Talk: Understanding Storytelling

This week I attended a very interesting talk by Dr. Micha Elsner. Yes, this was one of those full-house ISP seminars. I was glad that I reached the venue a bit earlier than the usual. Dr. Elsner started his talk by giving us an overview of the bigger goals he is looking at. His work is helping us formally understand storytelling and develop computational methods for it. If you have ever used Auto Summarize in Word, you’ll have an intuitive idea about how it works: It finds sentences with frequently used words to make a summary of the document. It can generate satisfactory summaries for articles that merely state some facts, but would fail miserably in trying to understand and summarize a story.

Dr. Elsner’s approach focuses on observing social relationships between characters as the story unfolds, to understand the high level plot. He uses two basic insights about common plots in a story: a) it has an emotional trajectory, i.e. over time, we see a variation in negative and positive emotions, and b) characters interact with each other and have a social network just like in real life.

To begin his analysis, Dr. Elsner would first parse the text to identify characters from the noun phrases in the sentences. This step itself is not an easy one. For example, one character may be referred to by several different names through the chapters like – Miss Elizabeth Bennet, Miss Bennet, Miss Eliza, Lizzy and so on. Once we have that, we could try understanding the relationships between different characters over course of time. Simple functions measuring nearby mentions (co-occurrence) of the characters and their emotional trajectory curves are used to build a complex similarity measure. Emotion trajectory is plotted by finding words with “strong sentiment” cues. This makes up the first-order character kernel for measuring similarity. Now, he adds social network features to build the second order kernel. Characters are more similar if they each have close friends who are also similar.

I think that the method for testing the similarity the proof of concept was also an ingenious one. Dr. Elsner artificially re-orders the chapters of a book, and attempts to distinguish it from the one in the original form. Success here would imply that we indeed been able to gather some understanding about a plot by using this method. A corpus of novels from Project Gutenberg is used as a training data for this purpose. Do go through the links in the section below to find out more!

Further Reading

  1. Micha Elsner. Character-based Kernels for Novelistic Plot Structure. Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), Avignon, France. Available: http://aclweb.org/anthology-new/E/E12/E12-1065.pdf
  2. Presentation slides are also available on Dr. Elsner’s page: http://www.ling.ohio-state.edu/~melsner/slides/novelpres.pdf

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