A scalable approach to legal question answering

Recently I’ve had the privilege of working with colleagues at Lexis Nexis on a variety of projects in the area of artificial intelligence and natural language processing. So I am pleased to share with you the following paper, which has been accepted for presentation at the 16th International Conference on Artificial Intelligence and Law in London next week. It’s co-authored with colleagues Zach Bennett and Kate Farmer.

We’ll be presenting this as part of the demo session on Tuesday afternoon. The paper is just two pages long so it’s quite concise, but we are hoping to submit an extended version to a suitable conference or workshop in due course. In the meantime, comments and feedback welcome 🙂

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Book review: Deep Text by Tom Reamy

When I started the London Text Analytics meetup group some seven years ago, ‘text analytics’ was a term used by few, and understood by even fewer. Apart from a handful of enthusiasts and academics (who preferred the label of “natural language processing” anyway), the field was either overlooked or ignored by most people. Even the advent of “big data” – of which the vast majority was unstructured – did little to change perceptions.

But now, in these days of chatbot-fuelled AI mania, it seems everyone wants to be part of the action. The commercialisation and democratisation of hitherto academic subjects such as AI and machine learning have highlighted a need for practical skills that focus explicitly on the management of unstructured data. Career opportunities have inevitably followed, with job adverts now calling directly for skills in natural language processing and text mining. So the publication of Tom Reamy’s book  “Deep Text: Using Text Analytics to Conquer Information Overload, Get Real Value from Social Media, and Add Bigger Text to Big Data” is indeed well timed.

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A framework for chatbot evaluation

Unless you’ve been on another planet for the last year or so, you‘ll almost certainly have noticed that chatbots (and conversational agents in general) became quite popular during the course of 2016. It seems that every day a new start up or bot framework was launched, no doubt fuelled at least in part by a growth in the application of data science to language data, combined with a growing awareness in machine learning and AI techniques more generally. So it’s not surprising that we now see on a daily basis all manner of commentary on various aspects of chatbots, from marketing to design, development, commercialisation, etc.

But one topic that doesn’t seem to have received quite as much attention is that of evaluation. It seems that in our collective haste to join the chatbot party, we risk overlooking a key question: how do we know when the efforts we have invested in design and development have actually succeeded? What kind of metrics should be applied, and what constitutes success for a chatbot anyway?

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London Text Analytics: call for venues and speakers

After a brief hiatus, I’m pleased to say that we will shortly be relaunching the London Text Analytics meetup. As many of you know, in the recent past we have organized some relatively large and ambitious events at a variety of locations. But we have struggled to find a regular venue, and as a result have had difficulty in maintaining a scheduled programme of events.

What we really need is a venue we can use on a more regular schedule, ideally on an ex-gratia basis. It doesn’t have to be huge – in fact; a programme of smaller (but more frequent) meetups is in many ways preferable to a handful of big gatherings.

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UXLabs ‘Internships’ for 2016

Here’s a sample of some of the things we’re working on at UXLabs this year, neatly packaged into Masters level ‘internships’. I use quotes there as it’s a convenient term used by many of my academic colleagues, but these opportunities are (a) unpaid and (b) remote (i.e. hosted by your own institution). So perhaps ‘co-supervised MSc projects initiated by a commercial partner’ is more accurate term… Anyway, what we offer is support, expertise, supervision and access to real world data/challenges. If you are interested in working with us on the challenges below, get in touch.

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Text Mining and Visualization

I received a pleasant surprise in the post today: my personal copy of Text Mining and Visualization: Case Studies Using Open-Source Tools, edited by Markus Hofmann and Andrew Chisholm. Now I don’t normally blog about books, since as editor of Informer there was a time when I would be sent all manner of titles for inspection and review. But I’ll make an exception here. This is partially since Chapter 7 is my own contribution (on mining search logs), as discussed in my earlier blog posts. This is complemented by 11 other chapters, covering a variety of topics organised into four sections:

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UXLabs ‘Internships’ for 2015

Here’s a sample of some of the things we’re working on at UXLabs this year, neatly packaged into Masters level ‘internships’. I use quotes there as although it’s a convenient term used by many of my academic colleagues, these opportunities are (a) unpaid and (b) remote (i.e. hosted by your own institution). So maybe ‘co-supervised MSc projects initiated by a commercial partner’ is more accurate term… Anyway, what we offer is support, expertise, co-supervision and access to real world data/challenges. If you are interested in working with us on the challenges below, get in touch.

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