Posted in academia, programming

Trying to take up a coding mindset (as a linguist)

Blog_coding_20150625
I originally tweeted this photo on the morning when my second R book by Stefan Gries arrived at the same time as the Bloomberg code issue… was that a sign? I haven’t gotten around to starting through with the book yet though, as I am still working through the first one!
I am currently attempting to learn something about the programming language R. Why? Is that even a good idea?

At a few points during the past few years I have considered whether I chose  the wrong degree(s). My BA degree was called “English Studies for the Professions (BAESP)” and I really enjoyed it and found everything interesting. At the same time I wanted to get more involved with research and see how linguistics can get really useful. So I moved on to an MA in Applied Linguistics and finally a PhD in the same field. I am really interested in linguistics and think it is a worthwhile area. BUT at times I wonder “Why didn’t I study computational linguistics?” Since my research deals with corpus linguistics this is actually not so far of a stretch. The problem is that I don’t seem have a computational mindset… So far the only type of computational stuff that I can more or less deal with is interactive. During the MA we did some work with the statistical package SPSS which used to be command-driven but now has an interactive interface. For corpus linguistic analyses I have used WordSmith Tools, AntConc and SketchEngine, which are all more or less user-friendly. If anything I get confused by too many buttons and settings that are offered.

When and how did I decide to do something about my non-computational situation?
I have been playing with the thought of getting a little bit more tech-savvy (and at the same time brush up on my understanding of statistics) for a year or so. Throughout my studies I have simply come across so many studies where people do more interesting stuff than I seem to be able to do because I don’t know how to make something like that happen. An example is a Twitter study that I already quoted in my BA project (which was also about Twitter). For my own project I used an online tool (at the time it was called TAGS v3 now there is TAGS v6) to collect a limited number of Tweets, leading to a small corpus. Michelle Zappavigna (@SMLinguist), in her book Discourse of Twitter and Social Media, however, had access to the infrastructure and support necessary for downloading and compiling a large Twitter corpus containing over 100 million Tweets. She used a Python script and the Twitter API. At that time I thought that I’m never going to be able to either do this myself or have the required technical support. While I still don’t know how to do this my attitude has changed slightly. I’m lucky to be cooperating with people from statistics and programming for a project coordinated by my supervisor. This regular interdisciplinary contact has taught me there are things that seem infinitely difficult to me but can easily be done by others in a short amount of time with a few lines of code. Moreover, the cooperation is gradually showing what kind of things are actually possible with programming. In the meantime I have been wondering whether or not it is worth investing time and energy (and money I guess) for learning some baby steps in programming when there are so many experts out there? Well, I don’t know, but I am trying to regain some control over my work…

Here are some interesting view points on coders and coding expressed by Paul Ford in that recent Bloomberg code issue:

Coders are people who are willing to work backward to that key press. It takes a certain temperament to page through standards documents, manuals, and documentation and read things like “data fields are transmitted least significant bit first” in the interest of understanding why, when you expected “ü,” you keep getting “?”

[Paul Ford, What Is Code?, Bloomberg Special Double Issue June 15-28, 2015, print p. 24 (digital – free & with really cool animated visualisations! – Section 2.1)]

Regarding the question whether or not to learn coding, Ford says:

There’s likely to be work. But it’s a global industry, and there are thousands of people in India with great degrees. […] I’m happy to have lived through the greatest capital expansion in history, an era in which the entirety of our species began to speak, awkwardly, in digital abstractions, as venture capitalists waddle around like mama birds, dropping blog posts and seed rounds into the mouths of waiting baby bird developers, all of them certain they will grow up to be billionaires. It’s a comedy of ego, made possible by logic gates. I am not smart enough to be rich, but I’m always entertained. I hope you will be, too. Hello, world!

[print pp. 109-112, digital Section 7.5]

Personally, I don’t think I can now start to become ‘a real coder’ and ‘compete’ with all those computer science graduates and other professional coders. BUT, the whole thing seems fascinating and if I know a little bit some light might be shed on so many areas that are still dark for me.

Why R?
I saw info about the ‘Regression modelling for corpus linguistics‘ workshop by the linguist Stefan Gries (held in Lancaster, 20 July) and knew about his books (Quantitative Corpus Linguistics with R – QCLWR – and Statistics for Linguistics with R) so I finally decided to buy them. That’s really the main point for me. [By the way, in the book, Gries argues that R is particularly well-suited for corpus linguistics…] While I know other resources are available, such as MOOCs (I even attempted a MOOC on R but dropped out), I need to see something that’s relevant to my own research (the R MOOC I attempted used data from biology, I believe). Having said that the MOOC introduced a neat little learning environment called ‘Swirl‘ which allows you to “learn R, in R”. I might go back to that at some point. Actually, it’s even hard for me to get through the first 100 pages of Gries’ QCLWR because it’s about the basics with few linguistic applications. But I try to motivate myself to continue by flipping beyond the 100 pages now and then because  I can see that soon I’ll be soon (hopefully) able to apply those basics to linguistic problems (I’m almost at page 96  now – yay!). So if someone had made a book about Python for corpus linguistics (is there one?), I might have gone for that, because I didn’t really know anything about which language is best to know. However, I am looking forward to a session at the Nottingham Summer School in Corpus Linguistics entitled ‘Essential python for corpus linguists’ run by Johan de Joode.

My main problems so far
Unfortunately, I am still lacking the coding mindset, but I hope that will change after working through the second, more applied linguistics part of QCLWR. I haven’t done proper math since high school and this step-wise logical thinking about embedding logical/ regular expressions and loops and variables and whatnot all feels a bit foreign to me. More often than not I can’t follow the examples at first sight (usually because I have missed a parenthesis somewhere…). Just have a look at an example of the lines that I have been trying to work through… (Gries, 2009: 89):

gsub(“(\\w+?)(\\W+\\w*?)\\1(\\W)”, “\\1\\2\\1\\3”, text, perl=T)

Trying to keep track of everything that could be potentially useful in my copy of QCLWR with sticky tags.
Trying to keep track of everything that could be potentially useful in my copy of QCLWR with sticky tags.

I also have difficulties with remembering function names and their argument structures and, worse still, I can’t really follow the R/ RStudio help entries about the functions. The biggest problem is that it takes me ages to go through the tutorial in Gries’ QCLWR. There are still more than a hundred pages left including masses of exercises and assignments and the second book (Statistics for linguists with R) is still waiting for me… Obviously this is not even the only task I’m supposed to be doing for my PhD at the moment…
On the bright side, though, I am slowly starting to feel more comfortable staring at condensed strings of digits and characters and slowly picking up the ability to analyse a command string step by step. Once something does work it really delights me.

What are your experiences with starting to code? Do you think it’s worthwhile to invest in these skills? Which programming language are you learning and why? [And sorry for turning this into such a long post…]

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Posted in academia, academic writing

Nail your colours to the mast!

Blog_Nail-your-colours_20140614 

“Nail your colours to the mast”

Meaning: “To defiantly display one’s opinions and beliefs. Also, to show one’s intention to hold on to those beliefs until the end” (phrases.org.co.uk).

This blog post is inspired by my recent first year confirmation review. The review was actually a positive, encouraging and also refreshing experience. I received numerous pieces of valuable advice. One point, though, stuck with me most and this was the motto “nail your colours to the mast”. This seemed to be the examiner’s main concern (and I shamelessly quote his saying about the colours here). What’s the actual theoretical approach that the project is based on?

Blog_Terminology_20150613As the author of the report, having spent like half a year on it, I can testify that this has really been the main struggle. In writing my literature review I had spent plenty of time on locating different positions in the literature and identifying potential differences between the different approaches. This was at times a frustrating endeavour, as ever so similar terms were used for frameworks with only very subtle differences. Coping with this diverse, overlapping terminology has been a key issue. Mapping out the different terms and their usage had already cost quite a bit of energy. I understand that I fell short on the next step – evaluating them and picking one for my work or even making up another term (and of course justifying it!).

Whose side am I on? To me as a first year PhD student it is just a scary thought to have to make such a decision. If I take up a specific term (such as, in my case, ‘corpus-assisted discourse analysis’ rather than ‘corpus-based/driven’), do I need to then follow the scholars associated with this terms for the rest of my work? Will I contradict myself if I choose one term and at a later stage take a turn with my work that doesn’t really harmonise with the work of the related people? How do I know the implications? Due to some of these daunting questions, I attempted to stay on ‘friendly terms’ with various approaches. This, however, is problematic in itself.

Is it possible to decide on a different set of colours at a later point? (The definition for the colour metaphor quoted above seems to suggest this, but maybe in academia we can allow for more flexibility? Clearly we’re all evolving and learning?) The saying was new to me, but in the context of the confirmation review I understood what was meant – clearly express what you’re trying to achieve and how you are doing that. I think I did that fairly well in my methods section (although at times it needs simplification as I was told) – in the parts that are very practically oriented. I have noticed that I struggle more with attempting to explain the theoretical implications of my work. And I believe this problem is routed in the fear that I misuse theoretical claims, for instance by combining incompatible approaches.

At the end of the review meeting both my examiner and supervisor emphasised, however, that it is okay (and probably even good!) to keep an open mind throughout the PhD and refine your theoretical standpoint through continuous writing. That made me feel more relieved. Still, I have had to go back to more reading on the terms I wasn’t sure about (this includes the definition of ‘discourse’ – a real can of worms…).

What are your views on this topic? Have you encountered similar difficulties?

Posted in academia, careers

PhD – career in academia?

Mybooks

Whether or not to seek a job in academia is probably a question that most if not all PhD students will consider at some point. I imagine that many actually start their PhD with the motivation to work in academia (whether this motivation stays is probably another question). I’m choosing this career topic today, because I just attended a related training session at the University of Nottingham: ‘Academic careers in Higher Education’. This panel session, hosted by @UoNgradschool, featured four academics and was specifically organised for arts and social science graduates.

The course had the following objectives:

“By the end of the session you will:
1. have an understanding of possible modes of entry into academia
2. know what your next step should be if you are considering working in this sector
3. have had the opportunity to ask questions of professionals working in the sector”

The academics from across the Faculties of Arts and Social Science (Dr Sarah Davison, Dr Andrew FisherDr Cathy Johnson and Dr Andrew Mumford) all shared their interesting career paths in academia along with their top dos and don’ts for PhD students seeking an academic job. What I really liked about the event was the personal touch as it seems that there’s never the one and only way to do something. However, some patterns emerged and I have summarised the main points that I gained from the session in the diagram below.

Paths from PhD to academic career (my summary of the panels' discussion)
Typical paths from PhD into academia (my summary of the panel’s discussion, specifically inspired by Dr Mumford’s comments on the temporary lectureship vs. post-doc routes)
The Hong Kong Polytechnic University
The Hong Kong Polytechnic University

Personally, as I am just about to finish my first PhD year (confirmation review is tomorrow… oO), I don’t have any clear plan yet as to which of these routes I will take. However, I am quite sure that I would like to attempt an academic career. In fact, I signed up for the panel session today because I enjoy working in an academic environment and would like to stay in this sector. Having completed my undergraduate at Hong Kong PolyU and my MA as well as the first year of my PhD at the University of Nottingham, I can say that I have felt very comfortable in both of these institutions.

I am also conscious that I am now at the first stage of the diagram, the PhD, and that means that I have to work on meeting requirements listed in ‘person specifications’ of potential future jobs. (The panelists emphasised throughout how important it is to stay on top of what’s required by the market – many of them still regularly check jobs.ac.uk! That’s a habit I need to start.) So now, in the first stage, I have to do the extra stuff. Luckily, I have had the chance to be involved in organising last year’s ICAME 35 conference in Nottingham and am now in the process of co-organising the Nottingham Summer School in Corpus Linguistics as well as our Symposium ‘Corpus Linguistics beyond Boundaries’. I’m very grateful for these opportunities and feel that they help me learn more about the field, procedures of admin work and of course myself. I have just started attending conferences as a participant as well and that’s something I have to further work on. Right now I am still a bit nervous about publications and haven’t submitted anything yet, but that will hopefully change in this coming PhD year. I am also hoping for the opportunity to do part-time undergraduate teaching during the second year of my PhD, because I think teaching is an important aspect of academia. As the panelists pointed out today, the chance of getting a post-doc position though extremely attractive is rather unlikely in the current funding situation. Therefore, a teaching position (with possibly some research elements) seems the most likely job opportunity after the PhD…

What are your thoughts or experiences regarding the academic career path?

University of Nottingham Photo credits: @HunterZhou
University of Nottingham
Photo by my friend @HunterZhou

Posted in Misc

A little introduction to myself and this blog

Hello there.

As you may have gathered from somewhere on this page my name is Viola. I am still a first year PhD student (although the end of year is approaching…) and a slightly longer paragraph about myself can be found on my profile. Here is a recent photo of me at ICAME 36 two weeks ago, where I had the chance to thank the organisers for the wonderful conference. (Many thanks to Sebastian Hoffmann for this shot – more of his photos from the conference are shown in this gallery).

ICAME 36 Conference,  Trier, Germany, May 2015
ICAME 36 Conference,
Trier, Germany, May 2015

Ever since I started my PhD in October 2014 the idea of starting a blog has been popping up at various trainings and it’s something I’ve observed others do very well. I like the notion of documenting the ‘PhD journey’. During my PhD life I have already started becoming a lot more active on Twitter – follow me at @violawiegand. I am excited that many people in ‘my’ academic field (here I mainly mean corpus linguistics – see the corpus linguistics Twitter list I compiled) are sort of permanently on Twitter, often giving me new ideas and just showing me what developments are out there. At the same time I also enjoy viewing encouraging posts on writing advice and other aspects of grad/PhD/academic life as documented in my ‘writing’ Twitter list. I hope to somehow join these two crowds with this blog.

So what’s the blog going to be about?

I’m still considering that but it will probably document what I’m concerned with as my PhD continues. My topics of interest include:

  • corpus linguistics
  • increasingly also more computational topics – now learning to work with R 🙂
  • discourse analysis
  • surveillance studies
  • academic writing

I also welcome feedback from more experienced bloggers regarding what’s best to focus on in a PhD blog and what’s perhaps rather boring for all of you to read. Anyhow, I’m excited about this step and hope that the blog will interest some of you and provide myself with a fresh view on my work.