Andere Kunden interessierten sich auch für
![Opinion Mining using Lexicon Matching Based on Hidden Markov Model]( "Opinion Mining using Lexicon Matching Based on Hidden Markov Model")
Opinion Mining using Lexicon Matching Based on Hidden Markov Model23,99 €![EEG Signal Classification Using Hidden Markov Model]( "EEG Signal Classification Using Hidden Markov Model")
EEG Signal Classification Using Hidden Markov Model26,99 €![Sentence Extraction for Automatic Summarization and Notetaking Systems]( "Sentence Extraction for Automatic Summarization and Notetaking Systems")
Sentence Extraction for Automatic Summarization and Notetaking Systems38,99 €![Automatic Generation of MCQs Using Information Extraction]( "Automatic Generation of MCQs Using Information Extraction")
Automatic Generation of MCQs Using Information Extraction48,99 €![Punjabi TTS System Using Hidden Markov Models]( "Punjabi TTS System Using Hidden Markov Models")
Punjabi TTS System Using Hidden Markov Models32,99 €![Named Entity Recognition in Natural languages using Hidden Markov Mode]( "Named Entity Recognition in Natural languages using Hidden Markov Mode")
Named Entity Recognition in Natural languages using Hidden Markov Mode36,99 €![Improvements of Vietnamese Hidden Markov Model based speech synthesis]( "Improvements of Vietnamese Hidden Markov Model based speech synthesis")
Improvements of Vietnamese Hidden Markov Model based speech synthesis36,99 €
The number and size of documents in electronic formats is rising very rapidly and due to the flexibility and ease of use, huge amount of information is stored in textual formats in unstructured way than structured materials, where important information or interesting knowledge is buried in. Information extraction (IE), which is one of text mining approaches, is found efficient in discovering particular entities or relations between the entities in free texts. This book designs, develops and evaluates an Information Extraction system for Amharic language texts, in which it extracts valuable information from the texts and put them on the predefined slots or attributes. Among a number of techniques and methods available for IE, Hidden Markov Model (HMM), a sequential labeling based method, was selected as a methodology. In this book emphasis has been given to the available algorithms in Hidden Markov Model to build an Information Extraction system and finally simple counting of correct annotations and standard Information Retrieval (IR) performance evaluation techniques was used to evaluate the system.