fix README

README.md

@@ -1,18 +1,26 @@
-# Automatic Question Answering (AQA)
-The Question Answering pipeline is built for flective languages. This pipeline comprises four major components: the Question Processor, Document Selector, Answer Selector, and Answer Extractor, where each module incorporates and state-of-the-art algorithm for a specific task. Using a Long Short Term (LSTM) neural network and standard Natural Language Processing (NLP) tools, the Question Processor is able to extract all necessary information regarding the input question. Provided features are then used by Document Selector, which incorporates the TF-IDF scoring mechanism enhanced by our weighting strategy. The final answer is then established by combining the last two modules. First, the Answer Selection module, based on Transformers and reinforced by context features, selects the most probable answer sentence. Then the Answer Extraction module extracts the shortest and still informative span as a final answer.
+# Simple Question Answering Database (SQAD)
+SQAD is a Czech database for question answering developed in **NLP laboratory at Masaryk University**. Database have been harvested from **Czech Wikipedia** articles by students and annotated with appropriate question, answer sentence, exact answer, question type and answer type. Part of speech tagging and lemmatization by [Majka](https://nlp.fi.muni.cz/projekty/ajka/) pipeline.
 
-The final pipeline is tested against the Simple Question Answering Database ([SQAD](https://gitlab.fi.muni.cz/nlp/sqad)) that was developed for training and testing purposes of the AQA system. 
+Each record consist of several files:
+* `01question.vert`: contains a question in vertical format
+* `03text.vert`: contains full text of Wikipedia article in vertical format. Or can be represented by symbolic link to another record that also uses the same text. This feature is designed for	saving the disk space.
+* `04url.txt`: stores original URL address
+* `05metadata.txt`: consists of several XML like lines that encodes important metadata
+	* `<q_type>`: question type
+    * `<a_type>`: answer type
+    * `<user>`: name of the user that creates the record
+* `06answer.selection.vert`: contains an sentence from the document that can answer the input question in vertical format
+* `09answer_extraction.vert`: includes a sub-string from answer selection sentence in vertical format. This sub-string is informative enough to answer the input question.
+* `10title.vert`: contains the title of the article itself in vertical format.
 
-On the SQAD database the Document Selector reaches more than 90\% accuracy on the document selection task, the Answer Selector selects the correct answer sentence in nearly 92\% of cases, and the Answer Extractor provides the correct final answer in almost 91\% of cases. 
+SQAD have been developed as a part of dissertation and is used in Automatic Question Answering (AQA) system. 
 
-AQA have been developed as a part of dissertation in  **NLP laboratory at Masaryk University**.
-
-## Cloning AQA modules
+## SQAD to databse submodule cloning:
 ```
 git submodule init
 git submodule update
 ```
- 
+
 Please cite:
 * MEDVEĎ, Marek and Aleš HORÁK. Sentence and Word Embedding Employed in Open Question-Answering. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018). Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, 2018. p. 486-492. ISBN 978-989-758-275-2.
 ```
@@ -32,31 +40,28 @@ Please cite:
 }
 ```
 
-* MEDVEĎ, Marek a Aleš HORÁK. AQA: Automatic Question Answering System for Czech. In Sojka Petr, Horák Aleš, Kopeček Ivan, Pala Karel. Text, Speech, and Dialogue 19th International Conference, TSD 2016 Brno, Czech Republic, September 12–16, 2016 Proceedings. Switzerland: Springer International Publishing, 2016. s. 270-278. ISBN 978-3-319-45510-5. doi:10.1007/978-3-319-45510-5_31.
+* Marek Medveď, Radoslav Sabol, and Aleš Horák. Czech Question Answering with Extended SQAD v3.0 Benchmark Dataset. In Horák, Aleš and Rychlý, Pavel and Rambousek, Adam. Proceedings of the Thirteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2019. Brno: Tribun EU, 2019. p. 99-108. ISBN 978-80-263-1530-8.
 ```
-@inproceedings{1353405,
-   author = {Medveď, Marek and Horák, Aleš},
-   address = {Switzerland},
-   booktitle = {Text, Speech, and Dialogue 19th International Conference, TSD 2016 Brno, Czech Republic, September 12–16, 2016 Proceedings},
-   doi = {http://dx.doi.org/10.1007/978-3-319-45510-5_31},
-   editor = {Sojka Petr, Horák Aleš, Kopeček Ivan, Pala Karel},
-   keywords = {Question Answering; AQA; Simple Question Answering Database; SQAD; Named entity recognition},
+@inproceedings{1591218,
+   author = {Sabol, Radoslav and Medveď, Marek and Horák, Aleš},
+   address = {Brno},
+   booktitle = {Proceedings of the Thirteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2019},
+   editor = {Horák, Aleš and Rychlý, Pavel and Rambousek, Adam},
+   keywords = {question answering; QA benchmark dataset; SQAD; Czech},
    howpublished = {tištěná verze "print"},
    language = {eng},
-   location = {Switzerland},
-   isbn = {978-3-319-45510-5},
-   pages = {270-278},
-   publisher = {Springer International Publishing},
-   title = {AQA: Automatic Question Answering System for Czech},
-   url = {http://dx.doi.org/10.1007/978-3-319-45510-5_31},
-   year = {2016}
+   location = {Brno},
+   isbn = {978-80-263-1530-8},
+   pages = {99-108},
+   publisher = {Tribun EU},
+   title = {Czech Question Answering with Extended SQAD v3.0 Benchmark Dataset},
+   year = {2019}
 }
-
 ```
- 
+
 [License](https://nlp.fi.muni.cz/en/LicenceWebCorpus)
 
 [Publications](https://is.muni.cz/person/359226#publikace)
 
-[Project web](https://nlp.fi.muni.cz/projekty/question_answering/)
+[Project web](https://nlp.fi.muni.cz/projekty/sqad/)