If you are interested in studying Linguistic Data Science at LDSL at RUB, you can test the fit here. The test is structured into different topics you should consider when applying for a place in the Master's program. While the result itself will be private for you, you will need the confirmation that you took the test for your application that appears after sending the test. (For your application, you should screenshot the confirmation or simply note down the confirmation code.)
The test should take you between 30 and 90 minutes (time will not be measured).
Keeping in mind that the programm deals with Linguistic Data Science, you should of course have a foundation in Linguistics.
If you are completly new to Linguistics, you should try to go with your intuition and feel free to google concepts - we don't expect everyone to be familiar with every theory of every subfield, but being able to grasp concepts quickly will help with your studies.
Which of the following statements is true with regard to the given tree? (Basic)
Which of the following sentences contains a syntactic ambiguity? (Basic)
Which of the following phrases is not a complete constituent? (Basic)
Icelandic is assumed to have quirky subjects, i.e. the subjects of some predicates don’t appear in the nominative (All examples and their glosses in this question and the following are taken from (Sigurðsson, 1991). N, A, D, and G stand for nominative, accusative, dative, and genitive respectively):
Hana/Hún vantaði vinnu.
her(A)/(N) lacked job
She lacked a job.
Henni/Hún leiddist.
her(D)/she bored
She was bored.
Hennar/Hún var getið.
her(G)/she was mentioned
She was mentioned (by someone).
Take a look at the following sentences and their glosses and pay special attention to the forms of the floating quantifier allir (“all”), which are the only ones that can be used in the respective examples:
Strákarnir komust allir í skóla.
the boys(N) got all (Npl.m.) to school
The boys all managed to get to school.
Strákana vantaði alla í skólann.
the boys(A) lacked all (Apl.m.) in the school
The boys were all absent from school.
Strákunum leiddist öllum í skóla.
the boys(D) bored all (Dpl.m.) in school
The boys were all bored in school.
Strákanna var allra getið í ræðunni.
the boys(G) was all (Gpl.m.) mentioned in the speech
The boys were all mentioned in the speech.
Now compare the following examples (slightly modified from Sigurðsson (1991)):
Strákarnir vonast til [að komast allir í skóla].
the boys(N) hope for to get all (N) to schoo
Strákarnir vonast til [að vanta ekki alla í skólann].
the boys(N) hope for to lack not all (A) in the school
Strákarnir vonast til [að leiðast ekki öllum í skóla].
the boys(N) hope for to bore not all (D) in school
Strákarnir vonast til [að verða allra getið í ræðunni.
the boys(N) hope for to be all (G) mentioned in the speech
If we assume that the bracketed non-finite clauses contain a covert pronoun, we can take the comparison to indicate that …:
For the following questions: Choose the head of the phrase:
just behind the corner
very big
I love Syntax
the first book by Montague
Which of the following is not a Gricean maxim?
Which of the following pairs of words is an example of proper meronymy?
Which of the following inferences is based on a presupposition?
Which of the following phrases is not a pattern of concatenative morphology?
Which of the following is not a distinctive property of count nouns in English? (specialized)
To be able to use methods from Linguistic Data Science, you should have a firm grasp of the basics of programming and ideally some knowlege of Computational Linguistics, even if that will not be your focus.
If you are completly new to programming and computational linguistics, you should try to go with your intuition and feel free to google concepts - we don't expect everyone to be familiar with every theory of every subfield, but being able to grasp concepts quickly will help with your studies!
Consider the UPOS tag set for part-of-speech tagging: https://universaldependencies.org/u/pos/all.html
What is the appropiate POS-tag for the word such in the following example? In the present study, we examine the outcomes of such a period of no exposure on the neurocognition of L2 grammar.
What does Zipf’s law state?
How to calculate the evaluation metric precision? (TP = True positive, FN = False negative)
When can you leave out commenting your code?
What would you use the Big O notation for?
What is a validation data set?
What is your experience in programming in general?
From never having seen code to having written your own complex program - what is your experience in general?
What is your experience in with Python, especially language packages like NLTK or spaCy and data science tools like pandas?
From never having used it (0) or being familiar in general but not with language processing or statistics (4) to extended experience especially with language processing (7).
What is your experience in programming with R, especially with language related packages like lanuageR, Rling and glmm?
From never having used it (0) or being familiar in general but not with language processing (4) to extended experience especially with language processing (7).
Data Science is one of the core foci you can choose in your studies, but it is also the eponymous foundation for our course. Part of this is logic and linguistic knowledge, but there is also statistics/mathematics and programming.
If you are completly new to data science, you should try to go with your intuition and feel free to google concepts - we don't expect everyone to be familiar with every theory of every subfield, but being able to grasp concepts quickly will help with your studies!
What does it mean if a model "overfits"?
You plan a logistic regression analysis on the following dataset: a total of 38 observations, 4 variables. Generally speaking: Is this dataset suitable for this type of analysis?
You want to visualize a dataset with continuous variables - which visualization method is appropriate?
What is an applicable method to compensate for smaller test and training datasets?
A common problem regarding the evaluation of standard cluster analyses is:
The program for this course relies a lot on your ability to self-organize and in exchange gives you the freedom to really form your own focus and develop your abilities. You need the ability to handle this freedom to your advantage. While your lecturerrs will guide you in this for specific aspects of forming your academic career and your abilities, the general idea is that you have to take the offer yourself.
Remember, we are happy to invite you and won't see the results of this test - this is just for you. Especially if this would be your first times studying on your own (or even living on your own) outside of a scheduled program, consider the following questions to reflect if this is the right choice for you.
What do you like better?
left - I enjoy always knowing the answer immediately from learning by heart.
right - I enjoy looking at a complex problem from different methodological angles.
What would your ideal course of studies look like?
left - Having a clear curriculum where I know beforehand exactly what I will do each semester and what I have to learn.
right - I like picking courses myself to create my own profile and add research into topics I choose myself.
What would your ideal course of studies look like?
left - I like showing that I dilligently learned everything I was shown and repeat it in an exam or test.
right - I like showing that I understood what I was taught and applying it in a paper or project.
How do you learn better?
left - Learning things by heart that have been listed.
right - Understanding concepts from learning from different sources by myself.
What kind of classes do you prefer?
left - I like lectures in which my main task is listening and remembering.
right - I like learning in groups where I have to be active in developing the results myself.
What would you rather be?
left - An expert in one clear cut field who is approached by others to solve specific problems.
right - A connector between fields who helps several experts to solve complex problems.
Howpatient are you with work that takes diligence?
left - I am rather a rough cut kind of person and want to look at the big picture.
right - If necessary, I can spend hours combing through data to annotate or clear it up.