Deriving Event Relevance from the Ontology Constructed with Formal Concept Analysis Wei XU

2 Index Overview Motivation Construct Event Ontology Measure Event Relevance from Ontology Evaluation on Event-based Summarization Future Work Main Reference

3 Overview Text Summarization Event Relevance Importance of Event Sentence Ranking Event Ontology Event Extraction

4 Motivation(1) 1. Why we focus on event ? –Event reveals the important concepts in documents. Who did What to Whom When and Where –Event describes the relationship between concepts. What Who Whom When Where

5 Motivation(2) 2. Why we use relevance ? –If an event is relevant with more other events, If an event is relevant to important event, Then this event is assumed to be more important. –PageRank Event1 Event4 Event3 Event5 Event2

6 Motivation(3) 3. Why we base on Ontology ? –Ontology: A system containing the concepts and their relationships –can be utilized to analyze the relevance between concepts

7 Overview Text Summarization Event Relevance Importance of Event Sentence Ranking Event Ontology Event Extraction

8 Construct Event Ontology(1) 1. Event Extract –Action word between two name entities in a sentence Netscape chairman James Clark spoke boldly of attacking Microsoft head-on. Event1: ( spoke | James Clark, Microsoft ) Event2: ( attacking | James Clark, Microsoft )

9 Construct Event Ontology(2) 2. Construct Event Ontology by FCA –FCA: Formal Concept Analysis What Object Who When Attribute Where NetscapeJames ClarkMicrosoft spoke** attacking**

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11 Construct Event Ontology(4) 2. Construct Event Ontology by FCA –equivalent : Objects are equivalent when they are associated with exactly the same attributes. –super-class : The object with subset of attributes is considered as a super-class of the object with superset of attributes.

12 Measure Event Relevance from Ontology(1) 0. Basic Principle –If two events are concerned with the same person or same location, or occurred at the same time, –Then these two events are probably interrelated with each other.

13 Measure Event Relevance from Ontology(2) 1. Basic Measure (Binary & Symmetrical) –If two event are equivalent, Relevance=1. –Otherwise Relevance=0.

14 Measure Event Relevance from Ontology(3) 2. Extended Measure (Binary & Asymmetrical) –If two event are equivalent, Relevance=1. –if the event1 is the super-class of the event2, Relevance(event1, event2)=1, Relevance(event2, event1)=0, –Otherwise Relevance=0.

15 Measure Event Relevance from Ontology(4) 3. Further Extended Measure (Binary & Symmetrical) –If two events have at least one attribute in common, Relevance=1 –Otherwise Relevance=0. Relevance

16 Measure Event Relevance from Ontology(5) 4. Further Futher Extended Measure –strength of the relation between events Binary Scaled 0 or 1 0 ~ 1 such as

17 Overview Text Summarization Event Relevance Importance of Event Sentence Ranking Event Ontology Event Extraction PageRank

Evaluation on Event-based Summarization DUC 2004 multi-document summarization task ROUGE-1ROUGE-2ROUGE-W centroid BSM SAM BAM BSM SAM

19 Future Work 1. Refine the method of event extraction –Capture more concept in documents 2. event =What+3W(When, Who, Where) –Now: Relevance between event-What, measuring by 3W(attribute of What). –Future: What and 3W have equal roles in evaluating the importance of concept.

20 Main Reference 1. FCA, use FCA to construct ontology –Formal Concept Analysis Homepage –S.J. Li, Q. Lu, W.J. Li: Experiments of Ontology Construction with Formal Concept Analysis. IJCNLP05 2. Event-based Summarization –N. Daniel, D. Radev and T. Allison: Sub-event based Multi-document Summarization. HLT-NAACL03 Workshop –E. Filatova and V. Hatzivassiloglou: Event-based Extractive summarization. ACL04 Workshop

21 Thank you!