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Efficient enumeration of weighted tree languages over the tropical semiring
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)ORCID iD: 0000-0001-7349-7693
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)
2017 (English)In: Journal of computer and system sciences (Print), ISSN 0022-0000, E-ISSN 1090-2724, , 78 p.Article in journal (Refereed) Epub ahead of print
Abstract [en]

We generalise a search algorithm by Mohri and Riley from strings to trees. The original algorithm takes as input a nondeterministic weighted automaton M over the tropical semiring and an integer N, and outputs N strings of minimal weight with respect to M. In our setting, M is a weighted tree automaton, again over the tropical semiring, and the output is a set of N trees with minimal weight in this language. We prove that the algorithm is correct, and that its time complexity is a low polynomial in N and the relevant size parameters of M. 

Place, publisher, year, edition, pages
2017. , 78 p.
Keyword [en]
weighted tree automaton, N-best analysis, tropical semiring
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-132963DOI: 10.1016/j.jcss.2017.03.006OAI: oai:DiVA.org:umu-132963DiVA: diva2:1084722
Available from: 2017-03-27 Created: 2017-03-27 Last updated: 2018-01-13
In thesis
1. A novel approach to text classification
Open this publication in new window or tab >>A novel approach to text classification
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis explores the foundations of text classification, using both empirical and deductive methods, with a focus on author identification and syntactic methods. We strive for a thorough theoretical understanding of what affects the effectiveness of classification in general. 

To begin with, we systematically investigate the effects of some parameters on the accuracy of author identification. How is the accuracy affected by the number of candidate authors, and the amount of data per candidate? Are there differences in how methods react to the changes in parameters? Using the same techniques, we see indications that methods previously thought to be topic-independent might not be so, but that syntactic methods may be the best option for avoiding topic dependence. This means that previous studies may have overestimated the power of lexical methods. We also briefly look for ways of spotting which particular features might be the most effective for classification. Apart from author identification, we apply similar methods to identifying properties of the author, including age and gender, and attempt to estimate the number of distinct authors in a text sample. In all cases, the techniques are proven viable if not overwhelmingly accurate, and we see that lexical and syntactic methods give very similar results. 

In the final parts, we see some results of automata theory that can be of use for syntactic analysis and classification. First, we generalise a known algorithm for finding a list of the best-ranked strings according to a weighted automaton, to doing the same with trees and a tree automaton. This result can be of use for speeding up parsing, which often runs in several steps, where each step needs several trees from the previous as input. Second, we use a compressed version of deterministic finite automata, known as failure automata, and prove that finding the optimal compression is NP-complete, but that there are efficient algorithms for finding good approximations. Third, we find and prove the derivatives of regular expressions with cuts. Derivatives are an operation on expressions to calculate the remaining expression after reading a given symbol, and cuts are an extension to regular expressions found in many programming languages. Together, these findings may be able to improve on the syntactic analysis which we have seen is a valuable tool for text classification.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2017. 176 p.
Series
Report / UMINF, ISSN 0348-0542 ; 17.16
Keyword
Text classification, natural language processing, automata
National Category
Language Technology (Computational Linguistics) Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-138917 (URN)978-91-7601-740-1 (ISBN)
Public defence
2017-09-29, N430, Naturvetarhuset, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2017-09-04 Created: 2017-09-03 Last updated: 2018-01-13Bibliographically approved

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