Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research.
This paper explores techniques to take advantage of the fundamental difference in structure between hidden Markov models (HMM) and hierarchical hidden Markov models (HHMM). The HHMM structure allows repeated parts of the model to be merged together. A merged model takes advantage of the recurring patterns within the hierarchy, and the clusters that exist in some sequences of observations, in order to increase the extraction accuracy.
We describe a simple variant of the interpolated Markov model with non-emitting state transitions and prove that it is strictly more powerful than any Markov model. Empirical results demonstrate that the non-emitting model outperforms the interpolated model on the Brown corpus and on the Wall Street Journal under a wide range of experimental conditions. The nonemitting model is also much less prone to overtraining. The remainder of our article consists of four sections.
This paper presents a method to develop a class of variable memory Markov models that have higher memory capacity than traditional (uniform memory) Markov models. The structure of the variable memory models is induced from a manually annotated corpus through a decision tree learning algorithm. A series of comparative experiments show the resulting models outperform uniform memory Markov models in a part-of-speech tagging task.
The automatic coding of clinical documents is an important task for today’s healthcare providers. Though it can be viewed as multi-label document classiﬁcation, the coding problem has the interesting property that most code assignments can be supported by a single phrase found in the input document. We propose a Lexically-Triggered Hidden Markov Model (LT-HMM) that leverages these phrases to improve coding accuracy.
Most statistical machine translation systems rely on composed rules (rules that can be formed out of smaller rules in the grammar). Though this practice improves translation by weakening independence assumptions in the translation model, it nevertheless results in huge, redundant grammars, making both training and decoding ineﬃcient. Here, we take the opposite approach, where we only use minimal rules (those that cannot be formed out of other rules), and instead rely on a rule Markov model of the derivation history to capture dependencies between minimal rules. ...
This paper presents a supervised pronoun anaphora resolution system based on factorial hidden Markov models (FHMMs). The basic idea is that the hidden states of FHMMs are an explicit short-term memory with an antecedent buffer containing recently described referents. Thus an observed pronoun can ﬁnd its antecedent from the hidden buffer, or in terms of a generative model, the entries in the hidden buffer generate the corresponding pronouns.
Hidden Markov models (HMMs) are powerful statistical models that have found successful applications in Information Extraction (IE). In current approaches to applying HMMs to IE, an HMM is used to model text at the document level. This modelling might cause undesired redundancy in extraction in the sense that more than one ﬁller is identiﬁed and extracted. We propose to use HMMs to model text at the segment level, in which the extraction process consists of two steps: a segment retrieval step followed by an extraction step. ...
This paper presents an unsupervised topic identiﬁcation method integrating linguistic and visual information based on Hidden Markov Models (HMMs). We employ HMMs for topic identiﬁcation, wherein a state corresponds to a topic and various features including linguistic, visual and audio information are observed. Our experiments on two kinds of cooking TV programs show the effectiveness of our proposed method.
This paper presents a new approach to partial parsing of context-free structures. The approach is based on Markov Models. Each layer of the resulting structure is represented by its own Markov Model, and output of a lower layer is passed as input to the next higher layer. An empirical evaluation of the method yields very good results for NP/PP chunking of German newspaper texts.
This paper describes the conversion of a Hidden Markov Model into a sequential transducer that closely approximates the behavior of the stochastic model. This transformation is especially advantageous for part-of-speech tagging because the resulting transducer can be composed with other transducers that encode correction rules for the most frequent tagging errors. The speed of tagging is also improved. The described methods have been implemented and successfully tested on six languages.
We present a new approach to disambiguating syntactically ambiguous words in context, based on Variable Memory Markov (VMM) models. In contrast to fixed-length Markov models, which predict based on fixed-length histories, variable memory Markov models dynamically adapt their history length based on the training data, and hence may use fewer parameters. In a test of a VMM based tagger on the Brown corpus, 95.81% of tokens are correctly classified.
This paper describes an unsupervised dynamic graphical model for morphological segmentation and bilingual morpheme alignment for statistical machine translation. The model extends Hidden Semi-Markov chain models by using factored output nodes and special structures for its conditional probability distributions. It relies on morpho-syntactic and lexical source-side information (part-of-speech, morphological segmentation) while learning a morpheme segmentation over the target language. Our model outperforms a competitive word alignment system in alignment quality. ...
Surface realisation decisions in language generation can be sensitive to a language model, but also to decisions of content selection. We therefore propose the joint optimisation of content selection and surface realisation using Hierarchical Reinforcement Learning (HRL). To this end, we suggest a novel reward function that is induced from human data and is especially suited for surface realisation.
Mô hình Markov ẩn (tiếng Anh là Hidden Markov Model - HMM) là mô hình thống kê trong đó hệ thống được mô hình hóa được cho là một quá trình Markov với các tham số không biết trước và nhiệm vụ là xác định các tham số ẩn từ các tham số quan sát được, dựa trên sự thừa nhận này. Các tham số của mô hình được rút ra sau đó có thể sử dụng để thực hiện các phân tích kế tiếp, ví dụ cho các ứng dụng nhận dạng mẫu....
This book aims to give a complete and self-contained presentation of semi-
Markov models with finitely many states, in view of solving real life problems of
risk management in three main fields: Finance, Insurance and Reliability
providing a useful complement to our first book (Janssen and Manca (2006))
which gives a theoretical presentation of semi-Markov theory. However, to help
assure the book is self-contained, the first three chapters provide a summary of
the basic tools on semi-Markov theory that the reader will need to understand our
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Speech Silicon: An FPGA Architecture for Real-Time Hidden Markov-Model-Based Speech Recognition
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: IResearch Article Hidden Markov Model with Duration Side Information for Novel HMMD Derivation, with Application to Eukaryotic Gene Finding