Corpus-based lexeme ranking for morphological guessers

Peer reviewe

Building a Finnish SOM-based ontology concept tagger and harvester

Kehitän luonnollisessa kielessä ilmenevien sanojen merkitysten eroteluun sopivaa automaatista koneoppivaa työkalua. Laskennallinen malli perustuu itseoppivaan kartaan (SOM, Self-Organizing Map) ja annetuun suomenkieliseen semantisen webin ontologiaan. Malli oppii tunnistamaan käsiteiden ilmenemistä mallitekstistä, johon on annotoitu (tagatu) malliksi aiemmin laaditun ongologian käsiteitä. Koe liityy aiemmin englanninkielisten käsiteiden taggaamiseen liityvään OntoR-koejärjestelyyn joka tutki tekstisyöteessä ilmenevien termien liitämistä SOM-kartan soluihin malliksi annetun annotoidun tekstiesimerkin avulla. Tällainen malli oppii annetun käsitemallin huomatavan niukalla esimerkkiaineistolla ja sopii käytökohteisiin joissa ei ole tarjolla riitävän suurta datamäärää syvän oppimisen neuroverkkomallin opetamiseksi. Suomenkielisen kokeen morfologisen analyysin pohjalla on OMORFI- ja HFST-työkalut. Koneoppimisen toteutava SOM-karta lasketaan SOM-PAK-ohjelmistopaketin avulla. Kehitetyä laskennallista mallia käytetään käsiteiden tunnistamisen lisäksi myös uusien ontologiakäsiteiden ehdokkaiden löytämiseksi

Exploring Linguistic Constraints in Nlp Applications

The key argument of this dissertation is that the success of an Natural Language Processing (NLP) application depends on a proper representation of the corresponding linguistic problem. This theme is raised in the context that the recent progress made in our field is widely credited to the effective use of strong engineering techniques. However, the intriguing power of highly lexicalized models shown in many NLP applications is not only an achievement by the development in machine learning, but also impossible without the extensive hand-annotated data resources made available, which are originally built with very deep linguistic considerations. More specifically, we explore three linguistic aspects in this dissertation: the distinction between closed-class vs. open-class words, long-tail distributions in vocabulary study and determinism in language models. The first two aspects are studied in unsupervised tasks, unsupervised part-of-speech (POS) tagging and morphology learning, and the last one is studied in supervised tasks, English POS tagging and Chinese word segmentation. Each linguistic aspect under study manifests itself in a (different) way to help improve performance or efficiency in some NLP application

Szösszenet az elveszett morfémákért : az alaki analógiák haszna

A jelenlegi morfológiai elemzők gyakorlati okok miatt elég pragmatikus módon készültek. A céljuk, aránylag kis munkával fedjék le a magyar nyelvű szövegeinek szóalakjait minél kevesebb hibával. Ha a célt elérték, a szabályszerű eseteket jól leírták, a deviáns, kisebb gyakorisággal előforduló eseteket kivételként, egyedileg kezelik. A vizsgálataim szerint sokkal kevesebb kivétel van. A szavak végződése szerinti csoportosítással felderíthetők azok a szavak közötti összefüggések, melyek a korábbi adatbázisokból hiányoznak. A módszer segítségével elfeledett vagy csak leíró nyelvészek által említett szógyökök, toldalékok kerülnek napvilágra. Sőt a feltárás eredményeként pontosíthatóak a praktikus célra készült nyelvészeti, nyelvi tárak. The current morphological analyzers have been designed pragmatically for practical purposes. Their goal is to cover the word forms in Hungarian texts with relatively little effort and with as few mistakes as possible. Once the goal has been achieved, regular case affixes, marks, and verbal conjugation endings are well described in a formal way, but most derivative affixes and rare case suffixes are treated individually as exceptions. In my research, I found that there are far fewer exceptional word forms in Hungarian. By clustering word forms by their endings, new relationships, new roots, new morphemes can be discovered that are missing from earlier databases. By clustering word forms by their endings, new relationships among roots, morphemes can be discovered that are missing from earlier databases. One can simplify morphological descriptions without limiting their power. Even a complete morphological description of an unknown language can be generated based on a large corpus solely. Moreover, if not only similarities of endings, but clusters of ending patterns are used to group word forms, then many hidden word roots and suffixes can be discovered that have been forgotten altogether, or mentioned only by descriptive linguists. As a result of the method, semantic dependences might be discovered, and linguistic collections, databases made for practical purposes can be corrected, improved as well

Molecular variability of different species of cyathostomins in selected hosts

Dissertação de Mestrado Integrado em Medicina VeterináriaCyathostomins are important intestinal nematode parasites of equids and include more than 50 accepted species. Their taxonomy has been frequently revised and sometimes the presence of cryptic species was suggested. Besides, usually molecular- and morphology-based phylogenetic analyses give divergent results. In this study, the nucleotide sequences of the second internal transcribed spacer (ITS-2) and cytochrome c oxidase subunit I (COI) of nuclear ribosomal DNA and mitochondrial DNA, respectively, were determined for adults of six cyathostomin species (Cylicocyclus nassatus, Coronocyclus labiatus, Coronocyclus coronatus, Cylicostephanus longibursatus, Cylicostephanus minutus, Cylicostephanus calicatus) from hosts of two geographic regions (domestic horses from Germany; a Przewalski horse, a donkey, a kulan, a zebra and a horse from Ukraine). Maximum likelihood trees were calculated for ITS-2, COI and combined data sets. No obvious differentiation was detected between geographic regions, nor equine host species. The ITS-2 was unable to separate between Coronocyclus coronatus and some Cylicostephanus calicatus sequences. Although COI sequence-based analysis easily distinguished them, they also revealed a close relationship between these two species. Cryptic species were detected in Cylicostephanus minutus and Cylicostephanus calicatus. Cylicocyclus nassatus and Coronocyclus labiatus showed diverse and mixed mitochondrial and nuclear haplotypes, while Cylicostephanus longibursatus was comparatively homogenous. In conclusion, combined analysis of nuclear and mitochondrial haplotypes from the same specimen, improved resolution of analyses and should be applied to more species and hosts from various geographic regions.RESUMO - Variabilidade molecular de diferentes espécies de ciatostomíneos em hospedeiros selecionados - Os ciatostomíneos são parasitas nematodes intestinais importantes dos equídeos e incluem mais de 50 espécies reconhecidas como tal. A sua taxonomia tem sido frequentemente revista e a presença de espécies crípticas foi sugerida algumas vezes. Além disso, análises filogenéticas baseadas na morfologia ou sequências moleculares dão, por norma, resultados divergentes. Neste estudo, as sequências nucleotídicas do segundo espaçador interno transcrito (ITS-2) e citocromo c oxidase I (COI) do DNA ribossómico nuclear e do DNA mitocondrial, respetivamente, foram determinadas para adultos de seis espécies de ciatostomíneos (Cylicocyclus nassatus, Coronocyclus labiatus, Coronocyclus coronatus, Cylicostephanus longibursatus, Cylicostephanus minutus, Cylicostephanus calicatus) de hospedeiros de duas regiões geográficas (cavalos domésticos da Alemanha; um Przewalski, um burro, um kulan, uma zebra e um cavalo da Ucrânia). As árvores de máxima verossimilhança foram calculadas para conjuntos de dados de ITS-2, COI e combinados. Não foi detetada nenhuma diferenciação óbvia entre regiões geográficas, nem entre espécies hospedeiras equinas. O ITS-2 foi incapaz de separar Coronocyclus coronatus e algumas sequências de Cylicostephanus calicatus. Embora as sequências de COI as distinguissem facilmente, revelaram também uma estreita relação entre essas duas espécies. Para Cylicostephanus minutus e Cylicostephanus calicatus foram detetadas espécies crípticas. Cylicocyclus nassatus e Coronocyclus labiatus apresentaram diversos haplótipos mitocondriais e nucleares, enquanto que Cylicostephanus longibursatus foi comparativamente mais homogéneo. Em conclusão, a análise combinada de haplótipos nucleares e mitocondriais das mesmas espécies, melhorou a resolução das análises e deve ser aplicada a mais espécies e hospedeiros de várias regiões geográficas.N/

Lexikos 18 (AFRILEX-reeks/series 18: 2008): 303-318 Improving the Computational Morphological Analysis of a Swahili Corpus for Lexicographic Purposes *

Abstract: Computational morphological analysis is an important first step in the automatic treatment of natural language and a useful lexicographic tool. This article describes a corpus-based approach to the morphological analysis of Swahili. We particularly focus our discussion on its ability to retrieve lemmas for word forms and evaluate it as a tool for corpus-based dictionary compilation

General methods for fine-grained morphological and syntactic disambiguation

We present methods for improved handling of morphologically rich languages (MRLS) where we define MRLS as languages that are morphologically more complex than English. Standard algorithms for language modeling, tagging and parsing have problems with the productive nature of such languages. Consider for example the possible forms of a typical English verb like work that generally has four four different forms: work, works, working and worked. Its Spanish counterpart trabajar has 6 different forms in present tense: trabajo, trabajas, trabaja, trabajamos, trabajáis and trabajan and more than 50 different forms when including the different tenses, moods (indicative, subjunctive and imperative) and participles. Such a high number of forms leads to sparsity issues: In a recent Wikipedia dump of more than 400 million tokens we find that 20 of these forms occur only twice or less and that 10 forms do not occur at all. This means that even if we only need unlabeled data to estimate a model and even when looking at a relatively common and frequent verb, we do not have enough data to make reasonable estimates for some of its forms. However, if we decompose an unseen form such as trabajaréis `you will work', we find that it is trabajar in future tense and second person plural. This allows us to make the predictions that are needed to decide on the grammaticality (language modeling) or syntax (tagging and parsing) of a sentence. In the first part of this thesis, we develop a morphological language model. A language model estimates the grammaticality and coherence of a sentence. Most language models used today are word-based n-gram models, which means that they estimate the transitional probability of a word following a history, the sequence of the (n - 1) preceding words. The probabilities are estimated from the frequencies of the history and the history followed by the target word in a huge text corpus. If either of the sequences is unseen, the length of the history has to be reduced. This leads to a less accurate estimate as less context is taken into account. Our morphological language model estimates an additional probability from the morphological classes of the words. These classes are built automatically by extracting morphological features from the word forms. To this end, we use unsupervised segmentation algorithms to find the suffixes of word forms. Such an algorithm might for example segment trabajaréis into trabaja and réis and we can then estimate the properties of trabajaréis from other word forms with the same or similar morphological properties. The data-driven nature of the segmentation algorithms allows them to not only find inflectional suffixes (such as -réis), but also more derivational phenomena such as the head nouns of compounds or even endings such as -tec, which identify technology oriented companies such as Vortec, Memotec and Portec and would not be regarded as a morphological suffix by traditional linguistics. Additionally, we extract shape features such as if a form contains digits or capital characters. This is important because many rare or unseen forms are proper names or numbers and often do not have meaningful suffixes. Our class-based morphological model is then interpolated with a word-based model to combine the generalization capabilities of the first and the high accuracy in case of sufficient data of the second. We evaluate our model across 21 European languages and find improvements between 3% and 11% in perplexity, a standard language modeling evaluation measure. Improvements are highest for languages with more productive and complex morphology such as Finnish and Estonian, but also visible for languages with a relatively simple morphology such as English and Dutch. We conclude that a morphological component yields consistent improvements for all the tested languages and argue that it should be part of every language model. Dependency trees represent the syntactic structure of a sentence by attaching each word to its syntactic head, the word it is directly modifying. Dependency parsing is usually tackled using heavily lexicalized (word-based) models and a thorough morphological preprocessing is important for optimal performance, especially for MRLS. We investigate if the lack of morphological features can be compensated by features induced using hidden Markov models with latent annotations (HMM-LAs) and find this to be the case for German. HMM-LAs were proposed as a method to increase part-of-speech tagging accuracy. The model splits the observed part-of-speech tags (such as verb and noun) into subtags. An expectation maximization algorithm is then used to fit the subtags to different roles. A verb tag for example might be split into an auxiliary verb and a full verb subtag. Such a split is usually beneficial because these two verb classes have different contexts. That is, a full verb might follow an auxiliary verb, but usually not another full verb. For German and English, we find that our model leads to consistent improvements over a parser not using subtag features. Looking at the labeled attachment score (LAS), the number of words correctly attached to their head, we observe an improvement from 90.34 to 90.75 for English and from 87.92 to 88.24 for German. For German, we additionally find that our model achieves almost the same performance (88.24) as a model using tags annotated by a supervised morphological tagger (LAS of 88.35). We also find that the German latent tags correlate with morphology. Articles for example are split by their grammatical case. We also investigate the part-of-speech tagging accuracies of models using the traditional treebank tagset and models using induced tagsets of the same size and find that the latter outperform the former, but are in turn outperformed by a discriminative tagger. Furthermore, we present a method for fast and accurate morphological tagging. While part-of-speech tagging annotates tokens in context with their respective word categories, morphological tagging produces a complete annotation containing all the relevant inflectional features such as case, gender and tense. A complete reading is represented as a single tag. As a reading might consist of several morphological features the resulting tagset usually contains hundreds or even thousands of tags. This is an issue for many decoding algorithms such as Viterbi which have runtimes depending quadratically on the number of tags. In the case of morphological tagging, the problem can be avoided by using a morphological analyzer. A morphological analyzer is a manually created finite-state transducer that produces the possible morphological readings of a word form. This analyzer can be used to prune the tagging lattice and to allow for the application of standard sequence labeling algorithms. The downside of this approach is that such an analyzer is not available for every language or might not have the coverage required for the task. Additionally, the output tags of some analyzers are not compatible with the annotations of the treebanks, which might require some manual mapping of the different annotations or even to reduce the complexity of the annotation. To avoid this problem we propose to use the posterior probabilities of a conditional random field (CRF) lattice to prune the space of possible taggings. At the zero-order level the posterior probabilities of a token can be calculated independently from the other tokens of a sentence. The necessary computations can thus be performed in linear time. The features available to the model at this time are similar to the features used by a morphological analyzer (essentially the word form and features based on it), but also include the immediate lexical context. As the ambiguity of word types varies substantially, we just fix the average number of readings after pruning by dynamically estimating a probability threshold. Once we obtain the pruned lattice, we can add tag transitions and convert it into a first-order lattice. The quadratic forward-backward computations are now executed on the remaining plausible readings and thus efficient. We can now continue pruning and extending the lattice order at a relatively low additional runtime cost (depending on the pruning thresholds). The training of the model can be implemented efficiently by applying stochastic gradient descent (SGD). The CRF gradient can be calculated from a lattice of any order as long as the correct reading is still in the lattice. During training, we thus run the lattice pruning until we either reach the maximal order or until the correct reading is pruned. If the reading is pruned we perform the gradient update with the highest order lattice still containing the reading. This approach is similar to early updating in the structured perceptron literature and forces the model to learn how to keep the correct readings in the lower order lattices. In practice, we observe a high number of lower updates during the first training epoch and almost exclusively higher order updates during later epochs. We evaluate our CRF tagger on six languages with different morphological properties. We find that for languages with a high word form ambiguity such as German, the pruning results in a moderate drop in tagging accuracy while for languages with less ambiguity such as Spanish and Hungarian the loss due to pruning is negligible. However, our pruning strategy allows us to train higher order models (order > 1), which give substantial improvements for all languages and also outperform unpruned first-order models. That is, the model might lose some of the correct readings during pruning, but is also able to solve more of the harder cases that require more context. We also find our model to substantially and significantly outperform a number of frequently used taggers such as Morfette and SVMTool. Based on our morphological tagger we develop a simple method to increase the performance of a state-of-the-art constituency parser. A constituency tree describes the syntactic properties of a sentence by assigning spans of text to a hierarchical bracket structure. developed a language-independent approach for the automatic annotation of accurate and compact grammars. Their implementation -- known as the Berkeley parser -- gives state-of-the-art results for many languages such as English and German. For some MRLS such as Basque and Korean, however, the parser gives unsatisfactory results because of its simple unknown word model. This model maps unknown words to a small number of signatures (similar to our morphological classes). These signatures do not seem expressive enough for many of the subtle distinctions made during parsing. We propose to replace rare words by the morphological reading generated by our tagger instead. The motivation is twofold. First, our tagger has access to a number of lexical and sublexical features not available during parsing. Second, we expect the morphological readings to contain most of the information required to make the correct parsing decision even though we know that things such as the correct attachment of prepositional phrases might require some notion of lexical semantics. In experiments on the SPMRL 2013 dataset of nine MRLS we find our method to give improvements for all languages except French for which we observe a minor drop in the Parseval score of 0.06. For Hebrew, Hungarian and Basque we find substantial absolute improvements of 5.65, 11.87 and 15.16, respectively. We also performed an extensive evaluation on the utility of word representations for morphological tagging. Our goal was to reduce the drop in performance that is caused when a model trained on a specific domain is applied to some other domain. This problem is usually addressed by domain adaption (DA). DA adapts a model towards a specific domain using a small amount of labeled or a huge amount of unlabeled data from that domain. However, this procedure requires us to train a model for every target domain. Instead we are trying to build a robust system that is trained on domain-specific labeled and domain-independent or general unlabeled data. We believe word representations to be key in the development of such models because they allow us to leverage unlabeled data efficiently. We compare data-driven representations to manually created morphological analyzers. We understand data-driven representations as models that cluster word forms or map them to a vectorial representation. Examples heavily used in the literature include Brown clusters, Singular Value Decompositions of count vectors and neural-network-based embeddings. We create a test suite of six languages consisting of in-domain and out-of-domain test sets. To this end we converted annotations for Spanish and Czech and annotated the German part of the Smultron treebank with a morphological layer. In our experiments on these data sets we find Brown clusters to outperform the other data-driven representations. Regarding the comparison with morphological analyzers, we find Brown clusters to give slightly better performance in part-of-speech tagging, but to be substantially outperformed in morphological tagging