Indo‐European and Computational Cladistics

Ringe, Don; Warnow, Tandy; Taylor, Ann

doi:10.1111/1467-968x.00091

Cited by 241 publications

(279 citation statements)

References 11 publications

(11 reference statements)

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“…On these grounds, this school of thought might simply rule out some of Anttila's data. Ringe et al (2002) do not appear to sign up fully to this strongest view, though they do generally take a rather dim view of the value of distance data. Presumably not all of Anttila's data would have passed their own stringent data screening criteria; though as argued in Heggarty (in preparation a, §4.3.2), their screening itself is hardly without impact on their own database.…”

Section: (B) Limitations Of Neighbornetmentioning

confidence: 87%

Splits or waves? Trees or webs? How divergence measures and network analysis can unravel language histories

Heggarty

Maguire

McMahon

2010

Phil. Trans. R. Soc. B

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Linguists have traditionally represented patterns of divergence within a language family in terms of either a 'splits' model, corresponding to a branching family tree structure, or the wave model, resulting in a (dialect) continuum. Recent phylogenetic analyses, however, have tended to assume the former as a viable idealization also for the latter. But the contrast matters, for it typically reflects different processes in the real world: speaker populations either separated by migrations, or expanding over continuous territory. Since history often leaves a complex of both patterns within the same language family, ideally we need a single model to capture both, and tease apart the respective contributions of each. The 'network' type of phylogenetic method offers this, so we review recent applications to language data. Most have used lexical data, encoded as binary or multi-state characters. We look instead at continuous distance measures of divergence in phonetics. Our output networks combine branch-and continuum-like signals in ways that correspond well to known histories (illustrated for Germanic, and particularly English). We thus challenge the traditional insistence on shared innovations, setting out a new, principled explanation for why complex language histories can emerge correctly from distance measures, despite shared retentions and parallel innovations.

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Section: (B) Limitations Of Neighbornetmentioning

confidence: 87%

Splits or waves? Trees or webs? How divergence measures and network analysis can unravel language histories

Heggarty

Maguire

McMahon

2010

Phil. Trans. R. Soc. B

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“…We used the dataset assembled by Don Ringe and Ann Taylor [12]. While computing phylogenies, we also took into account some domain-specific information.…”

Section: Resultsmentioning

confidence: 99%

Computing weighted solutions in ASP: representation-based method vs. search-based method

Çakmak

Erdem

Erdoğan

2011

Ann Math Artif Intell

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For some problems with many solutions, like planning and phylogeny reconstruction, one way to compute more desirable solutions is to assign weights to solutions, and then pick the ones whose weights are over (resp. below) a threshold. This paper studies computing weighted solutions to such problems in Answer Set Programming. We investigate two sorts of methods for computing weighted solutions: one suggests modifying the representation of the problem and the other suggests modifying the search procedure of the answer set solver. We show the applicability and the effectiveness of these methods in reconstructing weighted phylogenies for Indo-European languages. We also compare these methods in terms of computational efficiency; the experimental results show that the search-based method is better.

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“…This problem is called the Binary Character Compatibility on Phylogenetic Networks, denoted henceforth by BCCPN. This problem is of prime interest on its own in the field of linguistics (see [6,11,12,14,16,17]). …”

Section: Ccpnmentioning

confidence: 99%

“…This case is of prime interest on its own since it models the problem of reconstructing evolutionary histories of languages, particularly from phonological and morphological character data, most of which exhibit at most two states [6,11,12,14,16,17]. We first prove the NP-hardness of this problem.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Evolution of Natural Languages: Complexity and Parameterized Algorithms

Kanj

Nakhleh

Xia

2006

Lecture Notes in Computer Science

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Abstract. In a recent article, Nakhleh, Ringe and Warnow introduced perfect phylogenetic networks-a model of language evolution where languages do not evolve via clean speciation-and formulated a set of problems for their accurate reconstruction. Their new methodology assumes networks, rather than trees, as the correct model to capture the evolutionary history of natural languages. They proved the NP-hardness of the problem of testing whether a network is a perfect phylogenetic one for characters exhibiting at least three states, leaving open the case of binary characters, and gave a straightforward brute-force parameterized algorithm for the problem of running time O(3 k n), where k is the number of bidirectional edges in the network and n is its size. In this paper, we first establish the NP-hardness of the binary case of the problem. Then we provide a more efficient parameterized algorithm for this case running in time O(2 k n 2 ). The presented algorithm is very simple, and utilizes some structural results and elegant operations developed in this paper that can be useful on their own in the design of heuristic algorithms for the problem. The analysis phase of the algorithm is very elegant using amortized techniques to show that the upper bound on the running time of the algorithm is much tighter than the upper bound obtained under a conservative worst-case scenario assumption. Our results bear significant impact on reconstructing evolutionary histories of languagesparticularly from phonological and morphological character data, most of which exhibit at most two states (i.e., are binary), as well as on the design and analysis of parameterized algorithms.

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Indo‐European and Computational Cladistics

Cited by 241 publications

References 11 publications

Splits or waves? Trees or webs? How divergence measures and network analysis can unravel language histories

Splits or waves? Trees or webs? How divergence measures and network analysis can unravel language histories

Computing weighted solutions in ASP: representation-based method vs. search-based method

Reconstructing Evolution of Natural Languages: Complexity and Parameterized Algorithms

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