000 04182nam a22003617a 4500
003 OSt
005 20260702144024.0
008 260702t20242006sz a|||er|||| 001 0 eng d
020 _a9783031575488 [hardbound]
040 _aUniversity of Cebu-Banilad
_cUniversity of Cebu-Banilad
100 _aNugues, Pierre M.,
_eauthor.
245 _aPython for natural language processing :
_bprogramming with NumPy, scikit-learn, Keras, and PyTorch /
_cPierre M. Nugues.
250 _aThird edition.
260 _aSwitzerland :
_bSpringer,
_cc2024.
300 _axxv, 520 pages :
_billustrations (black and white) ;
_c24 cm.
336 _2rdacontent
_atext
337 _2rdamedia
_aunmediated
338 _2rdacarrier
_avolume
500 _ahttps://doi.org/10.1007/978-3-031-575295
504 _aIncludes bibliographical references and index.
505 _aContents: 1 An overview of language processing — 2 A tour of python — 3 Corpus processing tools — 4 Encoding and annotation schemes — 5 Python for numerical computations — 6 Topics in information theory and machine learning — 7 Linear and logistic regression — 8 Neural networks — 9 Counting and indexing words — 10 Word sequences — 11 Dense vector representations — 12 Words, part of speech, and morphology — 13 Subword segmentation — 14 Part-of-speech and sequence annotation — 15 Self-attention and transformers — 16 Pretraining an encoder: The BERT Language model — 17 Sequence-to-sequence architectures: Encoder-decoders and decoders — References — Index.
520 _a"Since the last edition of this book (2014), progress has been astonishing in all areas of Natural Language Processing, with recent achievements in Text Generation that spurred a media interest going beyond the traditional academic circles. Text Processing has meanwhile become a mainstream industrial tool that is used, to various extents, by countless companies. As such, a revision of this book was deemed necessary to catch up with the recent breakthroughs, and the author discusses models and architectures that have been instrumental in the recent progress of Natural Language Processing. As in the first two editions, the intention is to expose the reader to the theories used in Natural Language Processing, and to programming examples that are essential for a deep understanding of the concepts. Although present in the previous two editions, Machine Learning is now even more pregnant, having replaced many of the earlier techniques to process text. Many new techniques build on the availability of text. Using Python notebooks, the reader will be able to load small corpora, format text, apply the models through executing pieces of code, gradually discover the theoretical parts by possibly modifying the code or the parameters, and traverse theories and concrete problems through a constant interaction between the user and the machine. The data sizes and hardware requirements are kept to a reasonable minimum so that a user can see instantly, or at least quickly, the results of most experiments on most machines. The book does not assume a deep knowledge of Python, and an introduction to this language aimed at Text Processing is given in Ch. 2, which will enable the reader to touch all the programming concepts, including NumPy arrays and PyTorch tensors as fundamental structures to represent and process numerical data in Python, or Keras for training Neural Networks to classify texts. Covering topics like Word Segmentation and Part-of-Speech and Sequence Annotation, the textbook also gives an in-depth overview of Transformers (for instance, BERT), Self-Attention and Sequence-to-Sequence Architectures. " --Backcover
521 _aAdult
541 _aPurchased
_xOrtega, Eric
_yCollege of Computer Studies
_zComputer Science
546 _aText in English
650 _aNatural language processing (Computer science).
650 _aPython (Computer program language).
650 _aMachine learning.
650 _aApplication software
_xDevelopment.
942 _2ddc
_cBK
998 _cJanna [new]
_d07/02/2026
999 _c15447
_d15447