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hi there! i’m a high schooler and i’m seriously considering majoring in computer science when the time comes. do you think you could maybe talk a little bit about what it’s like studying comp sci? if you can. i hope you’re having a great day
hope you’re having a great day too!
What my first year of studying computer science was like
obligatory preface that courses differ between universities and colleges. i study in australia.
The biggest challenge I faced per se going into comp sci was how nothing I did at school prepared me for it. The kind of content I’ve learnt wasn’t similiar to school and the assignments have been very different. No analytical essays and scientific reports for me (there has been essays and reports tho). In my school IT class I learnt basic HTML and CSS which was the only programming knowledge I went into comp sci with. (Id also gotten pretty far on khan academy’s JavaScript course in like year ten but I’ve forgotten all of that now lol).
I have four units a semester and have completed my first year (of 3, although I’m taking less units this year so I’ll finish in 3.5 years).
What I actually did in my first year:
Semester 1 (pre-major picking, two of these were for the other major option)
Learnt general computer hardware and how it functions. The assignment was using a raspberry pi to do something that used hardware (the pi), software (basic code) and the internet (I used twitter). My project is on Twitter at SunsetIFB102
Group project app design stages. Like drawing the layouts, getting feedback, then digital sketches then semi fancy looking sketches (not comp sci major)
Python! I really liked this. Taught the basics of python and in general programming. It was kind of intimidating how many people had coded before so I had to focus on ignoring them and learning for myself. Also really salty I was 1% off a high distinction for the whole unit AGH
Databases and SQL. This would’ve been a useful unit if the lecturer was, how do I put this kindly, good at his job. (Not comp sci major, although definitely use databases later on)
Semester two (all comp sci major)
C# , basics of object oriented programming principles. Which is a fancy way of saying it taught how you should write your code so it’s laid out effectively.
C, how to program microprocessors. Basically how to program hardware machines. C is mostly used for machines like ATMs, a fridge perhaps, probably a roomba and other single purpose kind of things. Also from this I can program arduinos and read arduino code easier since arduino code is a C/C++ hybrid. (This and the previous unit had high fail rates and were honestly Tough)
Information security (apart of network security minor) This unit talked about processes of protecting information in organisations and on a single person level. Basics of cryptography was discussed and hashes and how ways information is kept secure when transferring between objects (like over the internet)
Computation mathematics (apart of intelligent systems minor). This was a weird unit to me and was mostly math majors. It taught all these different equations which allow you to make approximations. Honestly confused how it’s used for computers but we learnt MATLAB and it’s a pre requisite for an intro to robotics unit I’m taking this year :)
So yeah, that’s an account of what I actually studied. Reading over it it seems way more impressive than it probably was. Computer science isn’t easy (well, unless you want to scrape a pass each time) but it isn’t horribly difficult. It’s how different it is to high school work that can trip you up. I can’t make aesthetic studyblr notes on paper because its all on my computer and can’t turn it off when studying because I need it.
My assignments are typically big coding projects, exams (multiple choice if a coding unit) and essays if it deals with theory (like info sec did).
This coming semester I’m learning Java and GUI’s (graphical user interfaces), JavaScript and HTML and whatever else for web applications, and discrete maths which seems to be for notation used later on... it looks very strange.
Hope this helps! If you have any more questions feel free to DM me!
Also if anyone else wants to add their experiences, feel free to reblog with your tips.
Design parsing methods to extract information from web pages and answer the following questions:
a) Assuming that you live in Toa Payoh town and owns a car. Hence, you are interested to find out what how many carparks are there available in Toa Payoh. Using the data information available, construct a Python program to count the total number of carparks in “Toa Payoh” and the total number of car parks decks available.
b) Assuming that you are driving home to Toa Payoh and hence you are interested to know which of the car parks will provide you the highest chance to getting a lot. Construct another Python program that enable you to check and display in real-time what is the availability of the carparks in Toa Payoh.
c) Based on the results in (a) and (b), how can you improve your program(s) and tabulate your chances of getting a lot in Toa Payoh. You should rank it from highest to lowest chance.
Solution:-
Introduction:
The main aim of this project is to develop a real time avaibility of Singapore's carpark information which can be implemented in Jupyter Python Notebook. The the MySQL database is designed using AWS on the RDS data services. Thus, the analysis of a real time car details will be investigated.Read More
https://www.allhomeworkassignments.com/programming-subjects/python-assignment-help.html Identifies Features That Make Python Unique
With significant measurement in TIOBE programming community index, Python is ranked among the top eight programming languages
It is counted as the third most popular language whose grammatical syntax is not based on the conventional C++, C# and JAVA programming language. Python is highly influenced by C programming language and hence the statement syntax and expressions in C helps the programmer to transit between the two languages. On evaluating the empirical study, Python (scripting languages) are considered to be more productive than C and Java (conventional language).
Students seeking Python programming assignment help, connect https://www.allhomeworkassignments.com/programming-subjects/python-assignment-help.html.
as with literally everything i write, this got really fucking long! like, wordcounter.net estimates this will take 7 minutes to read. so i’ve placed the bulk of this post under a read more
this is not a quick tips kind of post; this is a detailed breakdown of how to write a resume from scratch, with examples that are largely taken from my own resume. this is primarily a resource for people who don’t know where to start with writing a resume, not for people who just want resume hacks
i’m saying all this so i don’t get people in my inbox complaining about how long this is. writing a resume takes a lot of time and effort, and this post does not shy away from that
creating a resume will take you a while, especially if this is your first attempt. don’t be discouraged! take breaks, and don’t try to make the perfect resume on the first try. this tutorial is designed to be completed in rounds
it usually takes me a week to get a new master resume into working order
don’t worry about page length right now. you should make a multipage master resume that contains every relevant experience before making a 1-page resume. after you’ve made the master, you can build custom resumes from it for job applications
this post is best viewed on desktop, because i use nested bullets, and tumblr mobile hates those
let’s get into it!
step 1:
list out everything you’ve ever done that could feasibly count as a resume entry: extracurriculars, jobs, volunteer positions, research, organizations you were a part of (professional or casual), freelance work, long-term hobbies. i will refer to each different experience as an “entry”
for each entry, write where (city + state) and when (timespan) you did that thing
ex. tritones a cappella group, los angeles, ca, august 20xx - present
going forward, update this list as you join or complete new jobs/hobbies/whatever so that you don’t have to wrack your brain a year down the road wondering how long you held down that job or leadership role
step 2:
describe each entry
use bullet points to list out all the things you did within that role. start with the big picture, then move on to the small stuff
big picture: the goal of the role/organization/research, overarching and long-term projects, what results you were trying to achieve + why
ex. “studied the neuroanatomy and synaptopathy of the inner ear to determine the role of glutamate receptors in hearing loss”
small stuff: literal day-to-day tasks, every software and hardware you worked with, any particularly successful moments
basically, walk through a typical day or week in this role and list out every single thing you have to do, even the grunt work.
ex. “used redcap to administer neuropsychological batteries and collect biological data”
ex. “designed and implemented a novel article format that yielded a 10% increase in audience retention”
if you still have access to the original job posting or a corporate description of responsibilities for your role, pull that up and see how much you can paraphrase from it
no duty is too stupid rn. did you google weather forecasts for your boss every week? write it down. you can make it fancy or choose to delete it later
step 3:
fancify this shit
rewrite your bullet points from step 2 with better jargon. tell your employers what you did in a concise yet assertive manner
it helps to break down each point into its most basic components, which you can then generalize or rephrase
ex. “googled weather forecasts” might become “compiled weekly reports on changing data points to assess weather trends over time”
use action words. you can find resources all over the internet for this, but if you’re still struggling, shoot me an ask and i’ll link some of the resources i’ve used myself
caution: you don’t want to sound like you used a thesaurus on every word. make sure you aren’t obscuring the meaning of your bullet points. “googled weather forecasts” should not become “utilized online databases to assemble weekly communications on meteorological variations”
start thinking about how your responsibilities for each entry relate to a) what skills you want to showcase and b) what the employer wants from you. does the employer want you to demonstrate familiarity with online databases, or does the employer want you to demonstrate familiarity with weather forecasts? your bullet point for “googled the weather” will change depending on the answer to these questions
step 4:
look at the big picture
you probably have a metric buttload of bullet points for each entry. now you need to cut that down to what’s relevant. think about which bullets are most impressive, noteworthy, and descriptive of each entry
aim for 3-5 bullet points. any less than that and you have to ask why you’re including that entry. any more than that and the employer’s eyes will glaze over
try to combine bullet points
ex. “identify content and write articles when necessary,” “maintain a pool of freelancers,” and “identify key graphics and maintain tagging structure when uploading articles” all involve the process of creating an article, so they can be combined into: “identify content, assign stories to freelancers, write articles when necessary, and upload with appropriate graphics and tags”
start thinking about tailoring your word choices and bullet points to what the employer is looking for
if you can, pull up the job posting or a sample resume for the job you’re applying to and compare your resume to it. are you using similar language? are you demonstrating similar skills?
jobhero.com is a lifesaver
finally, eliminate redundancy in your resume, both in every individual entry and in the resume as a whole. if a skill can be demonstrated by multiple entries, you only need to list it once
kill your darlings! it may sound harsh, but the things that seem super impressive to you probably won’t even be a blip on the employer’s radar. “but saying i made coffee runs shows i’m dependable and a team player!” the employer isn’t looking that deep, my dude. you can showcase your dependability in your cover letter or your interview
you should redo steps 3 and 4 several times, soliciting feedback from your parents, peers, career center, etc each time
step 5:
add the Other Stuff
education
typically, you should only include institutions for the highest level of education you’ve attended. (undergrad and grad school both count as college for this purpose)
there are exceptions to this, depending on how long you’ve spent at a higher level of education, whether your alma mater will earn you brownie points, whether you had genuinely impressive accomplishments earlier in your life, etc.
once you hit, like, 2 years in college, you should try to get rid of high school achievements and showcase college achievements instead
list the school name, city + state, degree type (BA/MA/etc) and expected graduation date (even if it’s in the future), your major(s) + minor(s), and any related coursework (ie preprofessional tracks, specific courses related to the job). you can list your gpa if you feel it’s relevant, but i caution against doing this once you’ve graduated
ex. (where // indicates a new line) harvard university, boston, ma, may 2020 // bachelor of arts in cognitive neuroscience // minor: english: focus in creative writing // related coursework: pre-medicine, computer science 101 and 102 // gpa: 3.9/4.0 (dean’s list, all semesters)
skills
a list of items without descriptions. you can do a bulleted list or you can list the entries in paragraph form, separated by commas or bold bullets
hard skills: hardware, software, languages (spoken and programming), digital and communication platforms, social media proficiencies, other technologies and devices
ex. microsoft office suite, java, wordpress, slack, familiarity with ap and chicago style
soft skills: general qualities, buzzwords, personality traits
ex. leadership, conflict resolution, time management
certifications and awards
can be one section or two depending on how many of each you have
list each one on a separate bullet point
for each, write the certification or award, the institution that granted it, and the month and/or year you received it if relevant
publications
tbh i just cite my publications in the following format instead of following a style guide
lastname, firstname. “article or chapter title.” book title, publisher (aka company or website). publication date.
if you’re the sole author, you don’t need to list the author’s name
interlude: stretch the truth a bit. don’t lie about having experience or skills you don’t, but if you can reasonably google how to do something, boom! you’re proficient in it. if you worked with two team members who never pulled their weight? you just became the sole project lead. were you a beta reader for anime fanfiction back in the day? you’re a freelance editor, baby!
step 6:
now you have to organize all the entries from step 4
separate your entries into relevant sections. what’s relevant might change based on what you’re applying for
i’ve had, at various points in my life, some subset of the following sections: work experience, volunteer experience, leadership experience, research experience, writing experience, other relevant experience
list sections in order of descending importance
write all entries in reverse chronological order: start with the most recent and work your way backwards
write all bullet points in order of descending importance. unfortunately, i don’t have any quick tips on determining what’s important, but it helps to look at the job posting and see what matters to the employer
i tend to list big picture goals, then personal accomplishments (leadership skills, projects), then daily tasks
step 7:
format this shit
you can find resume templates online or in your word processor. templates serve as a good starting point, but i recommend creating your own format so you can edit and customize it with ease. this will probably involve a lot of fiddling with indentations, paragraph spacing, and moving things around
don’t go smaller than 10pt font
mess around with line and paragraph spacing to get the right balance of white space. if you’re curious about what i use, shoot me an ask and i’ll share my weirdly specific settings
keep an eye out for bullet points with orphan words (ie lines containing only 1-3 words) and get rid of them to streamline your resume
margins can be anywhere between 0.5″ and 1″
consistency is key! make sure each entry has the same kind of spacing. don’t use hyphens in one entry and en dashes in another
in the header, write your name, email, phone number, and address
interlude: save this version of your resume as your master resume. this gives you an unedited list of everything you ever did that you can now pick and choose from when you apply to jobs. update this list every 3-6 months.
step 8:
customize your resume for the job application
unless you’ve been in the industry for several years, your job-specific resume should be no more than 1 page
if you have more than 1 page: compare the job listing and your resume side by side and ask which entries demonstrate your capabilities most effectively, which bullet points are the punchiest, and if there’s any extraneous info
match each job requirement to one bullet point on your resume. then match each bullet point on your resume to a requirement in the listing. get rid of any bullet points that don’t meet either of those criteria. if multiple bullet points match the same job requirement, get rid of the extra bullet points
if you have significantly less than 1 page: see if you can add more bullet points or reformat your resume to introduce some more white space. a 2-column set-up is great for this, with section headers on the left and bullets on the right. do you have any hobbies you’re forgetting about? any soft skills you could add?
emulate the language of the job posting; use the same action words, the same soft skills
coda
your resume should work in tandem with your cover letter, but that’s a topic for another post. maybe in another 6 months i’ll write a post on that, too
always save your resume as a pdf! you don’t want your employer to have access to your metadata
if you made it through this whole post... i’m so sorry lmao but also thanks for sticking with me
let me know if you found this helpful or if this method scored you a job!
I think RTFM has its place. I know I’m not in the minority with this view, but it’s not exactly polite to talk about it. We have all started out on the other side of RTFM at some point, clueless and helpless, not knowing where to begin.
To be quite honest, I wasn’t told to RTFM that much, because I learned to program from books when I was 12, and I wished I could ask somebody for guidance. My father still knew BASIC from the late 70s and early 80s, and my teachers knew enough PASCAL to pass their own exams and then teach children how to use Microsoft Excel. There was nobody to turn to.
When I started learning Java from a book, I was very confused, and I learned many bad habits, idiosyncrasies of the book’s author that I stuck with because I didn’t understand what they meant. Then I bought a bigger, heavier book about Java, and I slowly learned to program. I learned programming in general and Java in particular at the same time. One textbook explained what classes in Java do, but not why you would use them, and the other textbook vacillated between treating OOP as a scary newfangled concept nobody understands but everybody has to use because of Java, and something you have to be familiar with already because the book assumed you already knew C, C++, BASIC and smalltalk.
I read the manual and I didn’t understand. What finally got me to understand OOP was ironically learning and reading code in Python. Unlike in Java and smalltalk, OOP in Python is optional. Although everything in Python is an object, the common Pythonic programming style is procedural, with OOP constructs used sparingly when they make sense. This finally let me understand what OOP is good for. No amount of contrived examples like “class PickUpTruck extends Car { ... }“ helped me understand OOP in Java.
The same thing can happen with classes, module systems, macros, build systems, version control systems, bug trackers, databases, and visual modelling languages like UML and FMC. They are all paradigms or technologies to manage complexity, and if I give a student a toy teaching example of a SQL database, multi-module program, or UML diagram, then the student will be confused rather than enlightened. If the complexity is missing from the example, then the benefit of using complexity-mitigating technology is not obvious.
That even goes for comments! What good are comments in a textbook example, with explanatory text already left and right of the code listing?
If I had a teacher who could explain OOP to me, things would have been so much easier. Eventually, I managed to learn what I needed to learn. Some things are much, much harder to learn if you can’t ask a teacher multiple clarificatory question in quick succession. It would be even better if your teacher asked you a couple of questions to drill down on which part you didn’t understand.
Nowadays, I see many questions on Discord, IRC, and forums from people who are just starting out learning to program. It’s a vast difference between learning Unity3D when you are already a programmer who shipped software in C++ and wrote games in Java, and learning programming, C#, game design, level design, shaders, 3D modelling, and the Unity3D Engine in one go.
If somebody asks a confused question online, the first order of business is to establish whether they are an expert or a confused beginner. That can sound confusing and condescending, but I often fear if I give a straight answer to a confused question, I do more harm than good.
When somebody asks “How do I iterate over the pixels in a pygame surface?“, I can give the straight answer, or three advanced answers with different performance characteristics. You probably want to use numPy and cache the results during level loading. Maybe you can also use numPy if it’s only an occasional thing, and you can stomach the dozens of megabytes of native code dependencies. You probably want to use OpenGL with a GLSL fragment shader if you do the thing every frame. If perchance you want to do palette-swaps only, then you can use the pygame palette handing functions rather than iterating over pixels and doing a dict look-up each time. Iterating over all the pixels in a pygame surface is slow. You can probably get away with it on a 16x16 sprite, but not on a 1920x1080 screenshot.
That’s not even the worst of it! I see confused questions by people who think they found a bug in a library/framework/engine, but actually they just don’t understand their own code, or they don’t understand the programming language. I see confused questions by people who don’t know what problem they are actually encountering, who don’t know what to Google.
These people don’t need to be told to RTFM. Either they already read the manual, but they don’t understand it, or they don’t even know which manual, or what to look for. They can’t be told to RTFM, but they can’t be given straight answers either. The best thing you can do is to ask “You’re new to this, aren’t you?“ and point them to a more basic tutorial. Or, if your time is worthless, you can decide to tutor them one-on-one over the Internet.
All these problems can combine into the worst possible scenario: Somebody asks on StackOverflow/IRC/the mailing list/Slack/Discord, because their teacher is not available, or told them to learn to RTFM and figure it out independently. These people can be high school students too intimidated by their teachers to ask questions, university students who can’t be bothered to attend lectures or wait for office hours, or junior programmers who are trying to impress their boss.
Figuring things out is a useful skill to have, but it’s not something you should rely on in a high school class. If you’re a teacher, don’t punish students for asking questions! And don’t expect students to bother strangers on the Internet to do your job for you. The best thing you can do to get students to RTFM is to answer their questions when they get stuck, so they get a good idea of what to look for, which terms to search for, what to ask on IRC.
If you want to teach your pupils to RTFM, you should at least follow up with them and point them in the right direction in case they get stuck trying to look up the answer, or if they don’t understand the text in the manual. Of course you can assign reading, but for some reason, some of your pupils will take this to mean that you refuse to explain the topic, so the only recourse is to ask strangers online.
And then the student comes back next week and asks even more confused questions, now that the next assignment is due, impossible to complete without having understood last week’s topic.
Around a third of confused newbie questions I see are from students who would rather not ask their teachers, not even those who post whole homework assignments.
Some people want us to stop saying RTFM online, ever. They also want us to stop saying “You’re new to this, aren’t you?“ or “Please take a step back and think about what you are trying to accomplish with this“. They all are too condescending. I’m not just trying to shift the blame away from open source projects and programmer online communities. Confused online questions sometimes have offline causes. These causes cannot be hyperlinked, retroactively screenshotted, and posted to twitter.
For all the talk of rudeness online, many students would rather ask questions here than at school. Maybe the problem lies in the classroom.
Ready for Big Data Training & Certification? Browse courses like Big Data - What Every Manager Needs to Know...
Despite big data currently ranking among top business intelligence and data analytics trends, businesses continue to suffer from a lack of data-savvy talent. Research from BARC shows half of respondents reporting a lack of analytical or technical know-how for big data analytics. This is good news for tech beginners, however, whose knowledge and skills are being welcomed by companies who want to reap the benefits of big data.
If you find data science a tempting opportunity, you’ll benefit from this overview of big data basics for beginners. Below, we’ll discuss what the requirements for jobs are and which skills you should master in order to start a successful data science career.
What is Big Data?
Instead of reciting a definition or giving a generic overview, let’s look at big data’s key features through the lens of something that is well known to all of us: recommendation engines. These are tools widely used in e-commerce to aid in customer experience, but that also help gather data about consumers. Web store visitors search for products, view them, add and remove them from their carts, make purchases like, etc. – and every activity is an entry in a database. The entry may look like “Customer X opened Product Y page.” Millions of customers exist, and they perform dozens of activities per visit, which means that a retailer needs impressive storage capacity to log all these actions.
Distributed data storage has become a solution to this problem. According to this principle, data is stored on numerous standard computers rather than on one custom-built powerful machine. This allows companies to achieve high scalability: when the number of records increases, the retailer can just add extra machines.
Each time a visitor starts a new tour on the website, the analytical system tracks all their activities and compares them with previous activities of this particular visitor and those of other visitors. In order to perform this task quickly, the analytical system divides the tasks among numerous machines to enable parallel data processing. The analysis results lay the basis for personalized recommendations.
Summing it up: Big data is data sets that resemble a log of events by nature and require distributed data storage, parallel data processing and special approaches and methods. You can learn more about big data use cases in this primer.
Big Data Technology Stack
You should generally expect to master multiple technologies to become an expert in big data. We’ve selected the most popular frameworks and programming languages for a beginner to get acquainted with. The list is not exhaustive: so, feel free to go beyond it whenever you are ready.
Big data frameworks
Apache Hadoop is a framework for parallel data processing and distributed data storage.
Apache Spark is a parallel data processing framework.
Apache Kafka is a stream processing framework.
Apache Cassandra is a distributed NoSQL database management system.
Big data programming languages
Java
Scala
Python
R (not obligatorily, but good to know)
What are the Programming Paradigms Used in Big Data?
It’s advisable to grasp general programming concepts (such as declarative and imperative), as well as big data-specific paradigms (MapReduce).
Declarative paradigm is the approach to programming that is focused on declaring what the task is and the expected results are, without describing the control flow. This approach is used in database programming. For example, SQL (Structured Query Language) is a declarative language.
Imperative programming is the approach focused on describing the commands that should be executed for the program to change its state. It is used for backend development (for instance, in Java).
For example: Copy a directory from A to B shows a declarative approach, while if it’s enriched with such commands as check if there are existing files with the same name and copy only new ones – it’s an imperative approach.
MapReduce paradigm is the concept of parallel processing of distributed data. It allows for dealing with large data sets by applying map function for data filtering, sorting or parameterization and reduce function for summarizing the interim results.
Jobs in Big Data
Now for the burning question: What kinds of big data jobs exist? The good news: there is quite a choice.
Data analysts closely interact with the end users to identify their needs, analyze and interpret data, build reports and visualize data.
Data scientists assess data sources and establish data collection procedures, apply algorithms and machine-learning techniques to mine data.
Data architects design databases and develop relevant documentation and policies.
Database managers control database performance, troubleshoot the corporate databases and upgrade hardware and software.
Big data engineers design, implement and support big data solutions.
Don’t be misled by the fact that only one of the jobs – a big data engineer – refers to big data directly. With good knowledge of big data, you have more value for any job in data analytics. With the lack of such knowledge, you may have limited opportunities in terms of the tasks or projects assigned.
Big data is evolving as more and more businesses see its benefits. However, research clearly shows a lack of big data experts. It’s time to bridge this gap by educating the next wave of tech beginners. To pave your way into the big data world, it’s important to get a strong grasp of the basics first. A newbie should cover both big data-specific technologies and general ones. Feel free to refer back to this article on your education journey, and best of luck!
Hi Katja, let me ask about your professional experience: how did you find your interest in computational linguistics and developed through it?
Hi, I started my university studies in Rostov-on-Don, Russia. My subject was German language and literature, with emphasis on literature and translation studies. I studied for 2 years and then continued my studies at the University of Cologne in Germany. I started there from the first year, as in Germany similar program starts two years later than in Russia. Additionally, I took another topic of interest, French, as I was keen to learn it. But after a couple of years, I realized I had high interest in linguistics, especially after taking courses in modern linguistics and formal syntax in university. But honestly, I was not aware of computational linguistics at that point. One day I found that there is a study subject ‘Linguistic data processing’ at the University of Cologne and I joined the class after a talk with a professor. After a couple of years I started to work at the department, and of course, it was a good time to learn programming, which I really enjoyed. At that point, I realized much more about computer science. We studied Java as a first language, though many in the field start now with Python. I remember we programmed a search engine over a summer.
It reminds me a talk to Natalia Karlova-Burbonus. Natalia has a very similar story: going from interest to the German language to Computational Linguistics in Germany.
My next question whether you remember your first project or last at that time.
Yes, my first job was related to exploring self-organizing maps (so-called Kohonen nets). I don’t remember all project details, but we worked on syntactic dependency structures and tried to represent it in Kohonen maps for the German language. After we tried different IE approaches, text classification and run other experiments. That was a great time for learning. I had done an internship during my studies as well. It was in Paris, at a software company called Arisem, so I could also practice my French. It was the B2B company which focused on semantic search, dedicated one, including crawling. Then I came back to finish my master thesis.
What was master thesis topic about?
It was about the numerical representation of text corpora including how can we represent corpora for classification. I tried LSA that time also, but the topic was like a meta-analysis of different approaches.
Then Ph.D. happened to you.
Yes, at some point after I decided to stay in academia, to do a Ph.D. I went to Jena university, a big move from Cologne. But it was not only a Ph.D. position but a research assistant position in a European project BootStrep. The focus was on biomedical text processing: text mining in biology, semantic search over the publication of medicine/bio published research. There is a huge database PubMed which has millions of citations and which continue to grow quickly. And, obviously, a problem for a biologist is to find relevant information in such an enormous amount of data. So, preprocessing of data, named entity recognition (NER), normalization of extracted entities and relation extraction, are of particular interest here. My personal focus was on relation extraction, e.g. how a researcher describes gene expression processes.
Did you have medicine ontology for named entities?
We had a couple of Ph.D. students, which helped to develop the ontology in our group, of course using terminology from established sources. It reminds also what else was great about the project group: everybody had a specific skill-set and the tasks were assigned well and according to a person focus: somebody worked on NER and fast annotation using active learning, someone - preprocessing, another person cared about the ontology, search engines. I focused on detection of events and relations. It was a great experience to have such a skilled team.
Do you remember a day when you realized that you need to leave the project?
I continued working on the project during my Ph.D. I started later and the main result I would say was my participation in BioNLP 2009 shared task, where I got a second place once evaluated. After that, I elaborated on my topic. On 2012 I’ve completed my Ph.D. and started to look for a new challenge. I could have stayed in the Biomedical domain, but I was open to other topics also as I studied a lot while reading about different topics, including dependency parsing, collecting data in general. Then I found an open position at Nuance, there were not many at that moment in Germany. So, I became one of the first joining the NLU (Natural Language Understanding) team and moved to Aachen, which I also like as it’s close to Cologne.
How many people in Nuance NLU team now?
There are about 60 people in Automotive cloud NLU, which includes Aachen, Montreal, and Burlington offices and people working remotely. Company-wide there are more NLU people (100+).
NLU is a challenge by the name. So, tell us, what do you do and how do you overcome the challenges?
First, our main application area is an automotive domain. Our team works at the moment on a classification of user intents and named entity recognition. So, you have one-two step dialog, one-shot query, which requires a classification of the intent. I’d say that it’s now for the navigation system, office system in the car.
Well, actually from my experience I remember around a year ago participating in a hackathon organized by Nuance NLU system. And if I recall correctly, for NLU system you need to provide not only intents but also labels, concepts to train it, am I right?
Yes, you also need to provide concepts which need to be detected.
Would be nice if you can share an example of a use-case.
Ok, the simple example is a question about the weather: “What will be the weather tomorrow in Trento?” So, we need to recognize the intent: weather, the date: tomorrow, the location: Trento. Another example, you can: would it be sunny tomorrow in Trento? So, we do have multiple steps, relying on statistical models and many features, like named entities, and lexical information (keywords sun, weather, etc). Both are possible: you can do intent classification first and then named entities or the other way around.
As I remember from the mentioned hackathon, you have two interfaces: speech and text.
You are right, but it’s another project, it’s Nuance MIX you mean, our project. In our solution, we provide an ability to type, use speech interface and handwriting.
You haven’t told us a lot of internal details yet ;) Ok, what languages do you support?
We support over 20 languages for Automotive cloud NLU, additionally to major European languages we have Czech, Swedish, Turkish, for Asia - Japanese, Cantonese, Mandarin, and others.
It leads me to the question: do you reuse models available or develop all yourself?
We develop all internally. For example, we have developers graduated from the Charles University in Prague, who work on Czech support.
That’s an interesting story about computational linguistics in Czech, though I wouldn’t call it as widely spoken as others in Europe, Charles University has two or three groups which develop universal dependencies for the language, though some more representative languages have none.
Alright, what do you work on currently?
It’s mostly improving accuracy for automotive-related projects (for features like navigation, weather search, and more), which includes adding of data. Also embedding extensions, and for that case, the main challenge is the proper evaluation, which helps to avoid a degradation in quality. We worked on a hybrid solution: embedded NLU and cloud NLU. As we have some overlap, we need to split the responsibilities in a clear way. We need to work on confidence for prediction. We are facing AI as well, I mean complex request, e.g. a user could ask: find me a good restaurant and a parking slot around. So, a combination of intents brings an interesting challenge.
So, let’s come back quickly to language sources: do you plan to release the language resources to the language developers community.
I have no insight regarding this from the business.
It is a company which was bought by Microsoft, Maluuba, which developed an evaluation dataset, NewsQA. So, releasing an evaluation dataset can be a good step from Nuance. Thank you for the talk and I wish you good luck with a challenge of multiple intents.
Thanks, I was happy to share the knowledge and what we do.
Image 1 is published with an agreement of K. Kruchinina
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