Learning Phonological Categories

Goldsmith, John; Xanthos, Aris

doi:10.1353/lan.0.0100

Cited by 37 publications

(61 citation statements)

References 23 publications

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“…It is possible that below this threshold Sukhotin's algorithm would still be preferable. Goldsmith and Xanthos (2009) only evaluate their method on one collection of written words, sampled from Finnish, 7 and they obtain the same result as we do below, with our algorithm only misclassifying the grapheme 'q'. 8 This should come as no surprise, because their method is an algebraically very close variant of ours -they compute eigenvectors on the Gram closure of our grapheme/context matrix (which they call F ) instead of a singular value decomposition directly.…”

Section: A Spectral Universal Over Alphabetsmentioning

confidence: 98%

“…While the premise that someone would have phonemically transcribed a text without knowing by the end which phones were vowels or consonants may seem far-fetched, Goldsmith and Xanthos (2009) draw some important conclusions for a subsequent analysis of vowel-harmonic processes that we shall not investigate further here. Goldsmith and Xanthos (2009) also cite Sukhotin (1962), whose method we evaluate below, as a precedent for their own study, possibly influenced by Guy's (1991) English gloss of Sukhotin's work, which misrepresents Sukhotin's (1962) intention as seeking to classify letters in a substitution cipher as vowels or consonants. Sukhotin's (1962) study, which was originally written in Russian, is in fact about the written form (bukv) of plaintext letters, not of ciphers nor of the sounds of speech.…”

Section: Introductionmentioning

confidence: 99%

“…Another notable antecedent is Goldsmith and Xanthos (2009), who discovered essentially the same method for vowel-consonant separation in the context of spectrally analyzing phonemic transcriptions. While the premise that someone would have phonemically transcribed a text without knowing by the end which phones were vowels or consonants may seem far-fetched, Goldsmith and Xanthos (2009) draw some important conclusions for a subsequent analysis of vowel-harmonic processes that we shall not investigate further here.…”

Section: Introductionmentioning

confidence: 99%

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Proceedings of the First Workshop on Subword and Character Level Models in NLP

2017

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IntroductionTraditional NLP starts with a hand-engineered layer of representation, the level of tokens or words. A tokenization component first breaks up the text into units using manually designed rules. Tokens are then processed by components such as word segmentation, morphological analysis and multiword recognition. The heterogeneity of these components makes it hard to create integrated models of both structure within tokens (e.g., morphology) and structure across multiple tokens (e.g., multi-word expressions). This approach can perform poorly (i) for morphologically rich languages, (ii) for noisy text, (iii) for languages in which the recognition of words is difficult and (iv) for adaptation to new domains; and (v) it can impede the optimization of preprocessing in end-to-end learning.The workshop provides a forum for discussing recent advances as well as future directions on sub-word and character-level natural language processing and representation learning that address these problems.We received 37 submissions, out of which we accepted 24 as papers and 4 as extended abstracts. AbstractMost of neural language models use different kinds of embeddings for word prediction. While word embeddings can be associated to each word in the vocabulary or derived from characters as well as factored morphological decomposition, these word representations are mainly used to parametrize the input, i.e. the context of prediction. This work investigates the effect of using subword units (character and factored morphological decomposition) to build output representations for neural language modeling. We present a case study on Czech, a morphologically-rich language, experimenting with different input and output representations. When working with the full training vocabulary, despite unstable training, our experiments show that augmenting the output word representations with character-based embeddings can significantly improve the performance of the model. Moreover, reducing the size of the output look-up table, to let the character-based embeddings represent rare words, brings further improvement. IntroductionMost of neural language models, such as n-gram models (Bengio et al., 2003) are word based and rely on the definition of a finite vocabulary V. Therefore, a look-up table maps each wordw ∈ V to a vector of real features, and is stored in a matrix. While this approach yields significant improvement for a variety of tasks and languages, see for instance (Schwenk, 2007) in speech recognition and (Le et al., 2012; Devlin et al., 2014; in machine translation, it induces several limitations.For morphologically-rich languages, like Czech or German, the lexical coverage is still an important issue, since there is a combinatorial explosion of word forms, most of which are hardly observed on training data. On the one hand, growing the look-up table is not a solution, since it would increase the number of parameters without having enough training examples for a proper estimation. On the other hand, rare words can be replaced...

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Section: A Spectral Universal Over Alphabetsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Proceedings of the First Workshop on Subword and Character Level Models in NLP

2017

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“…Au début du 21 ème siècle, le caractère empirique de la science linguistique (Goldsmith, 2010) se voit réaffirmé dans tous les domaines, des Grammaires de Constructions aux Grammaires d'Usages (Langacker, 2000) en passant par les modèles exemplaristes ou occurrentialistes (Bybee, 2006). La psycholinguistique, comme l'acquisition, sont directement concernées (Tomasello, , 2008 et de nouvelles approches acquisitionnelles, basées uniquement sur les données disponibles, sont proposées (Goldsmith & Xanthos, 2009). Après 40 ans de domination de l'exemplum, le datum fait enfin retour sur la scène métho-dologique.…”

Section: Corpus Et Théorie Linguistiqueunclassified

La liaison : approches contemporaines

2014

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“…For example, in our corpus (Schweitzer et al 2004) fewer than 20% of the 1 Related work by Xanthos (Goldsmith and Xanthos 2009) explores a range of methods for establishing whether it is possible to automatically infer whether segments in a data sample are vowels or consonants (in addition to examining vowel harmony and phonotactic induction). Segmentation, however, is not the focus.…”

Section: Approaches To Segmentation Based On Representations Of the Amentioning

confidence: 99%

A Computational Model of Unsupervised Speech Segmentation for Correspondence Learning

Duran

Schütze

Möbius

et al. 2010

Res on Lang and Comput

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In this paper, we develop a new conceptual framework for an important problem in language acquisition, the correspondence problem: the fact that a given utterance has different manifestations in the speech and articulation of different speakers and that the correspondence of these manifestations is difficult to learn. We put forward the Correspondence-by-Segmentation Hypothesis, which states that correspondence is primarily learned by first segmenting speech in an unsupervised manner and then mapping the acoustics of different speakers onto each other. We show that a rudimentary segmentation of speech can be learned in an unsupervised fashion. We then demonstrate that, using the previously learned segmentation, different instances of a word can be mapped onto each other with high accuracy when trained on utterance-label pairs for a small set of words.

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Learning Phonological Categories

Cited by 37 publications

References 23 publications

Proceedings of the First Workshop on Subword and Character Level Models in NLP

Proceedings of the First Workshop on Subword and Character Level Models in NLP

La liaison : approches contemporaines

A Computational Model of Unsupervised Speech Segmentation for Correspondence Learning

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