Archive for the ‘Opinions’ Category

Summarize Opinions with a Graph – Part 1

Monday, August 13th, 2012

Summarize Opinions with a Graph – Part 1 by Max De Marzi.

From the post:

How does the saying go? Opinions are like bellybuttons, everybody’s got one? So let’s say you have an opinion that NOSQL is not for you. Maybe you read my blog and think this Graph Database stuff is great for recommendation engines and path finding and maybe some other stuff, but you got really hard problems and it can’t help you.

I am going to try to show you that a graph database can help you solve your really hard problems if you can frame your problem in terms of a graph. Did I say “you”? I meant anybody, specially Ph.D. students. One trick is to search for “graph based approach to” and your problem.

I’ll give you an example. The other day I ran in to “Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions” by Kavita Ganesan, ChengXiang Zhai and Jiawei Han at the University of Illinois at Urbana-Champaign.

I think you are going to like this. Max’s work is always interesting but this post is particularly so.

Has implications beyond opinion gathering.

Posted in Graphs, Neo4j, Opinions | No Comments »

Subjective Logic = Effective Logic?

Saturday, November 20th, 2010

Capture of Evidence for Summarization: An Application of Enhanced Subjective Logic

Authors(s): Sukanya Manna, B. Sumudu U. Mendis, Tom Gedeon Keywords: subjective logic, opinions, evidence, events, summarization, information extraction

Abstract:

In this paper, we present a method to generate an extractive summary from a single document using subjective logic. The idea behind our approach is to consider words and their co-occurrences between sentences in a document as evidence of their relatedness to the contextual meaning of the document. Our aim is to formulate a measure to find out ‘opinion’ about a proposition (which is a sentence in this case) using subjective logic in a closed environment (as in a document). Stronger opinion about a sentence represents its importance and are hence considered to summarize a document. Summaries generated by our method when evaluated with human generated summaries, show that they are more similar than baseline summaries.

The authors justify their use of “subjective” logic by saying:

pointed out that a given piece of text is interpreted by different person in a different fashion especially in the way how they understand and interpret the context. Thus we see that human understanding and reasoning is subjective in nature unlike propositional logic which deals with either truth or falsity of a statement. So, to deal with this kind of situation we used subjective logic to find out sentences which are significant in the context and can be used to summarize a document.

“Subjective” logic means we are more likely to reach the same result as a person reading the text.

Search results as used and evaluated by people.

That sounds like effective logic to me.

Questions:

  1. Read the Audun Jøsang’s article Artificial Reasoning with Subjective Logic.
  2. Summarize three (3) applications (besides the article above) of “subjective” logic. (3-5 pages, citations)
  3. How do you think “subjective” logic should be modeled in topic maps? (3-5 pages, citations optional)

Posted in Authoring Topic Maps, Information Retrieval, Opinions, Subjective Logic, Summarization | No Comments »