CLEA1500

CLEA¹⁵⁰⁰ certifies that you have reached a standard of knowledge concerning the development of natural language grammars for UNLization and may officially participate in the UNL Research. The certificate consists of a set of 50 sentences that must be UNLized with IAN.

Goal

In CLEA¹⁵⁰⁰, you are expected to provide the lingware (dictionary and grammars) necessary to UNLize a translated version of UCA1.

Instructions

1. Training corpus
   1. Prepare the training corpus
      • If your native language is not English: translate (manually) the 50 sentences of the corpus UCA1 from English into your native language. Be as close as possible to the original, and provide one single translation for each sentence. This will be your input document file, and your goal will be to provide (automatically, through IAN) the UNL graphs for each sentence. See an example of input file here.
      • If your native language is English: use the corpus UCA2 as your input document file.
   2. Save the translated text (without the English original) in a plain text (.txt) file with UTF-8 encoding and upload it to UNLWEB>UNLDEV>IAN>NL FILES.
2. Dictionary
   1. Create a dictionary for your training corpus. Your dictionary must contain all and only the word forms appearing in your training corpus, and must comply with the Dictionary Specs.
   2. Save the NL-UNL dictionary in a plain text (.txt) file with UTF-8 encoding and upload it to UNLWEB>UNLDEV>IAN>DICTIONARIES.
3. Grammars
   1. Create the NL-UNL (analysis) grammars necessary to UNLize the natural language sentences of the translated corpus. These grammars are the most difficult (and the actual goal) of the whole analysis task. There can be three different types of grammar:
      • N-grammar, which is used to segment the text into sentences and to normalize the input text, resolving contractions, abbreviations and other phenomena that can be handled without the dictionary (i.e., by manipulating isolated strings of characters);
      • T-grammar, which is used to transform the list structure (i.e., the natural language sentence) into a graph structure (the UNL graph);
      • D-grammar, which is used to improve the performance of the tokenization and of the transformation.
   2. Save the NL-UNL grammars in a plain text (.txt) file with UTF-8 encoding and upload them to the corresponding tabs in UNLWEB>UNLDEV>IAN.
   3. Test them and do the necessary changes until you get good results.
4. Evaluation
   1. Export the actual output of IAN (range=1-100, trace-level=NONE).
   2. Check the F-measure of the actual output at UNLWEB>UNLARIUM>TOOLS>F-MEASURE.
   3. If the F-measure > 0.9, upload the corpus, dictionary and grammars to VALERIE (UNLWEB>VALERIE>CLEA1500) in order to have your exercise evaluated.

Samples and Examples

The following resources have been used to deal with UCA1 in English and may be used as a sample of what is expected to be provided

UNLization

Language	Corpus	Dictionary	N-Grammar	T-Grammar	D-Grammar	Output
English	uca1_eng.txt	eng_unl_dic.txt	eng_unl_ngrammar.txt	eng_unl_tgrammar.txt	eng_unl_dgrammar.txt	eng_unl_trace.txt

Recommended Readings

Before starting the activity, and in order to fully understand what is expected to be done, it is important for you to be acquainted with the following documentation:

• Tagset, because you are expected to use only the tags included in the tagset
• UNL Dictionary Specs, which is essential to understand the dictionary structure
• UNL Grammar Specs, which is essential to understand the grammar structure

It is also interesting to make a test drive with IAN.