lail2018

You can write a conference paper and participate in some local  student-friendly conferences . That way you'll be better prepared to apply to a Phd program next year.  
 
 

I'll add my own here, for additional hope. My overall GPA is 3.01 and my Phil GPA 3.59 (I got a D+ once upon a time and a few B-range grades). I was accepted to BC's MA program. What helped was having a decent sample, doing okay on the GRE, but most of all having a few very enthusiastic letter's, one of which did address the grades. I also made efforts to raise my GPA in my last two semesters, leaving with a 4.0 in philosophy classes in my last semester. 

If you have a low GPA, there is still hope. Form positive relationships with a few philosophy professors (and hopefully impress them) so you can secure outstanding letters, and write the best possible sample and statement you can. Then apply to many MA programs, because the chances are better there for low-GPA students like myself. Once you get into an MA program (maybe even a lesser-known one) you then have an opportunity to prove you can do graduate level work. Oh! One more thing, in the mean time, if you live in an area with a few colleges, check out their Philosophy departments and ask about auditing classes. That will show determination and also help keep you doing philosophy, and maybe even get you a letter-writer.

 

I know this thread is very old, sorry, I just thought I'd respond in case anyone is reading this and losing all hope because of a low GPA. I have a 2.75 undergrad GPA from a small university with an even smaller Philosophy department, and my major is not Philosophy. So far I've been waitlisted with funding to two pretty good PhD programs and accepted with full funding into a great terminal MA, which I think will give me a springboard to get into a better PhD than I can get into now. So, all hope is not lost if even I can get waitlisted to PhDs

Apologies for jumping in really late here, but you may want to consider applying for a master's degree in computational linguistics before applying for a PhD. (Good) PhD programs in CL are very competitive because of the rapidly increasing interest in the field and having a solid field-specific course background as well as having the opportunity to do original research in a professor's lab would really improve your application chances. I'd say to be competitive you need a solid understanding of most deep learning techniques in NLP (MLP, RNN, CNN, LSTM/Bi-LSTM, Autoencoders, etc.) 

Hi. I went to the same institution, and was in the pretty similar situation as yours. Here is my 0.02$. Unfortunately, many tutors in Oxford care more about their faces than the welfare of their former students. So, what is happening is that, after they wrote your letters for the first time, and you did not do well when their colleague serves as your tutor (for no fault on your part, as I know some of the Oxford courses really suck, especially some the 12 months or 9 months master courses), they feel that they lose faces in front of their colleagues, and there is 0 chance to get letters from these people, even if you have legitimate reasons for dropping out or for not doing well. 
One way you can get out of the dilemma is to apply to some place where references are not needed and try to get good references from this place and and then transfer to a better place that requires 3 references. One place that does not need reference is Australia universities, among which Monash University is notable. Hope this helps.

I found the prep book (specificially the 5 lb book) useful because I had not done any math in the past three years (linguistics major).  And the most recent math I had was calculus, not the algebra/geometry stuff the GRE tests on.  My practice scores before studying were around 153, and actual was 162 for quant (Practice was 150 verbal ahhh, and actual was 165 verbal, 5.5 writing).  The questions were useful for helping me actually remember stuff I had quite literally forgotten how to do/existed/did not recognize.  
I did do a lot of googling when I got questions wrong and re-learning concepts from high school.  I think the manhattan book has the right idea of level of math and general idea of question.  And general format (the A is greater, B is greater, both are equal, cannot be determined format took me a while to mark correctly even after calculating the answer correctly!!).  It helped me prepare for a general standardized-testing environment as well.  But the exact format is not what was on the GRE.  Their questions are good, but not line for line what is on the test.  I do agree that the GRE seemed to test stuff that I hadn't seen at all in the manhattan prep book.  And I did at least 75% of each quant prep section.  Usually 100%. 
One question I remember getting was something you could only get right if you knew how to calculate the interior degrees of a polygon.  Not sure if I worded that right, whatever.  I didn't remember that one stupid formula.  I went back to the question and figured out the dang formula based on square/triangle interior angles.  Glad I had that bit of extra time to figure out a dumb formula I was apparently supposed to memorize!  

I wrote this advice on the Magoosh facebook group and I thought I would post it here as well.  I took the test on August 25, 2017, and received 167Q/160V/4.0AWA.   I was nervous and I think I could have retaken and got a higher quant score but I felt like there isn't enough of a difference to adcoms between 167/170 to justify retaking in my case.  I definitely recommend Magoosh,  I actually tried it and canceled it in March until I realized there wasn't anything as good out there at a similar price (in my opinion).   
My magoosh predicted ranges were Q167-170 & V160-165. I had about 100 verbal questions left and I retook all missed quant questions. I took my first practice test in February, but I didn't start really studying until June. I don't have much verbal advice, but here are the books I used and the tests I took. I used the magoosh 90 day advanced plan from June to August. I did not do all the verbal practice in the plan since my target verbal score was 155. I also bought the Magoosh quant tutoring package, which I recommend. Overall, I feel that no practice tests accurately represent the 2nd hard quant section (for those who do well on the 1st section) beside the powerprep tests. I also recommend that in addition to watching the video explanations for the questions you miss and that you watch the videos for the questions you took longer than the average pace. I would also wait to retake missed problems until the very end of your practice, I remembered the answer to multiple problems. Below are the books and practice tests I used to prepare.
Books
Manhattan Prep 5 LB Book - Highly recommend anyone aiming for a 90%+ percentile on quant to do the last half of every chapter
ETS Official Guide
ETS Quant Practice Problems
ETS Verbal Practice Problems
Manhattan Prep Text Completion and Sentance Equilivance Strategy Guide - The words are very obscure, but it helped me learn how to guess better
Manhattan Prep Word Problems Strategy Guide - More helpful than I would have thought, great if you need more practice outside of Magoosh
Manhattan Prep Quantitative Comparison - also great if you need more QC practice
Nova GRE Math Prep Course - it has questions arranged by topic and difficulty. Contains around 200 hard and very hard problems. There are some typos, but most of the time they are pretty obvious
I did not use the GMAT official guide as suggested in the plan.
I bought the Ready4GRE app, but I don't recommend it.
Practice tests listed in order over 6 months
Kaplan- Q164/V155 (free), Looking back it was too easy
Manhattan Prep 1 (MP) - Q160/V155 In general, the MP quant section was WAY too hard, the verbal was pretty weird but the scores were pretty accurate for me. I felt the quant sections were demoralizing and made me feel like I wasn’t making progress.
MP 2 - Q163/V159
MP 3 - Q160/V160
ETS Paper 1 - Q165/V160
Magoosh Q167/V158
ETS Paper 2 - Q169/V159
Princeton Review - Q164/V162 (free) The second quant section felt accurate, but I felt it was graded too hard (I missed 3 questions total on quant).
Crunch Prep - (free) Q164/V157 The quant was weird, and they advertise that they grade AWA but they didn't grade mine before my trial ended
MP4- Q161/V161
The economist - Quit after the first section of quant, the question format and topics were way different from ETS material. It's the only free test I don't recommend.
MP5 - Q163/V156
Powerprep 1 - Q169/V159 - Quant was easy for me, but I thought the verbal was hard
Powerprep 2- Q166/V163 opposite of PP 1, quant had a few very hard questions

Reposting what I posted in another thread:
 
I graduated from UW CLMS program a few years ago. I had a BA in linguistics and a few CS classes including Data Structures and a probability/statistics class. I completed the MS in one year and did the internship option instead of the thesis.
 
You can complete the program in one year once you start the core classes. A lot of people spent a year taking prereqs in CS and probability/statistics. They're pretty lenient about probability/statistics and will let you self-learn it if you can pass the placement exam. But you pretty much have to take up until Data Structures. You can do this at a community college or at UW. UW has a very well respected CS program and teach Java. You can still take the linguistics and elective CLMS classes in the first year while taking the CS prereqs.
 
As far as the core classes go, it's a lot of programming. There's a programming assignment each week. You're probably best off doing them in Python since it's a simple language and has a lot of NLP libraries. One of the core classes requires Python since you have to use NLTK. Although there's a lot of programming, you aren't held to any standards like you'd be in a CS class. Your code basically just has to run. 
 
As far as after you graduate, there are many routes you can go. A lot of people go on to do NLU/NLP at the big companies like Amazon and Microsoft. A lot of people, including myself, went on to just do straight up Software Engineering. Some other people have gone the more machine learning route. Other people go on to do research.
 
Overall, it's a very diverse field and program. There's about a 50/50 mix of people coming in with linguistics vs CS. There are a lot of group assignments and people can play to their strengths. Even if you're not the strongest programmer, you can team up with someone who can handle more of the technical bulk. They're aware of the fact that people come in with different levels of experience, which is why they're pretty lenient, but they still have some minimum programming standards.

First off, not being told what you want to hear is not the same as not getting a "legitimate response." I think Omniun makes a very good point: how can you want a PhD in a subfield you don't know at all? You need to do your own legwork here, and you're not helping yourself by outsourcing this part of preparing for PhD applications. Go on the websites of a few programs that have good computational linguistics; look up their PhD requirements, look at their course offerings, find out what the faculty's interests and current research is about, and find out what current graduate students and recent alums are working on. That will give you the answers to the questions you asked (and a few you didn't) - how much knowledge in linguistics you'll need to have coming in, and how much you will acquire during your studies; what other knowledge you'll need to have coming in and what you'll learn during the degree; what kind of work is normally done in computational linguistics; and the balance between linguistics and CS in the program.
 
Second, if you don't want my "dick" advice, stop PMing me to solicit it. I don't need to waste my time on people who don't appreciate it (or bother to know my gender, and address me using rude language).