Named Entity Recognition and Text Compression

Import 13/01/2017In recent years, social networks have become very popular. It is easy for users to share their data using online social networks. Since data on social networks is idiomatic, irregular, brief, and includes acronyms and spelling errors, dealing with such data is more challenging than that of news or formal texts. With the huge volume of posts each day, effective extraction and processing of these data will bring great benefit to information extraction applications. This thesis proposes a method to normalize Vietnamese informal text in social networks. This method has the ability to identify and normalize informal text based on the structure of Vietnamese words, Vietnamese syllable rules, and a trigram model. After normalization, the data will be processed by a named entity recognition (NER) model to identify and classify the named entities in these data. In our NER model, we use six different types of features to recognize named entities categorized in three predefined classes: Person (PER), Location (LOC), and Organization (ORG). When viewing social network data, we found that the size of these data are very large and increase daily. This raises the challenge of how to decrease this size. Due to the size of the data to be normalized, we use a trigram dictionary that is quite big, therefore we also need to decrease its size. To deal with this challenge, in this thesis, we propose three methods to compress text files, especially in Vietnamese text. The first method is a syllable-based method relying on the structure of Vietnamese morphosyllables, consonants, syllables and vowels. The second method is trigram-based Vietnamese text compression based on a trigram dictionary. The last method is based on an n-gram slide window, in which we use five dictionaries for unigrams, bigrams, trigrams, four-grams and five-grams. This method achieves a promising compression ratio of around 90% and can be used for any size of text file.In recent years, social networks have become very popular. It is easy for users to share their data using online social networks. Since data on social networks is idiomatic, irregular, brief, and includes acronyms and spelling errors, dealing with such data is more challenging than that of news or formal texts. With the huge volume of posts each day, effective extraction and processing of these data will bring great benefit to information extraction applications. This thesis proposes a method to normalize Vietnamese informal text in social networks. This method has the ability to identify and normalize informal text based on the structure of Vietnamese words, Vietnamese syllable rules, and a trigram model. After normalization, the data will be processed by a named entity recognition (NER) model to identify and classify the named entities in these data. In our NER model, we use six different types of features to recognize named entities categorized in three predefined classes: Person (PER), Location (LOC), and Organization (ORG). When viewing social network data, we found that the size of these data are very large and increase daily. This raises the challenge of how to decrease this size. Due to the size of the data to be normalized, we use a trigram dictionary that is quite big, therefore we also need to decrease its size. To deal with this challenge, in this thesis, we propose three methods to compress text files, especially in Vietnamese text. The first method is a syllable-based method relying on the structure of Vietnamese morphosyllables, consonants, syllables and vowels. The second method is trigram-based Vietnamese text compression based on a trigram dictionary. The last method is based on an n-gram slide window, in which we use five dictionaries for unigrams, bigrams, trigrams, four-grams and five-grams. This method achieves a promising compression ratio of around 90% and can be used for any size of text file.460 - Katedra informatikyvyhově

Automating information extraction task for Turkish texts

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2011.Thesis (Ph. D.) -- Bilkent University, 2011.Includes bibliographical references leaves 85-97.Throughout history, mankind has often suffered from a lack of necessary resources. In today’s information world, the challenge can sometimes be a wealth of resources. That is to say, an excessive amount of information implies the need to find and extract necessary information. Information extraction can be defined as the identification of selected types of entities, relations, facts or events in a set of unstructured text documents in a natural language. The goal of our research is to build a system that automatically locates and extracts information from Turkish unstructured texts. Our study focuses on two basic Information Extraction (IE) tasks: Named Entity Recognition and Entity Relation Detection. Named Entity Recognition, finding named entities (persons, locations, organizations, etc.) located in unstructured texts, is one of the most fundamental IE tasks. Entity Relation Detection task tries to identify relationships between entities mentioned in text documents. Using supervised learning strategy, the developed systems start with a set of examples collected from a training dataset and generate the extraction rules from the given examples by using a carefully designed coverage algorithm. Moreover, several rule filtering and rule refinement techniques are utilized to maximize generalization and accuracy at the same time. In order to obtain accurate generalization, we use several syntactic and semantic features of the text, including: orthographical, contextual, lexical and morphological features. In particular, morphological features of the text are effectively used in this study to increase the extraction performance for Turkish, an agglutinative language. Since the system does not rely on handcrafted rules/patterns, it does not heavily suffer from domain adaptability problem. The results of the conducted experiments show that (1) the developed systems are successfully applicable to the Named Entity Recognition and Entity Relation Detection tasks, and (2) exploiting morphological features can significantly improve the performance of information extraction from Turkish, an agglutinative language.Tatar, SerhanPh.D

Neural Graph Transfer Learning in Natural Language Processing Tasks

Natural language is essential in our daily lives as we rely on languages to communicate and exchange information. A fundamental goal for natural language processing (NLP) is to let the machine understand natural language to help or replace human experts to mine knowledge and complete tasks. Many NLP tasks deal with sequential data. For example, a sentence is considered as a sequence of works. Very recently, deep learning-based language models (i.e.,BERT \citep{devlin2018bert}) achieved significant improvement in many existing tasks, including text classification and natural language inference. However, not all tasks can be formulated using sequence models. Specifically, graph-structured data is also fundamental in NLP, including entity linking, entity classification, relation extraction, abstractive meaning representation, and knowledge graphs \citep{santoro2017simple,hamilton2017representation,kipf2016semi}. In this scenario, BERT-based pretrained models may not be suitable. Graph Convolutional Neural Network (GCN) \citep{kipf2016semi} is a deep neural network model designed for graphs. It has shown great potential in text classification, link prediction, question answering and so on. This dissertation presents novel graph models for NLP tasks, including text classification, prerequisite chain learning, and coreference resolution. We focus on different perspectives of graph convolutional network modeling: for text classification, a novel graph construction method is proposed which allows interpretability for the prediction; for prerequisite chain learning, we propose multiple aggregation functions that utilize neighbors for better information exchange; for coreference resolution, we study how graph pretraining can help when labeled data is limited. Moreover, an important branch is to apply pretrained language models for the mentioned tasks. So, this dissertation also focuses on the transfer learning method that generalizes pretrained models to other domains, including medical, cross-lingual, and web data. Finally, we propose a new task called unsupervised cross-domain prerequisite chain learning, and study novel graph-based methods to transfer knowledge over graphs

Data-efficient methods for information extraction

Strukturierte Wissensrepräsentationssysteme wie Wissensdatenbanken oder Wissensgraphen bieten Einblicke in Entitäten und Beziehungen zwischen diesen Entitäten in der realen Welt. Solche Wissensrepräsentationssysteme können in verschiedenen Anwendungen der natürlichen Sprachverarbeitung eingesetzt werden, z. B. bei der semantischen Suche, der Beantwortung von Fragen und der Textzusammenfassung. Es ist nicht praktikabel und ineffizient, diese Wissensrepräsentationssysteme manuell zu befüllen. In dieser Arbeit entwickeln wir Methoden, um automatisch benannte Entitäten und Beziehungen zwischen den Entitäten aus Klartext zu extrahieren. Unsere Methoden können daher verwendet werden, um entweder die bestehenden unvollständigen Wissensrepräsentationssysteme zu vervollständigen oder ein neues strukturiertes Wissensrepräsentationssystem von Grund auf zu erstellen. Im Gegensatz zu den gängigen überwachten Methoden zur Informationsextraktion konzentrieren sich unsere Methoden auf das Szenario mit wenigen Daten und erfordern keine große Menge an kommentierten Daten. Im ersten Teil der Arbeit haben wir uns auf das Problem der Erkennung von benannten Entitäten konzentriert. Wir haben an der gemeinsamen Aufgabe von Bacteria Biotope 2019 teilgenommen. Die gemeinsame Aufgabe besteht darin, biomedizinische Entitätserwähnungen zu erkennen und zu normalisieren. Unser linguistically informed Named-Entity-Recognition-System besteht aus einem Deep-Learning-basierten Modell, das sowohl verschachtelte als auch flache Entitäten extrahieren kann; unser Modell verwendet mehrere linguistische Merkmale und zusätzliche Trainingsziele, um effizientes Lernen in datenarmen Szenarien zu ermöglichen. Unser System zur Entitätsnormalisierung verwendet String-Match, Fuzzy-Suche und semantische Suche, um die extrahierten benannten Entitäten mit den biomedizinischen Datenbanken zu verknüpfen. Unser System zur Erkennung von benannten Entitäten und zur Entitätsnormalisierung erreichte die niedrigste Slot-Fehlerrate von 0,715 und belegte den ersten Platz in der gemeinsamen Aufgabe. Wir haben auch an zwei gemeinsamen Aufgaben teilgenommen: Adverse Drug Effect Span Detection (Englisch) und Profession Span Detection (Spanisch); beide Aufgaben sammeln Daten von der Social Media Plattform Twitter. Wir haben ein Named-Entity-Recognition-Modell entwickelt, das die Eingabedarstellung des Modells durch das Stapeln heterogener Einbettungen aus verschiedenen Domänen verbessern kann; unsere empirischen Ergebnisse zeigen komplementäres Lernen aus diesen heterogenen Einbettungen. Unser Beitrag belegte den 3. Platz in den beiden gemeinsamen Aufgaben. Im zweiten Teil der Arbeit untersuchten wir Strategien zur Erweiterung synthetischer Daten, um ressourcenarme Informationsextraktion in spezialisierten Domänen zu ermöglichen. Insbesondere haben wir backtranslation an die Aufgabe der Erkennung von benannten Entitäten auf Token-Ebene und der Extraktion von Beziehungen auf Satzebene angepasst. Wir zeigen, dass die Rückübersetzung sprachlich vielfältige und grammatikalisch kohärente synthetische Sätze erzeugen kann und als wettbewerbsfähige Erweiterungsstrategie für die Aufgaben der Erkennung von benannten Entitäten und der Extraktion von Beziehungen dient. Bei den meisten realen Aufgaben zur Extraktion von Beziehungen stehen keine kommentierten Daten zur Verfügung, jedoch ist häufig ein großer unkommentierter Textkorpus vorhanden. Bootstrapping-Methoden zur Beziehungsextraktion können mit diesem großen Korpus arbeiten, da sie nur eine Handvoll Startinstanzen benötigen. Bootstrapping-Methoden neigen jedoch dazu, im Laufe der Zeit Rauschen zu akkumulieren (bekannt als semantische Drift), und dieses Phänomen hat einen drastischen negativen Einfluss auf die endgültige Genauigkeit der Extraktionen. Wir entwickeln zwei Methoden zur Einschränkung des Bootstrapping-Prozesses, um die semantische Drift bei der Extraktion von Beziehungen zu minimieren. Unsere Methoden nutzen die Graphentheorie und vortrainierte Sprachmodelle, um verrauschte Extraktionsmuster explizit zu identifizieren und zu entfernen. Wir berichten über die experimentellen Ergebnisse auf dem TACRED-Datensatz für vier Relationen. Im letzten Teil der Arbeit demonstrieren wir die Anwendung der Domänenanpassung auf die anspruchsvolle Aufgabe der mehrsprachigen Akronymextraktion. Unsere Experimente zeigen, dass die Domänenanpassung die Akronymextraktion in wissenschaftlichen und juristischen Bereichen in sechs Sprachen verbessern kann, darunter auch Sprachen mit geringen Ressourcen wie Persisch und Vietnamesisch.The structured knowledge representation systems such as knowledge base or knowledge graph can provide insights regarding entities and relationship(s) among these entities in the real-world, such knowledge representation systems can be employed in various natural language processing applications such as semantic search, question answering and text summarization. It is infeasible and inefficient to manually populate these knowledge representation systems. In this work, we develop methods to automatically extract named entities and relationships among the entities from plain text and hence our methods can be used to either complete the existing incomplete knowledge representation systems to create a new structured knowledge representation system from scratch. Unlike mainstream supervised methods for information extraction, our methods focus on the low-data scenario and do not require a large amount of annotated data. In the first part of the thesis, we focused on the problem of named entity recognition. We participated in the shared task of Bacteria Biotope 2019, the shared task consists of recognizing and normalizing the biomedical entity mentions. Our linguistically informed named entity recognition system consists of a deep learning based model which can extract both nested and flat entities; our model employed several linguistic features and auxiliary training objectives to enable efficient learning in data-scarce scenarios. Our entity normalization system employed string match, fuzzy search and semantic search to link the extracted named entities to the biomedical databases. Our named entity recognition and entity normalization system achieved the lowest slot error rate of 0.715 and ranked first in the shared task. We also participated in two shared tasks of Adverse Drug Effect Span detection (English) and Profession Span Detection (Spanish); both of these tasks collect data from the social media platform Twitter. We developed a named entity recognition model which can improve the input representation of the model by stacking heterogeneous embeddings from a diverse domain(s); our empirical results demonstrate complementary learning from these heterogeneous embeddings. Our submission ranked 3rd in both of the shared tasks. In the second part of the thesis, we explored synthetic data augmentation strategies to address low-resource information extraction in specialized domains. Specifically, we adapted backtranslation to the token-level task of named entity recognition and sentence-level task of relation extraction. We demonstrate that backtranslation can generate linguistically diverse and grammatically coherent synthetic sentences and serve as a competitive augmentation strategy for the task of named entity recognition and relation extraction. In most of the real-world relation extraction tasks, the annotated data is not available, however, quite often a large unannotated text corpus is available. Bootstrapping methods for relation extraction can operate on this large corpus as they only require a handful of seed instances. However, bootstrapping methods tend to accumulate noise over time (known as semantic drift) and this phenomenon has a drastic negative impact on the final precision of the extractions. We develop two methods to constrain the bootstrapping process to minimise semantic drift for relation extraction; our methods leverage graph theory and pre-trained language models to explicitly identify and remove noisy extraction patterns. We report the experimental results on the TACRED dataset for four relations. In the last part of the thesis, we demonstrate the application of domain adaptation to the challenging task of multi-lingual acronym extraction. Our experiments demonstrate that domain adaptation can improve acronym extraction within scientific and legal domains in 6 languages including low-resource languages such as Persian and Vietnamese

Joint Discourse-aware Concept Disambiguation and Clustering

This thesis addresses the tasks of concept disambiguation and clustering. Concept disambiguation is the task of linking common nouns and proper names in a text – henceforth called mentions – to their corresponding concepts in a predefined inventory. Concept clustering is the task of clustering mentions, so that all mentions in one cluster denote the same concept. In this thesis, we investigate concept disambiguation and clustering from a discourse perspective and propose a discourse-aware approach for joint concept disambiguation and clustering in the framework of Markov logic. The contributions of this thesis are fourfold: Joint Concept Disambiguation and Clustering. In previous approaches, concept disambiguation and concept clustering have been considered as two separate tasks (Schütze, 1998; Ji & Grishman, 2011). We analyze the relationship between concept disambiguation and concept clustering and argue that these two tasks can mutually support each other. We propose the – to our knowledge – first joint approach for concept disambiguation and clustering. Discourse-Aware Concept Disambiguation. One of the determining factors for concept disambiguation and clustering is the context definition. Most previous approaches use the same context definition for all mentions (Milne & Witten, 2008b; Kulkarni et al., 2009; Ratinov et al., 2011, inter alia). We approach the question which context is relevant to disambiguate a mention from a discourse perspective and state that different mentions require different notions of contexts. We state that the context that is relevant to disambiguate a mention depends on its embedding into discourse. However, how a mention is embedded into discourse depends on its denoted concept. Hence, the identification of the denoted concept and the relevant concept mutually depend on each other. We propose a binwise approach with three different context definitions and model the selection of the context definition and the disambiguation jointly. Modeling Interdependencies with Markov Logic. To model the interdependencies between concept disambiguation and concept clustering as well as the interdependencies between the context definition and the disambiguation, we use Markov logic (Domingos & Lowd, 2009). Markov logic combines first order logic with probabilities and allows us to concisely formalize these interdependencies. We investigate how we can balance between linguistic appropriateness and time efficiency and propose a hybrid approach that combines joint inference with aggregation techniques. Concept Disambiguation and Clustering beyond English: Multi- and Cross-linguality. Given the vast amount of texts written in different languages, the capability to extend an approach to cope with other languages than English is essential. We thus analyze how our approach copes with other languages than English and show that our approach largely scales across languages, even without retraining. Our approach is evaluated on multiple data sets originating from different sources (e.g. news, web) and across multiple languages. As an inventory, we use Wikipedia. We compare our approach to other approaches and show that it achieves state-of-the-art results. Furthermore, we show that joint concept disambiguating and clustering as well as joint context selection and disambiguation leads to significant improvements ceteris paribus

Aspects of Coherence for Entity Analysis

Natural language understanding is an important topic in natural language proces- sing. Given a text, a computer program should, at the very least, be able to under- stand what the text is about, and ideally also situate it in its extra-textual context and understand what purpose it serves. What exactly it means to understand what a text is about is an open question, but it is generally accepted that, at a minimum, un- derstanding involves being able to answer questions like “Who did what to whom? Where? When? How? And Why?”. Entity analysis, the computational analysis of entities mentioned in a text, aims to support answering the questions “Who?” and “Whom?” by identifying entities mentioned in a text. If the answers to “Where?” and “When?” are specific, named locations and events, entity analysis can also pro- vide these answers. Entity analysis aims to answer these questions by performing entity linking, that is, linking mentions of entities to their corresponding entry in a knowledge base, coreference resolution, that is, identifying all mentions in a text that refer to the same entity, and entity typing, that is, assigning a label such as Person to mentions of entities. In this thesis, we study how different aspects of coherence can be exploited to improve entity analysis. Our main contribution is a method that allows exploiting knowledge-rich, specific aspects of coherence, namely geographic, temporal, and entity type coherence. Geographic coherence expresses the intuition that entities mentioned in a text tend to be geographically close. Similarly, temporal coherence captures the intuition that entities mentioned in a text tend to be close in the tem- poral dimension. Entity type coherence is based in the observation that in a text about a certain topic, such as sports, the entities mentioned in it tend to have the same or related entity types, such as sports team or athlete. We show how to integrate features modeling these aspects of coherence into entity linking systems and esta- blish their utility in extensive experiments covering different datasets and systems. Since entity linking often requires computationally expensive joint, global optimi- zation, we propose a simple, but effective rule-based approach that enjoys some of the benefits of joint, global approaches, while avoiding some of their drawbacks. To enable convenient error analysis for system developers, we introduce a tool for visual analysis of entity linking system output. Investigating another aspect of co- herence, namely the coherence between a predicate and its arguments, we devise a distributed model of selectional preferences and assess its impact on a neural core- ference resolution system. Our final contribution examines how multilingual entity typing can be improved by incorporating subword information. We train and make publicly available subword embeddings in 275 languages and show their utility in a multilingual entity typing tas

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