Natural Language Processing

From Ioannis Kourouklides
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This page contains resources about Natural Language Processing, Text Mining, Speech Processing, Audio Signal Processing and Computational Linguistics.

Subfields and Concepts[edit]

  • Vector Space Model (VSM)
  • Latent Semantic Indexing
  • Latent Semantic Analysis
  • Latent Dirichlet Allocation
  • Attention Mechanism
  • Speaker Recognition / Speaker Identification / Speaker Diarization
  • Speech Synthesis / Text-to-Speech
  • Speech Recognition / Voice Recognition / Speech-to-Text / Transcription
    • Conversational Speech
    • Voice Dictation
    • Voice Commands
  • Audio Captioning / Subtitling
  • Automatic Lyrics Recognition
  • Topic Model
  • Text Preprocessing
    • Word embeddings / Word vectors
      • word2Vec Model
        • Continuous Skip-gram
        • Continuous Bag-of-Words (CBOW)
      • GloVe
      • FastText
    • Bag-of-Words (BoW) Model
    • N-grams
      • Unigrams
      • Bigrams
  • Term Frequency - Inverse Document Frequency (TF-IDF)
  • Sequence-to-Sequence (seq2seq) Model
  • Dynamic Memory Network (a specific architecture of Artificial Neural Networks)
  • Sequence Tagging
  • Natural Language Generation (NLG)
  • Named-Entity Recognition (NER)
  • Sentiment Analysis
  • Emotion Recognition
  • Diacritization (e.g. in Hebrew or Arabic)
  • Chatbots
  • Question-Answering System

Online Courses[edit]

Video Lectures[edit]

Lecture Notes[edit]

Books[edit]

Natural Language Processing[edit]

  • Arumugam, R., & Shanmugamani, R. (2018). Hands-On Natural Language Processing with Python. Packt Publishing.
  • Srinivasa-Desikan, B. (2018). Natural Language Processing and Computational Linguistics. Packt Publishing.
  • Goldberg, Y., & (2017). Neural Network Methods for Natural Language Processing. Morgan & Claypool Publishers.
  • Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. O'Reilly Media, Inc.
  • Sarkar, D. (2016). Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from Your Data. Apress. (link)
  • Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. O'Reilly Media, Inc. (link)
  • Manning, C. D., & Schutze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.

Speech Processing[edit]

  • Rabiner, L. R., & Schafer, R. W. (2011). Theory and applications of digital speech processing. Pearson.
  • Gold, B., Morgan, N., & Ellis, D. (2011). Speech and audio signal processing: processing and perception of speech and music. 2nd Ed. John Wiley & Sons.
  • Mitra, S. K., & Kuo, Y. (2010). Digital signal processing: a computer-based approach. 4th Ed. McGraw-Hill.
  • Spanias, A., Painter, T., & Atti, V. (2006). Audio signal processing and coding. John Wiley & Sons.
  • Quatieri, T. F. (2001). Discrete-time speech signal processing: principles and practice. Pearson.
  • Rabiner, L. R., & Schafer, R. W. (1978). Digital processing of speech signals. Prentice Hall.

Speech and Natural Language Processing (both)[edit]

  • Jurafsky, D., & Martin, J. H. (2000). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall.

Scholarly articles[edit]

  • Wen, T. H., Gasic, M., Mrksic, N., Su, P. H., Vandyke, D., & Young, S. (2015). Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems. arXiv preprint arXiv:1508.01745.
  • Sethu, V., Epps, J., & Ambikairajah, E. (2015). Speech based emotion recognition. In Speech and Audio Processing for Coding, Enhancement and Recognition (pp. 197-228). Springer.
  • Blei, D. M. (2012). Probabilistic Topic Models. Communications of the ACM, 55(4), 77-84.

Tutorials[edit]

Software[edit]

Speech Recognition[edit]

Miscellaneous[edit]

See also[edit]

Other Resources[edit]