Why I started learning Java

I am learning Java. This is why.

My job applications were rejected several times on the grounds of not having “formal programming knowledge”. I have done some nice amateurish open source stuff and tools, proved myself in several technologies, but that was not good enough to convince HRs that I am worthy of a junior position. I will not go into details about the quality of the jobs I applied for, the context of the applications or experiences related to it – to me the rejections were enough to do something. I had to choose, and I wanted to choose, a programming language to study as formally as I could, and this is why I decided to dedicate my time and money to Java.

Open and free versus closed and proprietary

I have always liked open source world. I have learned so much from it and got introduced to some great people. I also believe that sharing knowledge and allowing creative freedom is a good thing, that has its rightful place in today’s consumerist/corporate world. Java is open and runs on almost anything. Chosen: Java. Rejected: C#

Java is in demand

True, Java is not the freshest thing around when it comes to IT word, but it is relevant and in demand. I browsed though job advertisements and compared Java with other technologies: Java seemed to be consistently present throughout years. Chosen: Java. Rejected: C#, PHP

Java is versatile

One of the courses offered to me was a course in PHP. I have never been a fan of PHP, but I worked on it when I had to, and there are some awesome projects written in it (this CMS for example!). Also, I did not feel I can learn the things I wanted the way I could with Java (OO and some advanced meta stuff). Another option was C++, which was not really my cup of tea (I am not interested into low-level languages). Chosen: Java. Rejected: C#, PHP, C++

Java is corporate

Now, this was an interesting moment for me: Java is a corporate technology. My programming projects have been either related to academic research or to open source / startups. I have not had a chance to see how programming works within a corporate context, and Java seemed like a perfect way into that world. It may not be the perfect world, but it dominates, and it would be foolish to ignore it. After all, all those startups are hoping to become a corporate leaders. Also, there have been moments where I felt at ease with more strict corporate frame of mind, than with over the place fresh startup’s. Chosen: Java. Rejected: PHP

So, what about Python?

I had not found a suitable course where Python is studied formally. Even if I had, that would have not affected my decision to choose Java. I love Python I keep coming back to it (most of the projects I wrote about on this site are Python-related), but sometimes it’s good to get a flavour of a different mental setup and learn new techniques. I have experienced that and I loved it when I had to learn the basics of R programming; now I am looking forward to the same excitement in Java. Learning a new programming language (a formal language) is similar to learning a new language (a natural language): you get a change to see reality from different angles, get to know different culture.

So, off I go to the Java adventures.

SNTRecorder – A tool to assist speakers in corpus recording

The purpose of SNTRecorder is to assist in corpus recording. It was written to make speakers feel easier during the recording process, by allowing them to choose they own pace in pronunciation, but again not allowing sentences to be read too fast. Written in Python, with interface created in Tkinter (Tcl/Tk), it is a multiplatform tool, executable on Linux and Windows. Download SNTRecorder (source code, 48KB).

Compiled sentence where user is allowed to read it aloud

Assistance during a recording session

SNTRecorder is not a recording software, just a small utility that assists in the process.  The best way to understand what this program do read the workflow description (taken from my MA paper about phonetics):

  1. The project is loaded and the program creates the sentences and the time list, and then  shuffles them.
  2. A user sits in front of the screen and enters the initials.
  3. The program shows a sentence and a red line in the lower part of the sentence screen. The sentence is crossed out at this step, as a signal to the speaker to read the sentence without saying it aloud.
  4. The randomly selected time for the current sentence elapses and the red line changes to green, and the text appears normal.
  5. The speaker pronounces the sentence and presses the “next” key to continue.
  6. Steps 3 through 5 repeat until all sentences are recorded.
  7. The program informs that the current session is over and asks if there is need to repeat some items. If answered yes, a window is shown to select the sentences and to repeat  steps 3-6 for a given selection. The session ends once there is no re-recording.
  8. A new user is ready and the cycle restarts.
Compiled sentence where user must wait to pronounce it

Speaker logs

The program creates a simple log for each speaker. Here is a sample of a log.

# Started at: 2011-03-11-10:42
# Ended at: 2011-03-11-10:48
# Speaker: ana
# Random time: 1-4
# Time scale: 10

Sentence t(1/SCALE s) Next
The word "theirs" is spoken. 19.6834339953 10:44:07:607000
The word "abjured" is spoken. 22.7161564864 10:44:16:982000
The word "bait" is spoken. 26.945639852 10:44:23:597000
The word "dare" is spoken. 19.9025319695 10:44:34:642000
The word "fierce" is spoken. 16.5143768762 10:44:42:457000
The word "douse" is spoken. 19.3227666274 10:44:48:136000
The word "bourse" is spoken. 34.6668924964 10:44:54:766000
The word "fears" is spoken. 21.7415600234 10:45:02:441000
The word "Job" is spoken. 18.9044580715 10:45:10:100000

The log is preceded by time and speaker information. Random time indicates the brackets for selecting minimum and maximum values the user will be prevented to move to the next sentence. The log lines consist of the sentence that was shown on the screen,  randomly selected time to prevent showing the next sentence, and finally the time when speaker pressed the “next” key.

Project settings, execution and OS difference

sntrecorder main window
Main window of the program

To create your own project, just make a copy of sampleproject.py and leave it in the same directory. Then, edit the new file. The comments explain how to change the template sentence and insert new words. The file name (without extension) is you project name in the program. To change min and max time for users to wait between each sentence, edit TIMEMIN and TIMEMAX in project.py file.

To execute the program, go into src folder and type python3 sntrecorder.py. If your’re using Windows you’ll have to provide the full path to the file, usually something like C:\python32\python,exe, but it depends on Python version you are using (must be 3+).

Sound will not work on Linux (clicks for start and end of a sentence, which I doubt you might need). Also, you will need python3-tk library.

Program uses distinct strings for on-screen messages that are editable in language.py, and you can provide localized versions. An example, in Serbian, is already there.

As you can see, this  is a small tool that I created to make the recording easier for students. Because this was just one of the tools I developed for my thesis, it is not user-friendly in terms of rich interface settings. But, thanks to open source and portability, you are free to adopt it to your requirements, with only a text editor and some patience. I hope you will find it useful.

Download SNTRecorder (source code, 48KB).

srmorph: Serbian Morphology in Python

My interest in linguistics and programming is continued with an experiment in morphology and srmorph project. It is a pilot endeavour I use to test ideas about parsing words of my native language (Serbian) on word level, and later, syntactic level. This post is about the work in progress.

What Can Be Seen, Searched, Parsed?

The project for time being has only Web/AJAX interface at http://srmorph.languagebits.com/ which allows:

Affixes as Basics

At the foundation of srmorph are Serbian affixes. I always wanted to write a parser that would work by first examining words on the level of prefixes an suffixes (infixes are somewhat tougher problem). Therefore, the analysis is for now based on identifying affixes.

Environment and Data Format

The environment is Python 3 programming language, while the grammar data format is based around Python classes themselves. The uninstantiated classes are the actual data containers, and after they inherit from main meta classes, the become useful for parsing. For example, a class containing suffixes about declension looks like this:

class AffNounDeclension0(MAffix):
    """Suffix. Example: 'доктор'. Ref. Klajn:51."""
    pos = 'MNoun'
    place = 'end'
    process = ('inflection', 'declension')
    subtype = 1
    gender = 'm'
    suffix = {0:'', 1:'а', 2:'у', 3:'а', 4:'е', 5:'ом', 6:'у'}
    blendswith = ('nonpalatal',)

The attribute suffix lists seven endings glued to some masculine nouns in Serbian (Croatian, Bosnian). POS identifies word class, here a noun, etc.

Parsing and Website

The inherited Serbian affix classes (60+) are so far parsed functionally. I have set up a dynamic website at http://srmorph.languagebits.com/ which shows some of the things that can be done by parsing. For now the algorithm is rather straightforward, until further filtering is introduced on word class level.

Once reasonably developed, the project will become open source.

screenshot: all classes where suffix "na"
Details about affix “na” in Serbian