An Unsolicited Soliloquy on Dependency Parsing

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] This thesis presents work on dependency parsing covering two distinct lines of research. The first aims to develop efficient parsers so that they can be fast enough to parse large amounts of data while still maintaining decent accuracy. We investigate two techniques to achieve this. The first is a cognitively-inspired method and the second uses a model distillation method. The first technique proved to be utterly dismal, while the second was somewhat of a success. The second line of research presented in this thesis evaluates parsers. This is also done in two ways. We aim to evaluate what causes variation in parsing performance for different algorithms and also different treebanks. This evaluation is grounded in dependency displacements (the directed distance between a dependent and its head) and the subsequent distributions associated with algorithms and the distributions found in treebanks. This work sheds some light on the variation in performance for both different algorithms and different treebanks. And the second part of this area focuses on the utility of part-of-speech tags when used with parsing systems and questions the standard position of assuming that they might help but they certainly won’t hurt.[Resumen] Esta tesis presenta trabajo sobre análisis de dependencias que cubre dos líneas de investigación distintas. La primera tiene como objetivo desarrollar analizadores eficientes, de modo que sean suficientemente rápidos como para analizar grandes volúmenes de datos y, al mismo tiempo, sean suficientemente precisos. Investigamos dos métodos. El primero se basa en teorías cognitivas y el segundo usa una técnica de destilación. La primera técnica resultó un enorme fracaso, mientras que la segunda fue en cierto modo un ´éxito. La otra línea evalúa los analizadores sintácticos. Esto también se hace de dos maneras. Evaluamos la causa de la variación en el rendimiento de los analizadores para distintos algoritmos y corpus. Esta evaluación utiliza la diferencia entre las distribuciones del desplazamiento de arista (la distancia dirigida de las aristas) correspondientes a cada algoritmo y corpus. También evalúa la diferencia entre las distribuciones del desplazamiento de arista en los datos de entrenamiento y prueba. Este trabajo esclarece las variaciones en el rendimiento para algoritmos y corpus diferentes. La segunda parte de esta línea investiga la utilidad de las etiquetas gramaticales para los analizadores sintácticos.[Resumo] Esta tese presenta traballo sobre análise sintáctica, cubrindo dúas liñas de investigación. A primeira aspira a desenvolver analizadores eficientes, de maneira que sexan suficientemente rápidos para procesar grandes volumes de datos e á vez sexan precisos. Investigamos dous métodos. O primeiro baséase nunha teoría cognitiva, e o segundo usa unha técnica de destilación. O primeiro método foi un enorme fracaso, mentres que o segundo foi en certo modo un éxito. A outra liña avalúa os analizadores sintácticos. Esto tamén se fai de dúas maneiras. Avaliamos a causa da variación no rendemento dos analizadores para distintos algoritmos e corpus. Esta avaliaci´on usa a diferencia entre as distribucións do desprazamento de arista (a distancia dirixida das aristas) correspondentes aos algoritmos e aos corpus. Tamén avalía a diferencia entre as distribucións do desprazamento de arista nos datos de adestramento e proba. Este traballo esclarece as variacións no rendemento para algoritmos e corpus diferentes. A segunda parte desta liña investiga a utilidade das etiquetas gramaticais para os analizadores sintácticos.This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150) and from the Centro de Investigación de Galicia (CITIC) which is funded by the Xunta de Galicia and the European Union (ERDF - Galicia 2014-2020 Program) by grant ED431G 2019/01.Xunta de Galicia; ED431G 2019/0

Viability of Sequence Labeling Encodings for Dependency Parsing

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] This thesis presents new methods for recasting dependency parsing as a sequence labeling task yielding a viable alternative to the traditional transition- and graph-based approaches. It is shown that sequence labeling parsers provide several advantages for dependency parsing, such as: (i) a good trade-off between accuracy and parsing speed, (ii) genericity which enables running a parser in generic sequence labeling software and (iii) pluggability which allows using full parse trees as features to downstream tasks. The backbone of dependency parsing as sequence labeling are the encodings which serve as linearization methods for mapping dependency trees into discrete labels, such that each token in a sentence is associated with a label. We introduce three encoding families comprising: (i) head selection, (ii) bracketing-based and (iii) transition-based encodings which are differentiated by the way they represent a dependency tree as a sequence of labels. We empirically examine the viability of the encodings and provide an analysis of their facets. Furthermore, we explore the feasibility of leveraging external complementary data in order to enhance parsing performance. Our sequence labeling parser is endowed with two kinds of representations. First, we exploit the complementary nature of dependency and constituency parsing paradigms and enrich the parser with representations from both syntactic abstractions. Secondly, we use human language processing data to guide our parser with representations from eye movements. Overall, the results show that recasting dependency parsing as sequence labeling is a viable approach that is fast and accurate and provides a practical alternative for integrating syntax in NLP tasks.[Resumen] Esta tesis presenta nuevos métodos para reformular el análisis sintáctico de dependencias como una tarea de etiquetado secuencial, lo que supone una alternativa viable a los enfoques tradicionales basados en transiciones y grafos. Se demuestra que los analizadores de etiquetado secuencial ofrecen varias ventajas para el análisis sintáctico de dependencias, como por ejemplo (i) un buen equilibrio entre la precisión y la velocidad de análisis, (ii) la genericidad que permite ejecutar un analizador en un software genérico de etiquetado secuencial y (iii) la conectividad que permite utilizar el árbol de análisis completo como características para las tareas posteriores. El pilar del análisis sintáctico de dependencias como etiquetado secuencial son las codificaciones que sirven como métodos de linealización para transformar los árboles de dependencias en etiquetas discretas, de forma que cada token de una frase se asocia con una etiqueta. Introducimos tres familias de codificación que comprenden: (i) selección de núcleos, (ii) codificaciones basadas en corchetes y (iii) codificaciones basadas en transiciones que se diferencian por la forma en que representan un árbol de dependencias como una secuencia de etiquetas. Examinamos empíricamente la viabilidad de las codificaciones y ofrecemos un análisis de sus facetas. Además, exploramos la viabilidad de aprovechar datos complementarios externos para mejorar el rendimiento del análisis sintáctico. Dotamos a nuestro analizador sintáctico de dos tipos de representaciones. En primer lugar, explotamos la naturaleza complementaria de los paradigmas de análisis sintáctico de dependencias y constituyentes, enriqueciendo el analizador sintáctico con representaciones de ambas abstracciones sintácticas. En segundo lugar, utilizamos datos de procesamiento del lenguaje humano para guiar nuestro analizador con representaciones de los movimientos oculares. En general, los resultados muestran que la reformulación del análisis sintáctico de dependencias como etiquetado de secuencias es un enfoque viable, rápido y preciso, y ofrece una alternativa práctica para integrar la sintaxis en las tareas de PLN.[Resumo] Esta tese presenta novos métodos para reformular a análise sintáctica de dependencias como unha tarefa de etiquetaxe secuencial, o que supón unha alternativa viable aos enfoques tradicionais baseados en transicións e grafos. Demóstrase que os analizadores de etiquetaxe secuencial ofrecen varias vantaxes para a análise sintáctica de dependencias, por exemplo (i) un bo equilibrio entre a precisión e a velocidade de análise, (ii) a xenericidade que permite executar un analizador nun software xenérico de etiquetaxe secuencial e (iii) a conectividade que permite empregar a árbore de análise completa como características para as tarefas posteriores. O piar da análise sintáctica de dependencias como etiquetaxe secuencial son as codificacións que serven como métodos de linealización para transformar as árbores de dependencias en etiquetas discretas, de forma que cada token dunha frase se asocia cunha etiqueta. Introducimos tres familias de codificación que comprenden: (i) selección de núcleos, (ii) codificacións baseadas en corchetes e (iii) codificacións baseadas en transicións que se diferencian pola forma en que representan unha árbore de dependencia como unha secuencia de etiquetas. Examinamos empíricamente a viabilidade das codificacións e ofrecemos unha análise das súas facetas. Ademais, exploramos a viabilidade de aproveitar datos complementarios externos para mellorar o rendemento da análise sintáctica. O noso analizador sintáctico de etiquetaxe secuencial está dotado de dous tipos de representacións. En primeiro lugar, explotamos a natureza complementaria dos paradigmas de análise sintáctica de dependencias e constituíntes e enriquecemos o analizador sintáctico con representacións de ambas abstraccións sintácticas. En segundo lugar, empregamos datos de procesamento da linguaxe humana para guiar o noso analizador con representacións dos movementos oculares. En xeral, os resultados mostran que a reformulación da análise sintáctico de dependencias como etiquetaxe de secuencias é un enfoque viable, rápido e preciso, e ofrece unha alternativa práctica para integrar a sintaxe nas tarefas de PLN.This work has been carried out thanks to the funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150)

Character-based Neural Semantic Parsing

Humans and computers do not speak the same language. A lot of day-to-day tasks would be vastly more efficient if we could communicate with computers using natural language instead of relying on an interface. It is necessary, then, that the computer does not see a sentence as a collection of individual words, but instead can understand the deeper, compositional meaning of the sentence. A way to tackle this problem is to automatically assign a formal, structured meaning representation to each sentence, which are easy for computers to interpret. There have been quite a few attempts at this before, but these approaches were usually heavily reliant on predefined rules, word lists or representations of the syntax of the text. This made the general usage of these methods quite complicated. In this thesis we employ an algorithm that can learn to automatically assign meaning representations to texts, without using any such external resource. Specifically, we use a type of artificial neural network called a sequence-to-sequence model, in a process that is often referred to as deep learning. The devil is in the details, but we find that this type of algorithm can produce high quality meaning representations, with better performance than the more traditional methods. Moreover, a main finding of the thesis is that, counter intuitively, it is often better to represent the text as a sequence of individual characters, and not words. This is likely the case because it helps the model in dealing with spelling errors, unknown words and inflections

Multiword expressions at length and in depth

The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual resources and mobilised several research groups in computational linguistics worldwide. This book contains extended versions of selected papers from the workshop. Authors worked hard to include detailed explanations, broader and deeper analyses, and new exciting results, which were thoroughly reviewed by an internationally renowned committee. We hope that this distinctly joint effort will provide a meaningful and useful snapshot of the multilingual state of the art in multiword expressions modelling and processing, and will be a point point of reference for future work

Luonnollisen kielen syntaksin parsiminen neuroverkoilla

Conversational user interfaces have made strong appearance during the last couple of years. Most growth with conversational UIs can be seen with customer service moving from phone to chat. As big as the hype surrounding the conversational user interfaces is, they often cannot surpass traditional alternatives for the use cases they are used without actually understanding the language user speaks. This has crated a large demand for artificial intelligence (AI) which can understand users in their natural language. Natural language used by humans is tremendously complex without people actually realizing that. Understanding something this complex often requires system which divides the problem into smaller sub-problems and tries to tackle those, a divide and conquer paradigm. Natural language processing tools are often built as pipelines where more information is mined from the text in each step. Finding syntactic features, such as part of speech and lemma, is one such step, and is the focus of this thesis. Main objective for this thesis was to build a neural network architecture which can classify lemmas and parts of speech for the input text. Research hypothesis was then to determine if such architecture could be modified to do the both tasks at the same time and if such change would improve classiication performance of the model. Doing experiments with Finnish Universal Dependencies dataset revealed that lemmatization benefits from jointly learning to POS-tag, but POS-tagging performance could not be improved. Best absolute lemmatization results were gained by using correct POS-tags as input features, but since they are not available for live predictions the result has no practical meaning

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

Implicit emotion detection in text

In text, emotion can be expressed explicitly, using emotion-bearing words (e.g. happy, guilty) or implicitly without emotion-bearing words. Existing approaches focus on the detection of explicitly expressed emotion in text. However, there are various ways to express and convey emotions without the use of these emotion-bearing words. For example, given two sentences: “The outcome of my exam makes me happy” and “I passed my exam”, both sentences express happiness, with the first expressing it explicitly and the other implying it. In this thesis, we investigate implicit emotion detection in text. We propose a rule-based approach for implicit emotion detection, which can be used without labeled corpora for training. Our results show that our approach outperforms the lexicon matching method consistently and gives competitive performance in comparison to supervised classifiers. Given that emotions such as guilt and admiration which often require the identification of blameworthiness and praiseworthiness, we also propose an approach for the detection of blame and praise in text, using an adapted psychology model, Path model to blame. Lack of benchmarking dataset led us to construct a corpus containing comments of individuals’ emotional experiences annotated as blame, praise or others. Since implicit emotion detection might be useful for conflict-of-interest (CoI) detection in Wikipedia articles, we built a CoI corpus and explored various features including linguistic and stylometric, presentation, bias and emotion features. Our results show that emotion features are important when using Nave Bayes, but the best performance is obtained with SVM on linguistic and stylometric features only. Overall, we show that a rule-based approach can be used to detect implicit emotion in the absence of labelled data; it is feasible to adopt the psychology path model to blame for blame/praise detection from text, and implicit emotion detection is beneficial for CoI detection in Wikipedia articles

Modeling Dependencies in Natural Languages with Latent Variables

In this thesis, we investigate the use of latent variables to model complex dependencies in natural languages. Traditional models, which have a fixed parameterization, often make strong independence assumptions that lead to poor performance. This problem is often addressed by incorporating additional dependencies into the model (e.g., using higher order N-grams for language modeling). These added dependencies can increase data sparsity and/or require expert knowledge, together with trial and error, in order to identify and incorporate the most important dependencies (as in lexicalized parsing models). Traditional models, when developed for a particular genre, domain, or language, are also often difficult to adapt to another. In contrast, previous work has shown that latent variable models, which automatically learn dependencies in a data-driven way, are able to flexibly adjust the number of parameters based on the type and the amount of training data available. We have created several different types of latent variable models for a diverse set of natural language processing applications, including novel models for part-of-speech tagging, language modeling, and machine translation, and an improved model for parsing. These models perform significantly better than traditional models. We have also created and evaluated three different methods for improving the performance of latent variable models. While these methods can be applied to any of our applications, we focus our experiments on parsing. The first method involves self-training, i.e., we train models using a combination of gold standard training data and a large amount of automatically labeled training data. We conclude from a series of experiments that the latent variable models benefit much more from self-training than conventional models, apparently due to their flexibility to adjust their model parameterization to learn more accurate models from the additional automatically labeled training data. The second method takes advantage of the variability among latent variable models to combine multiple models for enhanced performance. We investigate several different training protocols to combine self-training with model combination. We conclude that these two techniques are complementary to each other and can be effectively combined to train very high quality parsing models. The third method replaces the generative multinomial lexical model of latent variable grammars with a feature-rich log-linear lexical model to provide a principled solution to address data sparsity, handle out-of-vocabulary words, and exploit overlapping features during model induction. We conclude from experiments that the resulting grammars are able to effectively parse three different languages. This work contributes to natural language processing by creating flexible and effective latent variable models for several different languages. Our investigation of self-training, model combination, and log-linear models also provides insights into the effective application of these machine learning techniques to other disciplines