Phonotactics in morphological similarity metrics

Hathout, Nabil

doi:10.1016/j.langsci.2014.06.008

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…We approach this problem by sorting the list of possible base words for a lexeme from the most plausible ones to the least probable ones. Because sorting a whole lexicon for each lexeme is infeasible, we restrict the list of possible base words to the 100 most similar words according to Proxinette measure (Hathout, 2009;Hathout, 2014). Such constructed candidate list is sorted by a ranker, which is previously trained on a relatively small training set provided by a linguist.…”

Section: Machine Learning Approach: Polish and Spanishmentioning

confidence: 99%

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Semi-automatic construction of word-formation networks

Lango

Žabokrtský

Ševčíková

2020

Lang Resources & Evaluation

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The article presents a semi-automatic method for the construction of word-formation networks focusing particularly on derivation. The proposed approach applies a sequential pattern mining technique to construct useful morphological features in an unsupervised manner. The features take the form of regular expressions and later they are used to feed a machine-learned ranking model. The network is constructed by applying the learned model to sort the lists of possible base words and selecting the most probable ones. This approach, besides relatively small training set and a lexicon, does not require any additional language resources such as a list of vowel and consonant alternations, part-of-speech tags etc. The proposed approach is evaluated on lexeme sets of four languages, namely Polish, Spanish, Czech, and French. The conducted experiments demonstrate the ability of the proposed method to construct linguistically adequate word-formation networks from small training sets. Furthermore, the performed feasibility study shows that the method can further benefit from the interaction with a human language expert within the active learning framework.

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Section: Machine Learning Approach: Polish and Spanishmentioning

confidence: 99%

“…In order to avoid the consideration of all possible parents for each lexeme, only a candidate set of most morphologically similar words is considered. As a measure of morphological similarity, the Proxinette distance (Hathout, 2009;Hathout, 2014) is used. Next, the previously trained ranker is applied to order each candidate set.…”

Section: Introductionmentioning

confidence: 99%

Semi-automatic construction of word-formation networks

Lango

Žabokrtský

Ševčíková

2020

Lang Resources & Evaluation

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“…Commonly employed measures are as follows: sequence matcher ratio, Damerau–Levenshtein distance, normalized Damerau–Levenshtein distance, Jaccard distance, Masi distance, and Jaro–Winkler similarity distance. These measures have many applications in language research, biology (such as in DNA and RNA analysis), and data mining ( Bisani & Ney, 2008 ; Damper & Eastmond, 1997 ; Ferragne & Pellegrino, 2010 ; Gillot et al, 2010 ; Hathout, 2014 ; Heeringa et al, 2009 ; Hixon et al, 2011 ; Jelinek, 1996 ; Kaiser et al, 2002 ; Navarro, 2001 ; Peng et al, 2011 ; Riches et al, 2011 ; Schlippe et al, 2010 ; Schlüter et al, 2010 ; Spruit et al, 2009 ; Tang & van Heuven, 2009 ; Wieling et al, 2012 ). In a study by Smith et al (2019) , the phonemic edit distance ratio, which is an automatic distance function, was employed to estimate error frequency analysis for evaluating the speech production of individuals with acquired language disorders, such as apraxia of speech and aphasia with phonemic paraphasia, highlighting the efficacy of distance metrics in automating manual measures in the context of language pathology.…”

Section: Alternative Approaches Using “Distance Functions”mentioning

confidence: 99%

A Tool for Automatic Scoring of Spelling Performance

Themistocleous¹,

Neophytou

Rapp

et al. 2020

J Speech Lang Hear Res

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Purpose The evaluation of spelling performance in aphasia reveals deficits in written language and can facilitate the design of targeted writing treatments. Nevertheless, manual scoring of spelling performance is time-consuming, laborious, and error prone. We propose a novel method based on the use of distance metrics to automatically score spelling. This study compares six automatic distance metrics to identify the metric that best corresponds to the gold standard—manual scoring—using data from manually obtained spelling scores from individuals with primary progressive aphasia. Method Three thousand five hundred forty word and nonword spelling productions from 42 individuals with primary progressive aphasia were scored manually. The gold standard—the manual scores—were compared to scores from six automated distance metrics: sequence matcher ratio, Damerau–Levenshtein distance, normalized Damerau–Levenshtein distance, Jaccard distance, Masi distance, and Jaro–Winkler similarity distance. We evaluated each distance metric based on its correlation with the manual spelling score. Results All automatic distance scores had high correlation with the manual method for both words and nonwords. The normalized Damerau–Levenshtein distance provided the highest correlation with the manual scoring for both words ( r s = .99) and nonwords ( r s = .95). Conclusions The high correlation between the automated and manual methods suggests that automatic spelling scoring constitutes a quick and objective approach that can reliably substitute the existing manual and time-consuming spelling scoring process, an important asset for both researchers and clinicians.

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“…For normalizing Twitter data, Jin uses bigrams, skip-1-bigrams and sets the weight for each feature to 1. Barteld et al (2015) use yet another similarity measure, Proxinette (Hathout, 2014), for spelling variant detection. Similar to the Jaccard Index, Proxinette uses similarity features to compute the similarity of two types.…”

Section: Dealing With Spelling Variationmentioning

confidence: 99%

Dealing with word-internal modification and spelling variation in data-driven lemmatization

Barteld

Schröder²,

Zinsmeister

2016

Proceedings of the 10th SIGHUM Workshop on Language Technology For Cultural Heritage, Social Sciences, and Humanities

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This paper describes our contribution to two challenges in data-driven lemmatization. We approach lemmatization in the framework of a two-stage process, where first lemma candidates are generated and afterwards a ranker chooses the most probable lemma from these candidates. The first challenge is that languages with rich morphology like Modern German can feature morphological changes of different kinds, in particular word-internal modification. This makes the generation of the correct lemma a harder task than just removing suffixes (stemming). The second challenge that we address is spelling variation as it appears in non-standard texts. We experiment with different generators that are specifically tailored to deal with these two challenges. We show in an oracle setting that there is a possible increase in lemmatization accuracy of 14% with our methods to generate lemma candidates on Middle Low German, a group of historical dialects of German (1200-1650 AD). Using a log-linear model to choose the correct lemma from the set, we obtain an actual increase of 5.56%.

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Phonotactics in morphological similarity metrics

Cited by 4 publications

References 9 publications

Semi-automatic construction of word-formation networks

Semi-automatic construction of word-formation networks

A Tool for Automatic Scoring of Spelling Performance

Dealing with word-internal modification and spelling variation in data-driven lemmatization

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