Introduction to Natural Language Processing (slideshow)

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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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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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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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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Extracting sentiment from healthcare survey data

A short while ago I posted the slides to Despo Georgiou’s talk at the London Text Analytics meetup on Sentiment analysis: a comparison of four tools. Despo completed an internship at UXLabs in 2013-4, and I’m pleased to say that the paper we wrote documenting that work is due to be presented and published at the Science and Information Conference 2015, in London. The paper is co-authored with my IRSG colleague Andy MacFarlane and is available as a pdf, with the abstract appended below.

As always, comments and feedback welcome 🙂

ABSTRACT

Sentiment analysis is an emerging discipline with many analytical tools available. This project aimed to examine a number of tools regarding their suitability for healthcare data. A comparison between commercial and non-commercial tools was made using responses from an online survey which evaluated design changes made to a clinical information service. The commercial tools were Semantria and TheySay and the non-commercial tools were WEKA and Google Prediction API. Different approaches were followed for each tool to determine the polarity of each response (i.e. positive, negative or neutral). Overall, the non-commercial tools outperformed their commercial counterparts. However, due to the different features offered by the tools, specific recommendations are made for each. In addition, single-sentence responses were tested in isolation to determine the extent to which they more clearly express a single polarity. Further work can be done to establish the relationship between single-sentence responses and the sentiment they express.

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Sentiment analysis: a comparison of four tools

Diana Maynard entertains the troops

Last week I had the privilege of organising the 13^th 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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MeetUp review: AnnoMarket – text analytics in the cloud

Valentin Tablan kicks things off (photo: Hercules Fisherman)

After a brief hiatus I’m pleased to say the London Text Analytics meetup resumed last night with an excellent set of talks from the participants in the AnnoMarket project. For those of you unfamiliar, this project is concerned with creating a cloud-based, open market for text analytics applications: a kind of NLP ‘app store’, if you will. The caveat is that each app must be implemented as a GATE pipeline and conform to their packaging constraints, but as we’ve discussed before, GATE is a pretty flexible platform that integrates well with 3^rd party applications and services.

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Cloud-based text annotation (text analytics meetup)

Just a quick heads up that the London Text Analytics group is meeting on Weds Feb 12 to hear about AnnoMarket: a cloud-based text annotation marketplace. There’ll be complimentary drinks and nibbles, courtesy of our sponsors (the AnnoMarket project). We’ll be meeting at the Plough on Museum Street from 18:30. Further details are appended below, but if you’d like to join us just sign up on the event webpage. See you there!

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Sentiment analysis tools for non-coders?

I have an intern who will shortly be starting a project to extract sentiment from free text survey responses from the healthcare domain. She doesn’t have much programming experience, so is ideally looking for a toolkit /platform that will allow her to experiment with various approaches with minimal coding (e.g. perhaps just some elementary scripting etc.).

Free is best, although a commercial product on a trial basis might work. Any suggestions?

Related Posts:

1. How do you compare two text classifiers?
2. Text Analytics Summit Europe – highlights and reflections
3. How do you measure site search quality?
4. Prostitutes Appeal to Pope: Text Analytics applied to Search
5. The role of Natural Language Processing in Information Retrieval

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