Parts of speech tagging using hidden Markov model, maximum entropy model and conditional random field

Parts of Speech tagging assigns the suitable part of speech or in other words, the lexical category to every word in the sentence in Natural language. It is one of the essential tasks of Natural Language Processing. Parts of Speech tagging is the very first step following which various other processes as in chunking, parsing, named entity recognition etc. are performed. An adaptation of various machine learning methods are applied namely Hidden Markov Model (HMM), Maximum Entropy Model(MEM) and Conditional Random Field(CRF) . For HMM models, we have used the suffix information for smoothing of the emission probabilities, while for ME model, the suffix information is used as features. Similar case for the CRF as that used by ME model. The significant points brought about by thesis can be highlighted below: • Use of Hidden Markov Model for Parts Of Speech tagging purpose. To create a sophisticated tagger using small set of training corpus , resources like a Dictionary is used that improves the overall accuracy of the tagger. • Machine learning techniques have been introduced for acquiring discriminative approach. The Maximum Entropy Model and Conditional Random Field has been used for this task. Keywords: Hidden Markov Model, Maximum Entropy Model, Conditional Random Field, POS tagger

Probabilistic Constraint Logic Programming

This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-linear probability model for probabilistic constraint logic programming. On top of this model we define an algorithm to estimate the parameters and to select the properties of log-linear models from incomplete data. This algorithm is an extension of the improved iterative scaling algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm applies to log-linear models in general and is accompanied with suitable approximation methods when applied to large data spaces. Furthermore, we present an approach for searching for most probable analyses of the probabilistic constraint logic programming model. This method can be applied to the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl

Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic

Language modeling for an inflected language such as Arabic poses new challenges for speech recognition and machine translation due to its rich morphology. Rich morphology results in large increases in out-of-vocabulary (OOV) rate and poor language model parameter estimation in the absence of large quantities of data. In this study, we present a joint morphological-lexical language model (JMLLM) that takes advantage of Arabic morphology. JMLLM combines morphological segments with the underlying lexical items and additional available information sources with regards to morphological segments and lexical items in a single joint model. Joint representation and modeling of morphological and lexical items reduces the OOV rate and provides smooth probability estimates while keeping the predictive power of whole words. Speech recognition and machine translation experiments in dialectal-Arabic show improvements over word and morpheme based trigram language models. We also show that as the tightness of integration between different information sources increases, both speech recognition and machine translation performances improve

Domain Adaptation for Statistical Classifiers

The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the "in-domain" test data is drawn from a distribution that is related, but not identical, to the "out-of-domain" distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts. We present efficient inference algorithms for this special case based on the technique of conditional expectation maximization. Our experimental results show that our approach leads to improved performance on three real world tasks on four different data sets from the natural language processing domain

Topic-based mixture language modelling

This paper describes an approach for constructing a mixture of language models based on simple statistical notions of semantics using probabilistic models developed for information retrieval. The approach encapsulates corpus-derived semantic information and is able to model varying styles of text. Using such information, the corpus texts are clustered in an unsupervised manner and a mixture of topic-specific language models is automatically created. The principal contribution of this work is to characterise the document space resulting from information retrieval techniques and to demonstrate the approach for mixture language modelling. A comparison is made between manual and automatic clustering in order to elucidate how the global content information is expressed in the space. We also compare (in terms of association with manual clustering and language modelling accuracy) alternative term-weighting schemes and the effect of singular value decomposition dimension reduction (latent semantic analysis). Test set perplexity results using the British National Corpus indicate that the approach can improve the potential of statistical language modelling. Using an adaptive procedure, the conventional model may be tuned to track text data with a slight increase in computational cost

Natural Language Processing Using Neighbour Entropy-based Segmentation

In natural language processing (NLP) of Chinese hazard text collected in the process of hazard identification, Chinese word segmentation (CWS) is the first step to extracting meaningful information from such semi-structured Chinese texts. This paper proposes a new neighbor entropy-based segmentation (NES) model for CWS. The model considers the segmentation benefits of neighbor entropies, adopting the concept of "neighbor" in optimization research. It is defined by the benefit ratio of text segmentation, including benefits and losses of combining the segmentation unit with more information than other popular statistical models. In the experiments performed, together with the maximum-based segmentation algorithm, the NES model achieves a 99.3% precision, 98.7% recall, and 99.0% f-measure for text segmentation; these performances are higher than those of existing tools based on other seven popular statistical models. Results show that the NES model is a valid CWS, especially for text segmentation requirements necessitating longer-sized characters. The text corpus used comes from the Beijing Municipal Administration of Work Safety, which was recorded in the fourth quarter of 2018