ReadWriteWeb

News volume has moved from infoscarcity to infobesity. For the last hundred years, news in print was delivered in a container, called a newspaper, periodically, typically every twenty-four hours. The container constrained the product. The biggest constraints of the old paradigm were periodic delivery and limitations of column inches.

Now information continually bursts through our Google Readers, our cell phones, our tablets, display screens in elevators and grocery stores. Do we really need to read all 88,731 articles on the Bernie Madoff trial? Probably not. And that's the dilemma for news organizations.

In the old metaphor, column-inches was the constraint. In the new metaphor, reader attention span becomes the constraint.

Among a handful of patents transferred last December 31 from IBM's portfolio to that of Google, as first discovered by Bill Slawski of SEO By the Sea, is a system for processing text compiled by users of social networks, and ascertaining their common interests. We've already seen the rise of tools such as Radian6 for ascertaining social net users' individual interests; this new technology, which received a U.S. patent only one year ago, would judge what concepts they share with one another.

The goal of this technology, as IBM originally stated, is to literally to filter out irrelevant links to articles that may not pertain to users' search intentions. What we don't know yet is whether Google intends to use this technology, or simply keep others from using it first.

A few months ago we linked to Tomaž Kovačič's overview of text extraction algorithms. Now Kovačič has posted an evaluation of several text extraction algorithms and services, including Boilerpipe, NCleaner, the Python and Node.js versions of Readability and the Extractiv API.

To conduct his evaluations, Kovačič used the cleaneval dataset, which includes 681 documents, and a Google News dataset with 621 documents harvested by the authors of Boilerpipe.

Here's another semantic search tool for Web application developers: Picky, a "a semantic text search engine for categorized data, such as varchar fields from a database." It's written in Ruby and you can grab the source here.

The developer, Florian Hanke, emphasizes that Picky is not a replacement for for full text search engines like Sphinx and Lucene. It's just for searching small, structured data very quickly.

The rise of social media has led to an exponential proliferation of content online and widespread demand for tools to filter that information. Popularity and relevance are the most common metrics through which to filter that content - but are they the best?

We asked three people building cutting-edge social software what they think the relationship between relevance, popularity and filtering is going to be in the future. They offered three very different responses. What do you think the future of information filtering will look like?

There are many reasons to love delicious and hope that it survives its current rocky patch, but as a programmer there's one thing I've found it essential for. I often write applications that need to process and organize thousands or millions of Web pages.

To do that, I need to know something about their meaning, what topics they're associated with, if they're blogs, political, technical, commercial, and what other categories they fall into. One way is run an API like Zemanta or OpenCalais on the pages' text, and hope to use significant terms to pick categories. This is an extremely intensive process on large collections, and even the best semantic analysis is nowhere near as good as a human summary. What if you could get millions of people to categorize the pages for you, for free?

Aro Mobile, a mobile communications startup backed by Microsoft's Paul Allen, made waves back in October when it emerged after three years in stealth as a suite of interconnected applications for Android smartphones. Installed as a single download from the Android Market, Aro places icons on user's homescreens: Phone, Email, Browser, Calendar, Contacts and Messaging. These are the core "PIM" (personal information manager) applications on mobile devices.

Because of Android's relative openness, Aro is able to completely integrate its PIM solution onto the Android mobile platform. But now, as the company prepares to launch its iPhone version, compromises had to be made. This begs the question: can innovation around core apps even work on iPhone?

As the world shifts to using more tablets, touchscreens and mobile devices as the point of access to the Web, there's an increasing need to rethink the keyboard. On smaller form factors, the traditional method of tap typing may no longer be the best way to enter text on a screen. Enter Siine, a semantically-based, intelligent interface that evolves the keyboard.

But this is no mere keyboard replacement "app," it's much more. It's a communication platform. A universal translator. A system that learns how you speak and then speaks for you. Siine is the future of text-based communication - or, at least that's what the company says.

The Palo Alto Research Center is releasing new semantic technology, based on Xerox PARC IP, in the form of an Outlook plugin called Meshin. At first glance, Meshin looks like the ugly stepsister to a similar Outlook tool called Xobni, as it also loads into an email sidebar window, displaying sections dedicated to recent conversations and a summary of attachments shared back and forth via email, among other things. But what makes Meshin different is the engine powering it underneath: a semantic technology that uses "natural language processing" to understand entities, how they connect and what they mean.

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The music industry has long been interested in big data, most notably for tracking sales to identify the "Top of the Charts" recording artists. But when it comes to data about the music itself, the industry has no set standard, so labels, retailers, hardware, and online streaming services all annotate music data in different ways.

UK startup Decibel hopes to rectify this, normalizing metadata across the industry and creating a massive semantic, cloud-based relational database of music.