13 Mar
ChirpCity - An application written on top of Twitter that organizes tweets from and about a city. On first blush, not sure what value the application is providing. Now what would be interesting is if the the tweets were organized by category.
For e.g. When I typed (or clicked) Manhattan Beach, show me all the tweets about Restaurants, Movies, What’s happening at the beach, etc. That gives me a “real time” view that is contextualized.
Here is a rough draft with two verticals:
03 Feb
Evri’s topic pages (screenshot below) show related information about a particular topic, whether it be a named entity (Person, Place, Location, Company) or a Thing. These pages allow you to explore the relationship between content and associated topics by providing filters for drilldown. The relationships help better understand the information as well as allow you to jump from one topic to another, thus enabling discovery.
You can read more about Evri’s Entity Extraction here.
28 Dec
Solr now supports Tika through ExtractingRequestHandler
It is now possible to send any of Tika’s supported document types (MS Office, PDF, XML, HTML, etc.) and have the content extracted and then indexed, all within Solr.
A natural enhancement / extension to Metadata extraction and identification toolkit would be to layer a content analysis framework on top. For some verticals (especially news), there is value in extracting named entities out of the content from content sources (documents or web pages). These named entities can then be added to Solr that can allow users to slice and dice information by People, Company, Places, etc. Once there, it becomes a great platform for entrepreneurs to develop applications on top of it and not have to worry about entity extraction.
There are already a number of options for extracting entities from text (LingPipe, OpenCalais). The task is to standardize and wrap them in a framework that can be easily plugged into Solr (atleast to start with).
Grant, is someone already working on it? Any plans in the pipeline?
