Učenje pretraživanja za rješavanje zadataka obrade prirodnoga jezika

master's thesis

• Title (english): Learning to Search for Solving Natural Language Processing Tasks
• Author: Vjeran Crnjak
• Mentor: Jan Šnajder (mentor)
• Committee member: Jan Šnajder (predsjednik povjerenstva)
• Committee member: Marko Čupić (član povjerenstva)
• Committee member: Domagoj Jakobović (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2016-07-04, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Strukturno predviđanje i učenje sveprisutno je u problemima obrade prirodnoga
jezika. Metode učenja pretraživanja pružaju okvir u kojem je te probleme moguće
efikasno rješavati. U ovom radu dan je pregled povijesti metoda učenja
pretraživanja i osvrt na područja u strojnom učenju koja su omogućila njihov
razvoj. Pokazana je i primjena na razne probleme u obradi prirodnog jezika.
Istaknute su i bitne razlike između ostalih pristupa strukturnom učenju koje
utvrđuju nadmoć i prilagodivost metoda učenja pretraživanja. Ostvaren je
sustav koji zadatke označavanja vrste riječi i ovisnosnog parsanja vrši
združeno te su istaknute razlike i prednosti na pristupe koji zadatke ne gledaju
združeno. Vrednovanje sustava izvršeno je na podacima za hrvatski jezik.

Abstract (english)

Structured prediction and learning is omnipresent for problems in natural
language processing. Learning to search (L2S) methods provide a framework in
which these problems can be efficiently solved. This thesis gives an overview of
L2S methods and their theoretical basis that allowed their development. Their
application to various problems of natural language processing is described.
Important differences that show the superiority and flexibility of L2S
methods are pointed out in the context of previous solutions for structured and
joint prediction. Description of the development of a system that considers
part-of-speech tagging and dependency parsing as a joint task is provided and
detailed analysis of differences and advantages to various approaches is given.
Evaluation of the system is done on Croatian corpus.

• Keywords: učenje pretraživanja; obrada prirodnog jezika; strojno učenje; združeno učenje i predviđanje; hrvatski jezik
• Keywords (english): learning to search; natural language processing; machine learning; joint prediction and learning; Croatian language
• Language: croatian
• URN:NBN: urn:nbn:hr:168:209110
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2018-02-06 08:13:43