Hey everyone. I am a PhD Student in Computational Linguistics. My main research interest is computational linguistics for under resourced languages. I am particularly interested in computational linguistics for Bantu languages because of the extremely complex morphology that they bring to the table.

I am also doing research in sentiment analysis on stance detection and irony/sarcasm detection. Most of my research is on machine learning approaches.

@bgcarlisle Hey thanks!

Sorry, quick question. Do people talk about non-academic things as well on here or is that generally frowned down on? Nothing inappropriate just cat pictures.

@ksteimel Generally speaking, you want to post "on-topic" stuff as Public and "off-topic" as Unlisted

And there's CW's which are appropriate for politics, FB/Twitter mentions, lewd/nsfw stuff, spoilers for TV shows, or even hiding the punch line of a joke!

@katebowles Hi! Nice to meet you! Yes I'm involved with a lot of different projects as well as my own independent research. Trying to get involved with everything I can before I have to go into dissertation mode. I need to find two disparate topics for qualifiying exams this summer.


Oh cool! Yeah part of how I'm approaching it is by trying to detect shifts in the sentiment of the words used. For example, 'A beautiful spring day \' would have a shift in sentiment from positive at the beginning to negative at the end.

There's also certain words that tend to indicate sarcasm like 'just'.

If you don't mind me asking, what was STEDT working on?

@ksteimel Great to meet you. Do you sit more on the computational or the linguistic side of things?

I am usually on information retrieval / computational text understanding side. Usually English, but I also work with "crosslingual" folks.

I'm more on the linguistics side of things but I do have a huge interest in parallelizing NLP algorithms.

Oh awesome! It's great to meet someone else in my field.

What do you do for text understanding?

I had a class last semester where we took dependency parses and extracted semantic relations to put in a graph dB but I don't know if this is actually text understanding or really what text understanding entails

@ksteimel This is an "accepted approach" but I disagree.

I run a shared task on Complex Answer Retrieval. Given a search query, generate a Wikipedia page that tells you everything about it. http//

(Sorry, website needs updating)

The manual evaluation step must take a while. This looks like a very cool albeit difficult shared task.

Thank you for showing me this!

@ksteimel Well, you can use all of WIkipedia to train and do a first step evaluation

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