Morphological modeling

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  • Sediment Transport in Aquatic Environments is a book which covers a wide range of topics. The effective management of many aquatic environments, requires a detailed understanding of sediment dynamics. This has both environmental and economic implications, especially where there is any anthropogenic involvement. Numerical models are often the tool used for predicting the transport and fate of sediment movement in these situations, as they can estimate the various spatial and temporal fluxes.

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  • This paper describes an unsupervised dynamic graphical model for morphological segmentation and bilingual morpheme alignment for statistical machine translation. The model extends Hidden Semi-Markov chain models by using factored output nodes and special structures for its conditional probability distributions. It relies on morpho-syntactic and lexical source-side information (part-of-speech, morphological segmentation) while learning a morpheme segmentation over the target language. Our model outperforms a competitive word alignment system in alignment quality. ...

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  • We present a statistical model of Japanese unknown words consisting of a set of length and spelling models classified by the character types that constitute a word. The point is quite simple: different character sets should be treated differently and the changes between character types are very important because Japanese script has both ideograms like Chinese (kanji) and phonograms like English (katakana). Both word segmentation accuracy and part of speech tagging accuracy are improved by the proposed model. ...

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  • The aim of this work is to present some preliminary results of an investigation in course on the typology of the morphology of the native South American languages from the point of view of the formal language theory. With this object, we give two contrasting examples of descriptions of two Aboriginal languages finite verb forms morphology: Argentinean Quechua (quichua santiague˜ o) and Toba.

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  • This paper presents an exponential model for translation into highly inflected languages which can be scaled to very large datasets. As in other recent proposals, it predicts targetside phrases and can be conditioned on sourceside context. However, crucially for the task of modeling morphological generalizations, it estimates feature parameters from the entire training set rather than as a collection of separate classifiers.

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  • Most previous studies of morphological disambiguation and dependency parsing have been pursued independently. Morphological taggers operate on n-grams and do not take into account syntactic relations; parsers use the “pipeline” approach, assuming that morphological information has been separately obtained. However, in morphologically-rich languages, there is often considerable interaction between morphology and syntax, such that neither can be disambiguated without the other.

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  • We present a global joint model for lemmatization and part-of-speech prediction. Using only morphological lexicons and unlabeled data, we learn a partiallysupervised part-of-speech tagger and a lemmatizer which are combined using features on a dynamically linked dependency structure of words. We evaluate our model on English, Bulgarian, Czech, and Slovene, and demonstrate substantial improvements over both a direct transduction approach to lemmatization and a pipelined approach, which predicts part-of-speech tags before lemmatization. ...

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  • Morphological processes in Semitic languages deliver space-delimited words which introduce multiple, distinct, syntactic units into the structure of the input sentence. These words are in turn highly ambiguous, breaking the assumption underlying most parsers that the yield of a tree for a given sentence is known in advance. Here we propose a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.

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  • We present several unsupervised statistical models for the prepositional phrase attachment task that approach the accuracy of the best supervised methods for this task. Our unsupervised approach uses a heuristic based on attachment proximity and trains from raw text that is annotated with only part-of-speech tags and morphological base forms, as opposed to attachment information. It is therefore less resource-intensive and more portable than previous corpus-based algorithm proposed for this task. ...

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  • We present experiments with part-ofspeech tagging for Bulgarian, a Slavic language with rich inflectional and derivational morphology. Unlike most previous work, which has used a small number of grammatical categories, we work with 680 morpho-syntactic tags. We combine a large morphological lexicon with prior linguistic knowledge and guided learning from a POS-annotated corpus, achieving accuracy of 97.98%, which is a significant improvement over the state-of-the-art for Bulgarian.

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  • Morphological lexica are often implemented on top of morphological paradigms, corresponding to different ways of building the full inflection table of a word. Computationally precise lexica may use hundreds of paradigms, and it can be hard for a lexicographer to choose among them. To automate this task, this paper introduces the notion of a smart paradigm. It is a metaparadigm, which inspects the base form and tries to infer which low-level paradigm applies. If the result is uncertain, more forms are given for discrimination.

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  • We present a class-based language model that clusters rare words of similar morphology together. The model improves the prediction of words after histories containing outof-vocabulary words. The morphological features used are obtained without the use of labeled data. The perplexity improvement compared to a state of the art Kneser-Ney model is 4% overall and 81% on unknown histories.

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  • In morphologically rich languages such as Arabic, the abundance of word forms resulting from increased morpheme combinations is significantly greater than for languages with fewer inflected forms (Kirchhoff et al., 2006). This exacerbates the out-of-vocabulary (OOV) problem. Test set words are more likely to be unknown, limiting the effectiveness of the model. The goal of this study is to use the regularities of Arabic inflectional morphology to reduce the OOV problem in that language.

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  • Morphological segmentation has been shown to be beneficial to a range of NLP tasks such as machine translation, speech recognition, speech synthesis and information retrieval. Recently, a number of approaches to unsupervised morphological segmentation have been proposed. This paper describes an algorithm that draws from previous approaches and combines them into a simple model for morphological segmentation that outperforms other approaches on English and German, and also yields good results on agglutinative languages such as Finnish and Turkish. ...

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  • This paper describes our work on building Part-of-Speech (POS) tagger for Bengali. We have use Hidden Markov Model (HMM) and Maximum Entropy (ME) based stochastic taggers. Bengali is a morphologically rich language and our taggers make use of morphological and contextual information of the words. Since only a small labeled training set is available (45,000 words), simple stochastic approach does not yield very good results. In this work, we have studied the effect of using a morphological analyzer to improve the performance of the tagger. ...

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  • The major obstacle in morphological (sometimes called morpho-syntactic, or extended POS) tagging of highly inflective languages, such as Czech or Russian, is - given the resources possibly available - the tagset size. Typically, it is in the order of thousands. Our method uses an exponential probabilistic model based on automatically selected features. The parameters of the model are computed using simple estimates (which makes training much faster than when one uses Maximum Entropy) to directly minimize the error rate on training data.

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  • This paper discusses the supervised learning of morphology using stochastic transducers, trained using the ExpectationMaximization (EM) algorithm. Two approaches are presented: first, using the transducers directly to model the process, and secondly using them to define a similarity measure, related to the Fisher kernel method (Jaakkola and Haussler, 1998), and then using a Memory-Based Learning (MBL) technique. These are evaluated and compared on data sets from English, German, Slovene and Arabic. ...

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  • Morphological analysis must take into account the spelling-change processes of a language as well as its possible configurations of stems, affixes, and inflectional markings. The computational difficultyof the task can be clarified by investigating specific models of morphological processing. The use of finite-state machinery in the "twolevel" model by K i m m o Koskenniemi gives it the appearance of computational efficiency, but closer examination shows the model does not guarantee efficient processing.

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  • We propose a backoff model for phrasebased machine translation that translates unseen word forms in foreign-language text by hierarchical morphological abstractions at the word and the phrase level. The model is evaluated on the Europarl corpus for German-English and FinnishEnglish translation and shows improvements over state-of-the-art phrase-based models.

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  • Morphologically complex terms composed from Greek or Latin elements are frequent in scientific and technical texts. Word forming units are thus relevant cues for the identification of terms in domainspecific texts. This article describes a method for the automatic extraction of terms relying on the detection of classical prefixes and word-initial combining forms. Word-forming units are identified using a regular expression. The system then extracts terms by selecting words which either begin or coalesce with these elements. ...

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