emnlp emnlp2011 emnlp2011-77 knowledge-graph by maker-knowledge-mining

77 emnlp-2011-Large-Scale Cognate Recovery


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Author: David Hall ; Dan Klein

Abstract: We present a system for the large scale induction of cognate groups. Our model explains the evolution of cognates as a sequence of mutations and innovations along a phylogeny. On the task of identifying cognates from over 21,000 words in 218 different languages from the Oceanic language family, our model achieves a cluster purity score over 91%, while maintaining pairwise recall over 62%.

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Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 edu Abstract We present a system for the large scale induction of cognate groups. [sent-3, score-0.574]

2 Our model explains the evolution of cognates as a sequence of mutations and innovations along a phylogeny. [sent-4, score-0.349]

3 On the task of identifying cognates from over 21,000 words in 218 different languages from the Oceanic language family, our model achieves a cluster purity score over 91%, while maintaining pairwise recall over 62%. [sent-5, score-0.331]

4 1 Introduction The critical first step in the reconstruction of an ancient language is the recovery of related cognate words in its descendants. [sent-6, score-0.725]

5 The traditional approach used by linguists—the comparative method—iterates be- tween positing putative cognates and then identifying regular sound laws that explain correspondences between those words (Bloomfield, 1938). [sent-8, score-0.453]

6 Successful computational approaches have been developed for large-scale reconstruction of phylogenies (Ringe et al. [sent-9, score-0.143]

7 , 2002; Daum e´ III and Campbell, 2007; Daum e´ III, 2009; Nerbonne, 2010) and ancestral word forms of known cognate sets (Oakes, 2000; Bouchard-C oˆt´ e et al. [sent-10, score-0.641]

8 While it may seem surprising that cognate detection has not successfully scaled to large num- bers of languages, the task poses challenges not seen in reconstruction and phylogeny inference. [sent-15, score-0.835]

9 For instance, morphological innovations and irregular sound changes can completely obscure relationships between words in different languages. [sent-16, score-0.286]

10 In this paper, we present a system that uses two generative models for large-scale cognate identification. [sent-18, score-0.574]

11 Both models describe the evolution of words along a phylogeny according to automatically learned sound laws in the form of parametric edit distances. [sent-19, score-0.458]

12 The first is an adaptation of the generative model of Hall and Klein (2010), and the other is a new generative model called PARSIM with connections to parsimony methods in computational biology (Cavalli-Sforza and Edwards, 1965; Fitch, 1971). [sent-20, score-0.231]

13 To help correct this deficiency, we also describe an agglomerative inference procedure for the model of Hall and Klein (2010). [sent-22, score-0.177]

14 By using the output of our system as input to this system, we can find cognate groups that PARSIM alone cannot recover. [sent-23, score-0.659]

15 We apply these models to identifying cognate groups from two language families using the Austronesian Basic Vocabulary Database (Greenhill et al. [sent-24, score-0.659]

16 The datasets are by far the largest on which automated cognate recovery has ever been attempted, with 18 and 271 languages respectively. [sent-29, score-0.693]

17 We also analyze the mistakes of our system, where we find that some of the erroneous cognate groups our system finds may not be errors at all. [sent-33, score-0.687]

18 It also contains manual reconstructions for select ancestor languages produced by linguists. [sent-50, score-0.158]

19 These words are grouped into cognate groups and arranged by gloss. [sent-53, score-0.659]

20 For instance, there are 37 distinct cognate groups for the gloss “tail. [sent-54, score-0.753]

21 In this sample, there are 6307 such cognate groups and 210 distinct glosses. [sent-58, score-0.659]

22 Moreover, there is some amount of homoplasy—that is, languages with a word from more than one cognate group for a given gloss. [sent-60, score-0.727]

23 The languages in this group are exclusively found on the Austronesian homeland of Formosa. [sent-65, score-0.179]

24 For our final test set, we use the Oceanic subfamily, which includes almost 50% of the languages in the Austronesian family, meaning that it represents around 10% of all languages in the world. [sent-69, score-0.17]

25 3 Models In this section we describe two models, one based on Hall and Klein (2010)—which we call HK10—and another new model that shares some connection to parsimony methods in computational biology, which we call PARSIM. [sent-72, score-0.183]

26 Both are generative models that describe the evolution of words w‘ from a set of languages {‘} in a cognate group g along a fixed phylogeny T{‘. [sent-73, score-0.917]

27 1} nE aac cho cognate group laonndg w ao firxd ids pahlysoassociated with a gloss or meaning m, which we assume to be fixed. [sent-74, score-0.736]

28 2 In both models, words evolve according to regular sound laws ϕ‘, which are specific to each language. [sent-75, score-0.273]

29 In HK10, there is an unknown number of cognate groups G where each cognate group g consists of a set of words {wg,‘}. [sent-81, score-1.301]

30 In each cognate group, words esvetol ovfe w along a phylogeny, awchhe croeg genaacthe w groordup pin, a olardn-s guage is the result of that word evolving from its parent according to regular sound laws. [sent-82, score-0.814]

31 To model the fact that not all languages have a cognate in each group, each language in the tree has an associated “survival” variable Sg,‘, where a word may be lost on that branch (and its descendants) instead of evolving. [sent-83, score-0.684]

32 346 Figure 1: Plate diagrams for (a) HK10 (Hall and Klein, 2010) and (b) PARSIM, our new parsimony model, for a small set of languages. [sent-89, score-0.183]

33 In HK10, words are generated following a phylogenetic tree according to sound laws ϕ, and then “scrambled” with a permutation π so that the original cognate groups are lost. [sent-90, score-0.953]

34 In PARSIM, all words for each of the M glosses are generated in a single tree, with innovations I starting new cognate groups. [sent-91, score-0.676]

35 The task of inference then is to recover the original cognate groups. [sent-94, score-0.644]

36 The generative process for their model is as fol- lows: • • For each cognate group g, choose a root word Wroot ∼ p(W|λ), a language am orodoetl over words. [sent-95, score-0.642]

37 We reproduce the graphical model for HK10 for a small phylogeny in Figure 1a. [sent-103, score-0.144]

38 Inference in this model is intractable; to perform inference exactly, one has to reason over all partitions of the data into cognate groups. [sent-104, score-0.632]

39 First, we restrict the cognate assignments to stay within a gloss. [sent-108, score-0.574]

40 Second, we use an agglomerative inference procedure, which greedily merges cognate groups that result in the greatest gain 347 in likelihood. [sent-110, score-0.836]

41 That is, for all pairs of cognate groups ga with words wa and gb with words wb, we compute the score: logp(wa∪b|ϕ) − logp(wa |ϕ) − logp(wb |ϕ) This score is the difference between the log probability of generating two cognate groups jointly and generating them separately. [sent-111, score-1.318]

42 These changes may either be mutations, which merely change the surface form of the word, or innovations, which start a new word in a new cognate group that is unrelated to the previous word. [sent-121, score-0.69]

43 Mutations take the form of a conditional edit operation that models insertions, substitutions, and deletions that correspond to regular (and, with lower probability, irregular) sound changes that are likely to occur between a language and its parent. [sent-122, score-0.307]

44 We also depict our model as a plate diagram for a small phylogeny in Figure 1b. [sent-134, score-0.144]

45 Instead, pieces of the phylogeny are simply “cut” into subtrees whenever an innovation occurs. [sent-136, score-0.243]

46 4 Relation to Parsimony PARSIM is related to the parsimony principle from computational biology (Cavalli-Sforza and Edwards, 1965; Fitch, 1971), where it is used to search for phylogenies. [sent-142, score-0.231]

47 When using parsimony, a phylogeny is scored according to the derivation that requires the fewest number of changes of state, where a state is typically thought of as a gene or some other trait in a species. [sent-143, score-0.192]

48 When inducing phylogenies of languages, a natural choice for characters are glosses from a restricted vocabulary like a Swadesh list, and two words are represented as the same value for a character if they are cognate (Ringe et al. [sent-145, score-0.68]

49 Therefore, the parsimony score for this tree is two. [sent-151, score-0.208]

50 For instance, it might be extremely likely that B changes into both A and C, but that A never changes into B or C, and so weighted variants of parsimony might be necessary (Sankoff and Cedergren, 1983). [sent-153, score-0.279]

51 3 5 Limitations of the Parsimony Model Potentially, our parsimony model sacrifices a certain amount of power to make inference tractable. [sent-161, score-0.218]

52 Specifically, it cannot model homoplasy, the presence of more than one word in a language for a given 3It is worth noting that we are not the first to point out a connection between parsimony and likelihood. [sent-162, score-0.183]

53 (Aa)ABACAA(bA){A,BB}{AB,B}{A,AB}(cA)ABABAA(dA)AAAABBB BB 3: Trees illustrating parsimony and its limitations. [sent-164, score-0.183]

54 Here, given this tree, it seems likely that the ancestral languages contained both A and B. [sent-169, score-0.152]

55 Instead, it can at best only select group A or group B as the value for the parent, and leave the other group fragmented as two innovations, as in Figure 3c. [sent-176, score-0.204]

56 Where it does appear, our model should simply fail to get one of the cognate groups, instead explaining all of them via innovation. [sent-183, score-0.574]

57 To repair this shortcoming, we can simply run the agglomerative clustering procedure for the model of Hall and Klein (2010), starting from the groups that PARSIM has recovered. [sent-184, score-0.227]

58 Specifically, we group each word variable W‘ with its innovation parameter I‘. [sent-190, score-0.167]

59 Automata have been used to successfully model distributions of strings for inferring morphology (Dreyer and Eisner, 2009) as well as cognate detection (Hall and Klein, 2010). [sent-200, score-0.574]

60 Even in models that would be tractable with “ordinary” messages, inference with automata quickly becomes intractable, because the size of the automata grow exponentially with the number of messages passed. [sent-201, score-0.232]

61 Dreyer and Eisner (2009) used a mixture of a k-best list and a unigram language model, while Hall and Klein (2010) used an approximation procedure that projected complex automata to simple, tractable automata using a modified KL divergence. [sent-203, score-0.169]

62 That is, if a gloss has 10 distinct words across all the languages in our dataset, we pass messages that only contain information about those 10 words. [sent-208, score-0.207]

63 1 Sound Laws The core piece of our system is learning the sound laws associated with each edge. [sent-218, score-0.242]

64 Since the foundation of historical linguists with the neogrammarians, linguists have argued for the regularity of sound change at the phonemic level (Schleicher, 1861 ; Bloomfield, 1938). [sent-219, score-0.313]

65 In practice, of course, sound change is not entirely regular, and complex extralinguistic events can lead to sound changes that are irregular. [sent-221, score-0.382]

66 Speakers of these languages do find ways around this prohibition, often resulting in sound changes that cannot be explained by sound laws alone (Keesing and Fifi’i, 1969). [sent-223, score-0.542]

67 Nevertheless, we find it useful to model sound change as a largely regular if stochastic process. [sent-224, score-0.198]

68 We employ a sound change model whose expressive power is equivalent to that of Hall and Klein (2010), though with a different parameterization. [sent-225, score-0.167]

69 This last feature reflects the propensity of a particular phoneme to appear in a given language at all, no matter what its ancestral phoneme was. [sent-236, score-0.155]

70 These parameters can be learned separately, though due to data sparsity, we found it better to use a tied parameterization as with the sound laws. [sent-242, score-0.167]

71 As with the sound laws, we used an ‘2 regularization penalty to encourage the use of the global innovation parameter. [sent-244, score-0.266]

72 PARSIM is the new parsimony model in this paper, Agg. [sent-259, score-0.183]

73 HK10 is our agglomerative variant of Hall and Klein (2010) and Combination uses PARSIM’s output to seed the agglomerative matcher. [sent-260, score-0.284]

74 For the agglomerative systems, we report the point with maximal F1 score, but we also show precision/recall curves. [sent-261, score-0.142]

75 1 Cognate Recovery We ran both PARSIM and our agglomerative version of HK10 on the Formosan datasets. [sent-265, score-0.142]

76 For PARSIM, we initialized the mutation parameters ϕ to a model that preferred matches to insertions, substitutions and deletions by a factor of e3, innovation parameters to 0. [sent-266, score-0.173]

77 For the agglomerative HK10, we initialized its parameters to the values found by our model. [sent-268, score-0.142]

78 4 Based on our observations about homoplasy, we also considered a combined system where we ran PARSIM, and then seeded the agglomerative clustering algorithm with the clusters found by PARSIM. [sent-269, score-0.142]

79 Specifically, each cluster is assigned to the cognate group that is the most common cognate word in that group, and then purity is computed as the fraction of words that 4Attempts to learn parameters directly with the agglomerative clustering algorithm were not effective. [sent-272, score-1.424]

80 are in a cluster whose gold cognate group matches the cognate group of the cluster. [sent-276, score-1.284]

81 On Formosan, PARSIM has much higher precision and purity than our agglomerative version of HK10 at its highest point, though its recall and F1 suffer somewhat. [sent-287, score-0.232]

82 Next, we also examined precision and recall curves for the two agglomerative systems on For5The main difference between precision and purity is that pairwise precision is inherently quadratic, meaning that it penalizes mistakes in large groups much more heavily than mistakes in small groups. [sent-291, score-0.421]

83 2 Reconstruction We also wanted to see how well our cognates could be used to actually reconstruct the ancestral forms of words. [sent-297, score-0.289]

84 (2009)’s reconstruction system using both the cognate groups PARSIM found in the Oceanic language family and the gold cognate groups provided by the ABVD. [sent-299, score-1.497]

85 We then evaluated the average Levenshtein distance of the reconstruction for each word to the reconstruction of that word’s Proto-Oceanic ancestor provided by linguists. [sent-300, score-0.268]

86 (2009) in that they averaged over cognate groups, which does not make sense for our task because there are different cognate groups. [sent-302, score-1.148]

87 Using this metric, reconstructions using our system’s cognates are an average of 2. [sent-304, score-0.219]

88 47 edit operations from the gold reconstruction, while with gold cognates the error is 2. [sent-305, score-0.206]

89 While the plots are similar, the automatic cognates exhibit a longer tail. [sent-310, score-0.18]

90 Thus, even with automatic cognates, the reconstruction system can reconstruct words faithfully in many cases, but in a few instances our system fails. [sent-311, score-0.159]

91 1 Precision Many of our precision errors seem to be due to our somewhat limited model of sound change. [sent-315, score-0.191]

92 Somewhat surprisingly the former word is cognate with Paiwan /qmereN/ and Saisiat /maPr@m/ while the latter is not. [sent-321, score-0.574]

93 Perhaps a more sophisticated model of sound change could correctly learn this relationship. [sent-324, score-0.167]

94 However, despite these words’ similarity, there are actually three cognate groups here. [sent-327, score-0.659]

95 Crucially, these cognate groups do not follow the phylogeny closely. [sent-329, score-0.803]

96 While a more thorough, linguisticallyinformed analysis is needed to ensure that these are actually cognates, we believe that our system, in 353 conjunction with a trained Austronesian specialist, could potentially find many more cognate groups, speeding up the process of completing the ABVD. [sent-331, score-0.574]

97 For instance, there is a cognate group for “to eat” that includes Bunun /maun/, Thao /kman/, Favorlang /man/, and Sediq /manakamakan/, among others. [sent-335, score-0.642]

98 Reduplication cannot be modeled using mere sound laws, and so a more complex transition model is needed to correctly identify these kinds of changes. [sent-337, score-0.167]

99 10 Conclusion We have presented a new system for automatically finding cognates across many languages. [sent-338, score-0.18]

100 Automatic prediction of cognate orthography using support vector machines. [sent-469, score-0.574]


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