Measuring the Similarity between Implicit Semantic Relations from the Web

author: Danushka Bollegala, University of Tokyo
author: Yutaka Matsuo, University of Tokyo
author: Mitsuru Ishizuka, University of Tokyo
published: May 20, 2009,   recorded: April 2009,   views: 5253


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Measuring the similarity between semantic relations that hold among entities is an important and necessary step in various Web related tasks such as relation extraction, information retrieval and analogy detection. For example, consider the case in which a person knows a pair of entities (e.g. Google, YouTube), between which a particular relation holds (e.g. acquisition). The person is interested in retrieving other_such pairs with similar relations (e.g. Microsoft, Powerset). Existing keyword-based search engines cannot be applied directly in this case because, in keyword-based search, the goal is to retrieve documents that are relevant to the words used in a query -- not necessarily to the relations implied by a pair of words. We propose a relational similarity measure, using a Web search engine, to compute the similarity between semantic relations implied by two pairs of words. Our method has three components: representing the various semantic relations that exist between a pair of words using automatically extracted lexical patterns, clustering the extracted lexical patterns to identify the different patterns that express a particular semantic relation, and measuring the similarity between semantic relations using a metric learning approach. We evaluate the proposed method in two tasks: classifying semantic relations between named entities, and solving word-analogy questions. The proposed method outperforms all baselines in a relation classification task with a statistically significant average precision score of 0.74. Moreover, it reduces the time take by Latent Relational Analysis to process 374 word-analogy questions from 9 days to less than 6 hours, with a SAT score of 51%.

See Also:

Download slides icon Download slides: www09_bollegala_mtsisr_01.pdf (1.3┬áMB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 Miles Smith, July 5, 2019 at 10:09 p.m.:

Thank you so much for this. I was into this issue and tired to tinker around to check if its possible but couldnt get it done. Now that i have seen the way you did it, thanks guys

Comment2 charle ryals, June 18, 2021 at 9:48 p.m.:

This is so useful and helpful lecture for the students like me! Recently I have also found this website for knowing something different with the desired people around the world! I hope the authority of this platform will have been helping us with the useful lectures!

Write your own review or comment:

make sure you have javascript enabled or clear this field: