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Posts Tagged ‘NLP’

I am currently hiring for the following position. If you know of anyone suitable, please encourage them to apply!

Research Associate in the field of information retrieval / user experience (0.5 FTE)

This role is part of a Google-funded research project that aims to use AI (Artificial Intelligence) and data visualization to facilitate more efficient and effective approaches to information retrieval through the development of alternative approaches to search strategy formulation. This has the potential to minimize error and inefficiency in scientific research and facilitate more efficient and effective research workflows for the broader scientific community.

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I am recruiting sponsored or self-funded PhD students who wish to undertake projects in natural language processing and UX with focus on information retrieval, including the projects listed below.

Note that these topics are based on MSc level project proposals, but most have the scope and ambition to be scalable to PhD level work. Moreover, they are merely ideas at this stage, so feel free to adapt / enhance them to accommodate your own ideas and interests. Note also that this list is not exhaustive: we have other project ideas and proposals which aren’t quite ready for public dissemination.

If you are a self-funded student considering a PhD in any of the topics below please take a look at the further information and/or email me to discuss.

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I am currently starting work on developing an undergraduate module in Natural Language Processing (level 6, 3rd year). Although I have been involved in the field of NLP for many years, recent times have witnessed a transformation of the field, not just in terms of its academic foundations, but also its practical application in industry and its attractiveness as a fulfilling and rewarding career choice. My sense is that some of the topics which I originally studied for my doctorate retain their appeal since the key ideas remain relevant despite radical changes in the implementation. However, others are more hostage to the technological fortunes of deep learning and other neural/distributional approaches.

My view is that field benefits by being informed by more than one perspective: computer/data science may be a given, but cognitive science, information science and linguistics all have their contributions to make. Clearly, it is a tricky task to pack all this into just 10 topics, and to do so from both a theoretical and practical perspective. Here is my current thinking:

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Earlier this week I gave a talk called “Introduction to NLP” as part of a class I am currently teaching at the University of Notre Dame. This is an update of a talk I originally gave in 2010, whilst working for Endeca. I had intended to make a wholesale update to all the slides, but noticed that one of them was worth keeping verbatim: a snapshot of the state of the art back then (see slide 38). Less than a decade has passed since then (that’s a short time to me 🙂 but there are some interesting and noticeable changes. For example, there is no word2vec, GloVe or fastText, or any of the neurally-inspired distributed representations and frameworks that are now so popular (let alone BERT, ELMo & the latest wave). Also no mention of sentiment analysis: maybe that was an oversight on my part, but I rather think that what we perceive as a commodity technology now was just not sufficiently mainstream back then.

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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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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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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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textmining

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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Diana Maynard entertains the masses

Diana Maynard entertains the troops

Last week I had the privilege of organising the 13th meeting of the London Text Analytics group, which featured two excellent speakers: Despo Georgiou of Atos SE and Diana Maynard of Sheffield University. Despo’s talk described her internship at UXLabs where she compared a number of tools for analysing free-text survey responses (namely TheySay, Semantria, Google Prediction API and Weka). Diana’s talk focused on sentiment analysis applied to social media, and entertained the 70+ audience with all manner of insights based on her expertise of having worked on the topic for longer than just about anyone I know. Well done to both speakers!

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