Language resources for Hebrew

Itai, Alon; Wintner, Shuly

doi:10.1007/s10579-007-9050-8

Cited by 66 publications

(32 citation statements)

References 14 publications

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“…In this sense it is also in line with other Hebrew morphological analyzers, such as the MILA morphological analyzer of written Hebrew (Itai and Wintner 2008). Consequently, lexical entries are headed by lexemes (rather than root consonants, a design option that is possible in MOR) in a way that reflects the organization of printed dictionaries.…”

Section: Morphological Analysissupporting

confidence: 84%

The Hebrew CHILDES corpus: transcription and morphological analysis

Albert

MacWhinney

Nir

et al. 2013

Lang Resources & Evaluation

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We present a corpus of transcribed spoken Hebrew that reflects spoken interactions between children and adults. The corpus is an integral part of the CHILDES database, which distributes similar corpora for over 25 languages. We introduce a dedicated transcription scheme for the spoken Hebrew data that is sensitive to both the phonology and the standard orthography of the language. We also introduce a morphological analyzer that was specifically developed for this corpus. The analyzer adequately covers the entire corpus, producing detailed correct analyses for all tokens. Evaluation on a new corpus reveals high coverage as well. Finally, we describe a morphological disambiguation module that selects the correct analysis of each token in context. The result is a high-quality morphologically-annotated CHILDES corpus of Hebrew, along with a set of tools that can be applied to new corpora.

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Section: Morphological Analysissupporting

confidence: 84%

The Hebrew CHILDES corpus: transcription and morphological analysis

Albert

MacWhinney

Nir

et al. 2013

Lang Resources & Evaluation

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“…Only the roots are specified, however, and not other (non-root) properties. To obtain other morphological properties, we use the MILA Morphological Analysis tool (MILA-MA) (Itai and Wintner, 2008). Because its morphological knowledge is manually coded and its output deterministic, MILA-MA provides a good approximation to human annotation.…”

mentioning

confidence: 99%

Automatic Detection of Intra-Word Code-Switching

Nguyen¹,

Cornips²

2016

Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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Welcome to the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology. The workshop aims to bring together researchers interested in applying computational techniques to problems in morphology, phonology, and phonetics. Our program this year highlights the ongoing and important interaction between work in computational linguistics and work in theoretical linguistics. We received 23 submissions and accepted 11.The volume of submissions made it necessary to recruit several additional reviewers. We'd like to thank all of these people for agreeing to review papers on what seemed like impossibly short notice.This year also marks the first SIGMORPHON shared task, on morphological reinflection. The shared task received 9 submissions, all of which were accepted, and greatly advanced the state of the art in this area.We thank all the authors, reviewers and organizers for their efforts on behalf of the community. AbstractThis paper conceptualizes speech prosody data mining and its potential application in data-driven phonology/phonetics research. We first conceptualize Speech Prosody Mining (SPM) in a time-series data mining framework. Specifically, we propose using efficient symbolic representations for speech prosody time-series similarity computation. We experiment with both symbolic and numeric representations and distance measures in a series of time-series classification and clustering experiments on a dataset of Mandarin tones. Evaluation results show that symbolic representation performs comparably with other representations at a reduced cost, which enables us to efficiently mine large speech prosody corpora while opening up to possibilities of using a wide range of algorithms that require discrete valued data. We discuss the potential of SPM using time-series mining techniques in future works. IntroductionCurrent investigations on the phonology of intonation and tones (or pitch accent) typically employ data-driven approaches by building research on top of manual annotations of a large amount of speech prosody data (for example, (Morén and Zsiga, 2006; Zsiga and Zec, 2013), and many others). Meanwhile, researchers are also limited by the amount of resources invested in such expensive endeavor of manual annotations. Given this paradox, we believe that this type of data driven approach in phonology-phonetics interface can benefit from tools that can efficiently index, query, classify, cluster, summarize, and discover meaningful prosodic patterns from a large speech prosody corpus.The data mining of f 0 1 (pitch) contour patterns from audio data has recently gained success in the domain of Music Information Retrieval (aka MIR, see (Gulati and Serra, 2014; Gulati et al., 2015; Ganguli, 2015) for examples). In contrast, the data mining of speech prosody f 0 data (here on referred to as Speech Prosody Mining (SPM) 2 ) is a less explored research topic (Raskinis and Kazlauskiene, 2013). Fundamentally, SPM in a large prosody corpus aims at discovering meaningful patterns in the f ...

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“…Translating from Hebrew to Arabic, we use a morphological analyzer (Itai and Wintner 2008) for the Hebrew source, with no morphological disambiguation module. 4 This causes many wrong analyses to be processed and dramatically increases the size of the hypothesis lattice.…”

Section: Morphological Challengesmentioning

confidence: 99%

“…This helps us reduce the amount of hypotheses in the lattice. For generation we use the reverse direction of Itai and Wintner (2008) as a generator, which inflects better for gender than its Arabic counterpart. Due to the morphological disambiguator in Arabic and the generator in Hebrew, translation in this direction currently performs better.…”

Section: Morphological Challengesmentioning

confidence: 99%

“…Specifically, for our Hebrew-to-Arabic system we use a Hebrew morphological analyzer (Itai and Wintner 2008), a medium-sized dictionary, an Arabic morphological generator (Habash 2004), and a tokenized version of the Arabic GigaWord corpus as a language model. We manually constructed a grammar, currently consisting of over 40 rules, 21 of which are NP rules.…”

Section: Transfer-based Translationmentioning

confidence: 99%

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Machine translation between Hebrew and Arabic

Shilon

Habash

Lavie

et al. 2011

Machine Translation

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Hebrew and Arabic are related but mutually incomprehensible languages with complex morphology and scarce parallel corpora. Machine translation between the two languages is therefore interesting and challenging. We discuss similarities and differences between Hebrew and Arabic, the benefits and challenges that they induce, respectively, and their implications on machine translation. We highlight the shortcomings of using English as a pivot language and advocate a direct, transfer-based and linguistically-informed (but still statistical, and hence scalable) approach. We report preliminary results of the two systems we are currently developing, for translation in both directions.

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Language resources for Hebrew

Cited by 66 publications

References 14 publications

The Hebrew CHILDES corpus: transcription and morphological analysis

The Hebrew CHILDES corpus: transcription and morphological analysis

Automatic Detection of Intra-Word Code-Switching

Machine translation between Hebrew and Arabic

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