 overlaps with e3.

7. Features • Surface Features: • Events are one word • Time can be multiple words, treat as one unit • Semantic Features: • Semantic Attributes of events and time • Use manually written annotation as gold-standard • Syntactic Features: • gov-prep: governing preposition (for ten years) • gov-verb: verb governing event or time • gov-verb-pos: pos tag of the governing verb • Lexical Relations: • Semantic relations between two events with VerbOcean and WordNet

8. Markov Logic Networks • Weighted first-order clauses constituting a Markov network. • Collection of formulas from first order logic each with assign a weight. • Vertices are atomic formulas and edges are logical connectives

9. Modeling Temporal Relations with MLN 1/2 • Two types of predicates: • Hidden: not observed directly during test time. • relEventTimex(temporal relation between event and time expression) • relMainEvents (temporealrealtion between two main events) • relMainSub (temporal relation between main and dominated event • Observed: observed at test and represent one of the previously defined features

10. Modeling Temporal Relations with MLN 2/2 Event e Time expression t Document d Given this candidate pair to temporal relation r whenever it observes a word token w in the given event from the training data. Local because only one hidden predicate (relEventTimex) Global because it has two hidden predicates

11. Feature Set Optimization Feature set optimized for each task with 10% of the training data One model (NoLex) without lexical features Another (Full) with all features ET – relation between event and temporal MM – relation between two main events MS – two main and one syntactically dominated

12. Experimental Results Full shows improvements over NoLex and same or better in all categories

13. Conclusion • This approach presumes that events and time are already marked. • Markov Logic achieve state of the art performance. • Extend to recognize events and temporal relations in the text as well as their semantic attributes.