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  • 1.
    Björklund, Johanna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Drewes, Frank
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Jonsson, Anna
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    A Comparison of Two N-Best Extraction Methods for Weighted Tree Automata2018Ingår i: Implementation and Application of Automata: 23rd International Conference, CIAA 2018, Charlottetown, PE, Canada, July 30 – August 2, 2018, Proceedings, Springer, 2018, s. 197-108Konferensbidrag (Refereegranskat)
    Abstract [en]

    We conduct a comparative study of two state-of-the-art al- gorithms for extracting the N best trees from a weighted tree automaton (wta). The algorithms are Best Trees, which uses a priority queue to structure the search space, and Filtered Runs, which is based on an algorithm by Huang and Chiang that extracts N best runs, implemented as part of the Tiburon wta toolkit. The experiments are run on four data sets, each consisting of a sequence of wtas of increasing sizes. Our conclusion is that Best Trees can be recommended when the input wtas exhibit a high or unpredictable degree of nondeterminism, whereas Filtered Runs is the better option when the input wtas are large but essentially deterministic.

  • 2.
    Björklund, Johanna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Drewes, Frank
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Jonsson, Anna
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Finding the N Best Vertices in an Infinite Weighted Hypergraph2017Ingår i: Theoretical Computer Science, ISSN 0304-3975, E-ISSN 1879-2294, Vol. 682, s. 78s. 30-41Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We propose an algorithm for computing the N best vertices in a weighted acyclic hypergraph over a nice semiring. A semiring is nice if it is finitely-generated, idempotent, and has 1 as its minimal element. We then apply the algorithm to the problem of computing the N best trees with respect to a weighted tree automaton, and complement theoretical correctness and complexity arguments with experimental data. The algorithm has several practical applications in natural language processing, for example, to derive the N most likely parse trees with respect to a probabilistic context-free grammar. 

  • 3.
    Björklund, Johanna
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Drewes, Frank
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Jonsson, Anna
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    On the N best problem for hypergraphs2016Ingår i: / [ed] A. Maletti, 2016Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    We propose an algorithm for computing the $N$ best roots of a weighted hypergraph, in which the weight function is given over an idempotent and multiplicatively monotone semiring. We give a set of conditions that ensures that the weight function is well-defined and that solutions exist. Under these conditions, we prove that the proposed algorithm is correct.  This generalizes a previous result for weighted tree automata, and in doing so, broadens the practical applications.

  • 4.
    Drewes, Frank
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Jonsson, Anna
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Contextual Hyperedge Replacement Grammars for Abstract Meaning Representations2017Ingår i: Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms / [ed] M. Kuhlmann, T. Scheffler, Association for Computational Linguistics, 2017, s. 102-111Konferensbidrag (Refereegranskat)
    Abstract [en]

    We show how contextual hyperedge replacement grammars can be used to generate abstract meaning representations (AMRs), and argue that they are more suitable for this purpose than hyperedge replacement grammars. Contextual hyperedge replacement turns out to have two advantages over plain hyperedge replacement: it can completely cover the language of all AMRs over a given domain of concepts, and at the same time its grammars become both smaller and simpler.

  • 5.
    Jonsson, Anna
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Towards semantic language processing2018Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    The overall goal of the field of natural language processing is to facilitate the communication between humans and computers, and to help humans with natural language problems such as translation. In this thesis, we focus on semantic language processing. Modelling semantics – the meaning of natural language – requires both a structure to hold the semantic information and a device that can enforce rules on the structure to ensure well-formed semantics while not being too computationally heavy. The devices used in natural language processing are preferably weighted to allow for comparison of the alternative semantic interpretations outputted by a device.

    The structure employed here is the abstract meaning representation (AMR). We show that AMRs representing well-formed semantics can be generated while leaving out AMRs that are not semantically well-formed. For this purpose, we use a type of graph grammar called contextual hyperedge replacement grammar (CHRG). Moreover, we argue that a more well-known subclass of CHRG – the hyperedge replacement grammar (HRG) – is not powerful enough for AMR generation. This is due to the limitation of HRG when it comes to handling co-references, which in its turn depends on the fact that HRGs only generate graphs of bounded treewidth.

    Furthermore, we also address the N best problem, which is as follows: Given a weighted device, return the N best (here: smallest-weighted, or more intuitively, smallest-errored) structures. Our goal is to solve the N best problem for devices capable of expressing sophisticated forms of semantic representations such as CHRGs. Here, however, we merely take a first step consisting in developing methods for solving the N best problem for weighted tree automata and some types of weighted acyclic hypergraphs.

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