Naslov
Croatian POS Tagger as a Prerequisite for Knowledge Extraction in Intelligent Tutoring Systems

Autori
Vasić, Daniel ; Žitko, Branko ; Grubišić, Ani ; Stankov, Slavomir ; Gašpar, Angelina ; Šarić-Grgić, Ines ; Tomaš, Suzana ; Peraić, Ivan ; Markić-Vučić, Matea

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
HCII 2021: Adaptive Instructional Systems. Design and Evaluation: Proceedings, Part I / Sottilare, Robert A. ; Schwarz, Jessica - Cham : Springer, 2021, 334-345

ISBN
978-3-030-77857-6

Skup
3rd International Conference on Adaptive Instructional Systems, AIS 2021, Held as Part of the 23rd HCI International Conference, HCII 2021

Mjesto i datum
Online, 24.07.2021. - 29.07.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Natural language processing ; Knowledge extraction ; Part of Speech tagging ; Intelligent Tutoring Systems

Sažetak
In this article we present an knowledge extraction approach that can be used in systems that implement teaching in a fully automated manner. These systems are called Intelligent Tutoring Systems (ITS) and are conceived around the idea of one-to-one teaching. Many such systems use natural language processing to improve the communication interface between student and the system. These techniques can be also used on the content creator side to semi- automate or fully automate the task of teaching content creation. In such systems the knowledge representation plays a crucial role to successfully implement teaching and encourage learning. The output of the knowledge extraction phase is a knowledge in the form of a hyper graph that can be used for adaption to the students current knowledge level. We present a deep neural network architecture for precise POS tagging of words written in languages that are morphologically rich. Using sparse representations for words in this task increases the vector space and makes learning more complex. This problem can be solved to some extent by using traditional vector representations but there is also the problem with representing words that are ambiguous. Proposed architecture uses a Bidirectional Encoder Representations from Transformers (BERT) model that is pre-trained on Croatian language to achieve state-of-the-art accuracy for POS tagging.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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