Tag: newsreader

NewsReader – the developers story


This post was first published at ScraperWiki.

ScraperWiki has been a partner in NewsReader, an EU Framework 7 research project, for the last couple of years. The aim of NewsReader is to give computers the power to “understand” the news; to extract from a myriad of news articles the underlying events which gave rise to those articles; the who, the where, the why and the what of those events. The project is comprised of academic researchers specialising in computational linguistics (VUA in Amsterdam, EHU in the Basque Country and FBK in Trento), Lexis Nexis – a major news aggregator, and a couple of small technology companies: ourselves at ScraperWiki and SynerScope – a Dutch startup specialising in the visualisation of complex networks.

Our role at ScraperWiki is in providing mechanisms to enable developers to exploit the NewsReader technology, and to feed news into the system. As part of this work we have developed a simple REST API which gives access to the KnowledgeStore, the system which underpins NewsReader. The native query language of the KnowledgeStore is SPARQL – the query language of the semantic web. The Simple API provides a set of predefined queries which are easier for end users to work with than raw SPARQL, and help us as service managers by providing a predictable set of optimised queries. If you want to know more technical detail then we’ve written a paper about it (here).

The Simple API has seen live action at a Hack Day on World Cup news which we held in London in the summer. Attendees were able to develop a range of applications which probed violence, money and corruption in the realm of the World Cup. I blogged about our previous Hack Day here and here. The Simple API, and the Hack Day helped us shake out some bugs and add features which will make it even better next time.

“Next time” is another Hack Day to be held in the Amsterdam on 21st January 2015, and London on the 30th January 2015. This time we have processed 6,000,000 articles relating to the car industry over the period 2005-2014. The motor industry is a trillion dollar a year business, so we can anticipate finding lots of valuable information in this horde.

From our previous experience the three things that NewsReader excels at are:

  1. Finding networks of interactions, identifying important players. For the World Cup Hack Day we at ScraperWiki were handicapped slightly by having no interest in football! But the NewsReader technology enabled us to quickly identify that “Sepp Blatter”, “Jack Warner” and “Mohammed bin Hammam” were important in world football. This is illustrated in this slightly cryptic visualisation made using Gephi:beckham_and_blatter
  2. Finding events of a particular type. the NewsReader technology carries out semantic role labeling: taking sentences and identifying what type of event is described in that sentence and what roles the participants took. This information is then aggregated and exposed using semantic web technology. In the World Cup Hack Day participants used this functionality to identify events involving violence, bribery, gambling, and other financial transactions;
  3. Establishing timelines. In the World Cup data we could track the events involving “Mohammed bin Hammam” through time and the type of events he was involved in. This enabled us to quickly navigate to pertinent news articles.Timeline

You can see fragments of code used to extract these data using the Simple API in these GitHub Gists (here and here), and dynamic visualisations illustrating these three features here and here.

The Simple API is up and running already, you can find it (here). It is self-documenting, simply visit the root URL and you’ll see query examples with optional and compulsory parameters. Be aware though: the Simple API is under active development, and the underlying data in the KnowledgeStore is being optimised for the Hack Days so it may not be available when you visit.

If you want to join our automotive Hack Day then you can sign up for the Amsterdam event (here) and the London event (here).

Book review: Natural Language Processing with Python by Steven Bird, Ewan Klein & Edward Loper


This post was first published at ScraperWiki.

I bought Natural Language Processing in Python by Steven Bird, Ewan Klein & Edward Loper for a couple of reasons. Firstly, ScraperWiki are part of the EU Newsreader Project which seeks to make a “history recorder” using natural language processing to convert large streams of news articles into a more structured form. ScraperWiki’s role in this project is to scrape open sources of news related material, such as parliamentary records and to drive exploitation of the results of this work both commercially and through our contacts in the open source community. Although we’re not directly involved in the natural language processing work it seems useful to get a better understanding of the area.

Secondly, I’ve recently given a talk at Data Science London, and my original interpretation of the brief was that I should talk a bit about natural language processing. I know little of this subject so thought I should read up on it, as it turned out no natural language processing was required on my part.

This is the book of the Natural Language Toolkit Python library which contains a wide range of linguistic resources, methods for processing those resources, methods for accessing new resources and small applications to give a user-friendly interface for various features. In this context “resources” mean the full text of various books, corpora(large collections of text which have been marked up to varying degrees with grammatical and other data) and lexicons (dictionaries and the like).

Natural Language Processing is didactic, it is intended as a text for undergraduates with extensive exercises at the end of each chapter. As well as teaching the fundamentals of natural language processing it also seeks to teach readers Python. I found this second theme quite useful, I’ve been programming in Python for quite some time but my default style is FORTRANIC. The authors are a little scornful of this approach, they present some code I would have been entirely happy to write and describe it as little better than machine code! Their presentation of Python starts with list comprehensions which is unconventional, but goes on to cover the language more widely.

The natural language processing side of the book progresses from the smallest language structures (the structure of words), to part of speech labeling, phrases to sentences and ultimately deriving logical statements from natural language.

Perhaps surprisingly tokenization and segmentation, the process of dividing text into words and sentences respectively is not trivial. For example acronyms may contain full stops which are not sentence terminators. Less surprisingly part of speech (POS) tagging (i.e. as verb, noun, adjective etc) is more complex since words become different parts of speech in different contexts. Even experts sometimes struggle with parts of speech labeling. The process of chunking – identifying noun and verb phrases is of a similar character.

Both chunking and part of speech labeling are tasks which can be handled by machine learning. The zero order POS labeller assumes everything is a noun, the next simplest method is a simple majority voting one which takes the POS tag for previous word(s) and assumes the most frequent tag for the current word based on an already labelled body of text. Beyond this are the machine learning algorithms which take feature sets, including the tags of neighbouring words, to provide a best estimate of the tag for the word of interest. These algorithms include Bayesian classifiers, decision trees and the like, as discussed in Machine Learning in Action which I have previously reviewed. Natural Language Processing covers these topics fairly briefly but provides pointers to take things further, in particular highlighting that for performance reasons one may use external libraries from the Natural Language Toolkit library.

The final few chapters on context free grammars exceeded the limits of my understanding for casual reading, although the toy example of using grammars to translate natural language queries to SQL clarified the intention of these grammars for me. The book also provides pointers to additional material, and to where the limits of the field of natural language processing lie.

I enjoyed this book and recommend it, it’s well written with a style which is just the right level of formality. I read it on the train so didn’t try out as many of the code examples as I would have liked – more of this in future. You don’t have to buy this book, it is available online in its entirety but I think it is well worth the money.