Part-of-speech Tagging: A Machine Learning Approach based on Decision Trees

The study and application of general Machine Learning (ML) algorithms to theclassical ambiguity problems in the area of Natural Language Processing (NLP) isa currently very active area of research. This trend is sometimes called NaturalLanguage Learning. Within this framework, the present work explores the applicationof a concrete machine-learning technique, namely decision-tree induction, toa very basic NLP problem, namely part-of-speech disambiguation (POS tagging).Its main contributions fall in the NLP field, while topics appearing are addressedfrom the artificial intelligence perspective, rather from a linguistic point of view.A relevant property of the system we propose is the clear separation betweenthe acquisition of the language model and its application within a concrete disambiguationalgorithm, with the aim of constructing two components which are asindependent as possible. Such an approach has many advantages. For instance, thelanguage models obtained can be easily adapted into previously existing taggingformalisms; the two modules can be improved and extended separately; etc.As a first step, we have experimentally proven that decision trees (DT) providea flexible (by allowing a rich feature representation), efficient and compact wayfor acquiring, representing and accessing the information about POS ambiguities.In addition to that, DTs provide proper estimations of conditional probabilities fortags and words in their particular contexts. Additional machine learning techniques,based on the combination of classifiers, have been applied to address some particularweaknesses of our tree-based approach, and to further improve the accuracy in themost difficult cases.As a second step, the acquired models have been used to construct simple,accurate and effective taggers, based on diiferent paradigms. In particular, wepresent three different taggers that include the tree-based models: RTT, STT, andRELAX, which have shown different properties regarding speed, flexibility, accuracy,etc. The idea is that the particular user needs and environment will define whichis the most appropriate tagger in each situation. Although we have observed slightdifferences, the accuracy results for the three taggers, tested on the WSJ test benchcorpus, are uniformly very high, and, if not better, they are at least as good asthose of a number of current taggers based on automatic acquisition (a qualitativecomparison with the most relevant current work is also reported.Additionally, our approach has been adapted to annotate a general Spanishcorpus, with the particular limitation of learning from small training sets. A newtechnique, based on tagger combination and bootstrapping, has been proposed toaddress this problem and to improve accuracy. Experimental results showed thatvery high accuracy is possible for Spanish tagging, with a relatively low manualeffort. Additionally, the success in this real application has confirmed the validity of our approach, and the validity of the previously presented portability argumentin favour of automatically acquired taggers

Robust handling of out-of-vocabulary words in deep language processing

Tese de doutoramento, Informática (Ciências da Computação), Universidade de Lisboa, Faculdade de Ciências, 2014Deep grammars handle with precision complex grammatical phenomena and are able to provide a semantic representation of their input sentences in some logic form amenable to computational processing, making such grammars desirable for advanced Natural Language Processing tasks. The robustness of these grammars still has room to be improved. If any of the words in a sentence is not present in the lexicon of the grammar, i.e. if it is an out-of-vocabulary (OOV) word, a full parse of that sentence may not be produced. Given that the occurrence of such words is inevitable, e.g. due to the property of lexical novelty that is intrinsic to natural languages, deep grammars need some mechanism to handle OOV words if they are to be used in applications to analyze unrestricted text. The aim of this work is thus to investigate ways of improving the handling of OOV words in deep grammars. The lexicon of a deep grammar is highly thorough, with words being assigned extremely detailed linguistic information. Accurately assigning similarly detailed information to OOV words calls for the development of novel approaches, since current techniques mostly rely on shallow features and on a limited window of context, while there are many cases where the relevant information is to be found in wider linguistic structure and in long-distance relations. The solution proposed here consists of a classifier, SVM-TK, that is placed between the input to the grammar and the grammar itself. This classifier can take a variety of features and assign to words deep lexical types which can then be used by the grammar when faced with OOV words. The classifier is based on support-vector machines which, through the use of kernels, allows the seamless use of features encoding linguistic structure in the classifier. This dissertation focuses on the HPSG framework, but the method can be used in any framework where the lexical information can be encoded as a word tag. As a case study, we take LX-Gram, a computational grammar for Portuguese, to improve its robustness with respect to OOV verbs. Given that the subcategorization frame of a word is a substantial part of what is encoded in an HPSG deep lexical type, the classifier takes graph encoding grammatical dependencies as features. At runtime, these dependencies are produced by a probabilistic dependency parser. The SVM-TK classifier is compared against the state-of-the-art approaches for OOV handling, which consist of using a standard POS-tagger to assign lexical types, in essence doing POS-tagging with a highly granular tagset. Results show that SVM-TK is able to improve on the state-of-the-art, with the usual data-sparseness bottleneck issues imposing this to happen when the amount of training data is large enough.As gramáticas de processamento profundo lidam de forma precisa com fenómenos linguisticos complexos e são capazes de providenciar uma representação semântica das frases que lhes são dadas, o que torna tais gramáticas desejáveis para tarefas avançadas em Processamento de Linguagem Natural. A robustez destas gramáticas tem ainda espaço para ser melhorada. Se alguma das palavras numa frase não se encontra presente no léxico da gramática (em inglês, uma palavra out-of-vocabulary, ou OOV), pode não ser possível produzir uma análise completa dessa frase. Dado que a ocorrência de tais palavras é algo inevitável, e.g. devido à novidade lexical que é intrínseca às línguas naturais, as gramáticas profundas requerem algum mecanismo que lhes permita lidar com palavras OOV de forma a que possam ser usadas para análise de texto em aplicações. O objectivo deste trabalho é então investigar formas de melhor lidar com palavras OOV numa gramática de processamento profundo. O léxico de uma gramática profunda é altamente granular, sendo cada palavra associada com informação linguística extremamente detalhada. Atribuir corretamente a palavras OOV informação linguística com o nível de detalhe adequado requer que se desenvolvam técnicas inovadoras, dado que as abordagens atuais baseiam-se, na sua maioria, em características superficiais (shallow features) e em janelas de contexto limitadas, apesar de haver muitos casos onde a informação relevante se encontra na estrutura linguística e em relações de longa distância. A solução proposta neste trabalho consiste num classificador, SVM-TK, que é colocado entre o input da gramática e a gramática propriamente dita. Este classificador aceita uma variedade de features e atribui às palavras tipos lexicais profundos que podem então ser usado pela gramática sempre que esta se depare com palavras OOV. O classificador baseia-se em máquinas de vetores de suporte (support-vector machines). Esta técnica, quando combinada com o uso de kernels, permite que o classificador use, de forma transparente, features que codificam estrutura linguística. Esta dissertação foca-se no enquadramento teórico HPSG, embora o método proposto possa ser usado em qualquer enquadramento onde a informação lexical possa ser codificada sob a forma de uma etiqueta atribuída a uma palavra. Como caso de estudo, usamos a LX-Gram, uma gramatica computacional para a língua portuguesa, e melhoramos a sua robustez a verbos OOV. Dado que a grelha de subcategorização de uma palavra é uma parte substancial daquilo que se encontra codificado num tipo lexical profundo em HPSG, o classificador usa features baseados em dependências gramaticais. No momento de execução, estas dependências são produzidas por um analisador de dependências probabilístico. O classificador SVM-TK é comparado com o estado-da-arte para a tarefa de resolução de palavras OOV, que consiste em usar um anotador morfossintático (POS-tagger) para atribuir tipos lexicais, fazendo, no fundo, anotação com um conjunto de etiquetas altamente detalhado. Os resultados mostram que o SVM-TK melhora o estado-da-arte, com os já habituais problemas de esparssez de dados fazendo com que este efeito seja notado quando a quantidade de dados de treino é suficientemente grande.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/41465/2007