Predviđanje vrste pitanja za jezično sučelje bazi podataka

undergraduate thesis

• Title (english): Question Type Classification for a Natural Language Database Interface
• Author: Fran Andrija Arbanas
• Mentor: Jan Šnajder (mentor)
• Committee member: Jan Šnajder (predsjednik povjerenstva)
• Committee member: Domagoj Alagić (član povjerenstva)
• Committee member: Martin Soldić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2017-07-10, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

S ciljem približavanja baza podataka ljudima bez znanja potrebnih za upravljanje istima počela su se razvijati jezična sučelja bazama podataka. Kako bi se izgradilo stabilno jezično sučelje nužno je odbacivati loše upite. Taj problem riješen je u ovom radu uporabom metoda otkrivanja novih vrijednosti i strojnog učenja. Sljedeći korak pri izgradnji jezičnog sučelja je predviđanje vrste pitanja u upitu, kako bi se upit mogao ispravno prevesti u jezik upita. U opsegu ovog rada taj problem riješen je klasifikacijom u 14 klasa koristeći metode obrade prirodnog jezika i strojnog učenja. U radu su detaljno opisani algoritmi koji su korišteni te postupci kojima su se rezultati dobili. Skup podataka na kojima su modeli učeni ručno je izgrađen te označen.

Abstract (english)

Natural language interfaces to databases began to develop with the goal to bring databases closer to the people with no knowledge about them. To make a natural language interface it is necessary to filter out spam queries. That problem is solved in this thesis by using methods of novelty detection and machine learning. The next step in creating a natural language interface is predicting the question type in query, in order for the query to be easily translated to query language. In the scope of this thesis that problem was solved by classification into 14 classes using methods of natural language processing and machine learning. Algorithms that were used in the thesis are also thoroughly explained as well as the methods by which we got results. Dataset on which the models were trained was compiled and labeled by hand.

• Keywords: Obrada prirodnog jezika; strojno učenje; jezično sučelja bazi podataka; stroj potpornih vektora; naivni Bayesov klasifikator
• Keywords (english): Natural language processing; machine learning; natural language interface; support vector machine; naive Bayes classifier
• Language: croatian
• URN:NBN: urn:nbn:hr:168:792546
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2019-03-13 18:40:08