Evri Blog

We thought about launching a labs site where we could showcase our latest gadgetry, but decided none of us really fancy wearing lab coats. Many of us have gardens, however, and a few of us wear overalls, so we figured we’d instead start a garden to sprout new ideas. So, voila: we have a new section of our site called Evri’s Garden where we’ll be showcasing our fresh but not fully farmed veggies. Our first garden sprout is Evri Search, which I’ll spend some time chatting about.

Evri Search exposes our text analysis infrastructure that automatically identifies and makes available linguistic links connecting people, places and things found on the web. To provide this enhanced search capability, Evri Search performs an exhaustive deep natural language processing based analysis of every sentence in our corpus. This search interface allows you to directly interact with the same back end system our scientists and engineers use everyday to fine tune the algorithms used in our applications to search on your behalf. The help section on the search page is pretty exhaustive, so I thought it would be more entertaining to just walk through some interesting queries.

One of my favorite queries is to find corporate acquisitions. To do so using the Evri query language (EQL), I can construct a query like:

[Organization/Name]>buy>[Organization/Name]

In this query, I am asking the system for all sentences containing a grammatical clause where the source of an action is a named organization (usually companies but also non profits and government agencies), the action is the verb buy (or similar verbs), and the target of the action is also a company.  Here is a screen shot of the results the day I executed the query:

Note: the system has over 24000 instances of acquisitions, and I am shown them in ranked order. One day I will chat more about how we do this ranking, but in the mean time, suffice it to say many factors impact this ordering, including, but not limited to: relevance of the document, verb condition, importance of the entities, relationship parts of speech, relationship redundancy, document timeliness, and credibility of the source.

Now also note: I’m shown the name of the acquiring company and the name of the acquired company; I’m not sent off to a web page to sift through acquisitions nor do I need to merge results from multiple websites containing acquisitions. A key goal of traditional search engines, as well as many semantic search engines, is to point users to documents, or web sites, where users are expected to read the results and assimilate the information they are after. Evri Search excels at distilling relationships, or facts, from disparate web sites — this ultimately enables users to read less, and understand more. Now let’s expand the first result:

Note: the relationship: Bank of America > buy > Merrill Lynch was extracted from multiple different sentences, or different ways of expressing the same thing. Also note: you can click on the article titles to visit the article and read the sentence containing the matched relationship in context. Let’s do a slight modification of this query now, and execute:

[Organization/Name]>buy>[Organization/Name] PREP CONTAINS [Money]

Now we are asking for the same relationships as before, except now we only want relationships where the complement of the preposition is a monetary amount. In other words, the sentence should contain language like: Company X bought Company Y for Z dollars. Here is the first result expanded:

Note: In every sentence, the monetary amount of the acquisition is mentioned. Now lets say we want to get even more constrained. Lets say we only care about acquisitions with the amount mentioned but in the media sector. We could constrain the query a bit more:

[Organization/Name]>buy>[Organization/Name] PREP CONTAINS [Money] CONTEXT CONTAINS media

Now we are asking the system for the same results as before, except the context (the sentence containing the relationship, the sentence before or the sentence after) must contain the word media. Note: the results are now focused on the media sector:

You may, on occasion, note that the sentence matching a query does not contain the name of the entity. For example, in the query:

shark>attack>[Person/Name]

I expanded the first result (when I ran the query), and got:

Note: the shark attack victim is not mentioned in the matched sentence shown in black. This is because the pronoun she is referring to Bettina Pereira mentioned in a previous sentence. Evri Search is able to understand, similar to the way a human does, that pronouns (along with other anaphora like the company and the lawyer) refer to other named entities.

I’ll now leave this post with a few queries to help you get started geeking out with Evri Search. Feel free to try these queries out on your favorite keyword or semantic search engine.

• Actors or actresses entering rehab:
  [Actor]>enter>rehab
• Plane crashes involving casualties:
  plane>crash CONTEXT CONTAINS [Number]
• Companies filing bankruptcy:
  [Company]>file>bankruptcy
• People scoring points playing basketball:
  [Person/Name]>score>points CONTEXT CONTAINS basketball
• Musicians singing cover songs by the Beatles, Grateful Dead or Bob Dylan:
  [Musical_artist]>sing>(Beatles OR Grateful Dead OR Dylan) CONTEXT CONTAINS (cover OR song)

Finally, if you find any great searches you’d like to share, you’ll find the +share link in the top right of your browser that links to all your favorite bookmarking apps, else we’d love for you to drop it in the comments section here. Have fun geeking out on Evri Search.

This entry was posted on Tuesday, September 30th, 2008 at 5:36 pm and is filed under Evri Search, Evri's Garden, Tech talk. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.