Measuring the Influence of Errors Induced by the Presence of Dialogs in Reference Clustering of Narrative Text

1. Measuring the Influence of Errors Induced by the Presence of Dialogs in Reference Clustering of Narrative Text Alaukik Aggarwal, Department of Computer Science and Engineering, MAIT Pablo Gervás, Instituto de Technologia del Concimiento, Universidad Complutense de Madrid Raquel Hervás, Instituto de Technologia del Concimiento, Universidad Complutense de Madrid

2. Outline of the Problem • Coreference Resolution = Anaphoric + Non-anaphoric • Different genres of text studied: • Text without dialogues (like news articles) • Text consisting only of dialogues (conversations)

3. An Example • Sachin Tendulkarhas been honoured with Padma Vibhushan Award. India’sworld number one batsman secured 17,000 runs on home soil. Tendulkar has put India in a strong position against Australia in the One-Day Series. The Indian responded to his critics who believed that his career was sliding with his 40th century. Generally the kind of text found in News Articles.

4. Problems in Dialogue - Why? • Pronominal Reference within quoted fragments • Change in referential value of demonstratives • “You take these bags and I’ll take those” • Non-NP antecedents or no antecedents at all

5. Coreference in Narrative • Contain many characters and objects • Rich in dialogues and coreferences • Cover different style of writing from different authors and time periods

6. Another Example • The two elder sons did not delay but set off at once, and the third and youngest son began pleading. "No, my son, you mustn't leave me, an old man, all alone," said the king. "Please let me go, Father! I do so want to travel over the world and find my mother." The king reasoned with him, but, seeing that he could not stop him from going, said: "Oh, all right then, I suppose it can't be helped. Go and God be with you!" An excerpt from Three Kingdoms (by Alexander Afanasiev )

7. Quantitatively Analyzing the Presence of Dialogs in Narrative Texts

8. Resolving Coreference in NPs • Knowledge-rich and Knowledge-poor • Different approaches considered by us: • Decision trees • C4.5 Machine Learning algorithm • Clustering • Hybrid

9. Corpus of narrative texts • Thirty folk tales in English • Different styles, authors and time periods • Rich in dialogs between characters • Process: • Identify references • Enrich references with semantic information • Coreference resolution using a clustering approach

10. Step 1: Identifying References • GATE (General Architecture for Text Engineering) • Annie Sentence Splitter • Annie English Tokeniser • Annie POS Tagger • CREOLE plugin • Output in XML format

11. Step 2: Feature Extraction • Position • Part of Speech (POS) • Article • Number • Semantic Class • WordNet (sysnets) • Gender • A resource of Gender data

12. Annotated Data

13. Step 3: Algorithm and Working • Based on the clustering algorithm by (Cardie and Wagstaff, 1999) • dist(NPi, NPj) = ∑ wf * incompatibility (Npi, NPj) f Є F • Feature (f) - Position, Pronoun, Article, Word-substring, Number, Semantic Class, Gender

14. Evaluation and Results

15. Evaluation • Clustering algorithm over the tales twice • With dialogs • Without dialogs • Hand correction of the obtained coreferences for comparison • Precision and recall

16. Results • Precision and Recall Results with and without dialogues:

17. Conclusions • Nested dialogues decrease the efficiency by 9% in Precision and 7% in Recall • But information lost if dialogues are removed • Dialogs need to be treated separately • In addition, constructed a corpus of tales annotated with coreference information for nominal phrases

18. Future work • Dialogs could be extracted from the tale, and considered as a separated text • Information about the characters involved is required • Possible improvements in different problems • Word Sense Disambiguation • Named Entity Recognition

19. Thank You.