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Deep Learning per la Comprensione del Linguaggio Naturale - HKN

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Presentazione per il 24 Marzo 2018

Evento: "Introduzione al Machine Learning" organizzato da HKNPolito https://hknpolito.org/ al DAUIN.

"Una panoramica di come il Deep Learning viene applicato per la comprensione del Linguaggio Naturale: dalla trasformazione delle parole in vettori semantici all'elaborazione di sequenze con Recurrent Neural Networks. Gli usi più comuni sono la classificazione di frasi e il riconoscimento delle entità menzionate."

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Deep Learning per la Comprensione del Linguaggio Naturale - HKN

  1. 1. Deep Learning per la Comprensione del Linguaggio Naturale 24 Marzo 2018 1
  2. 2. Agenda - NLP e Deep Learning - RNN - input 2
  3. 3. Introduzione a NLP+DL 3
  4. 4. Cos’è il Natural Language Processing Goal: far processare / “capire” ai computer il linguaggio naturale 4
  5. 5. Cosa si può ottenere - Agenti interattivi - Assistenti virtuali - Chatbot - Strumenti di analisi testo - web crawling - analisi recensioni - Strumenti linguistici - correttore - traduttore 5
  6. 6. Esempi NLP - Classificazione testuale - spam filter - intent - sentiment analysis 6
  7. 7. Esempi NLP - Estrazione entità 1. riconoscimento dei frammenti di testo 2. risoluzione entità 3. capire le loro relazioni https://demos.explosion.ai/displacy-ent/ 7
  8. 8. Esempi NLP - Coreference resolution https://huggingface.co/coref/ 8
  9. 9. Esempi NLP - Statistical Parsing https://demos.explosion.ai/displacy/ 9
  10. 10. Esempi NLP - Traduzioni - N lingue → N × (N-1) combinazioni 10
  11. 11. Esempi NLP - Generare risposte https://cakechat.replika.ai/ 11
  12. 12. Perché il Deep Learning? - features vengono scelte dalla rete - migliori performance rispetto a ML - abbiamo le GPU - abbiamo tanti dati 12
  13. 13. Recurrent Neural Networks elaborare sequenze 13
  14. 14. Recurrent Neural Networks feedforward recurrent 14
  15. 15. Temporal Unfolding 15
  16. 16. Applicazione alle sequenze 16
  17. 17. Backpropagation gradient descent 17
  18. 18. Backpropagation Through Time 18
  19. 19. Gradienti e chain rule Per ogni istante temporale si espande 19
  20. 20. Le celle ricorrenti - BasicRNN problema del “vanishing gradient” 20
  21. 21. Le celle ricorrenti - LSTM [1] Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural computation - MIT Press 21
  22. 22. Le celle ricorrenti - GRU [2] Cho, K. et al. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. Proc. Conference on Empirical Methods in Natural Language Processing 1724–1734 22
  23. 23. Esempi di approcci (1) - classificazione intento [3] Liu, B. and Lane, I. (2016). Attention-based recurrent neural network models for joint intent detection and slot filling. Proceedings of The 17th Annual Meeting of the International Speech Communication Association 23
  24. 24. Esempi di approcci (2) - slot filling [4] Liu, B. and Lane, I. (2016). Attention-based recurrent neural network models for joint intent detection and slot filling. Proceedings of The 17th Annual Meeting of the International Speech Communication Association IOB = Inside Outside Beginning 24
  25. 25. Esempi di approcci (3) - traduzioni [5] Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. Proc. International Conference on Learning Representations 25 allineamento
  26. 26. [6] Luong, Minh-Thang, and Christopher D. Manning. "Achieving open vocabulary neural machine translation with hybrid word-character models." arXiv preprint arXiv:1604.00788 (2016). Esempi di approcci (3) - traduzioni 26
  27. 27. Esempi di approcci (4) - generazione di risposte [7] Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., & Pineau, J. (2016, February). Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models. In AAAI (Vol. 16, pp. 3776-3784). 27
  28. 28. Gli input il significato delle parole 28
  29. 29. Come passare le parole alla Rete Neurale? parole → numeri - dizionario: parola ←→ indice - stemming + dizionario - lemmatization + dizionario - reti di sinonimi: WordNet [8] rappresentazioni discrete [8] https://wordnet.princeton.edu/ 29
  30. 30. Rappresentazioni distribuite Distributional Semantics [9]: parole usate in contesti simili hanno significato simile [9] Harris, Z. S. (1970). Distributional structure. In Papers in structural and transformational linguistics (pp. 775-794). Springer, Dordrecht. 30
  31. 31. Word embeddings - cosa sono - ogni parola ha il suo vettore di reali - distribuzione semantica - dimensionalità ridotta 31
  32. 32. Word embeddings - come calcolarli (1) Count based: 1. Window-based co- occurrence [10] 2. Singular Value Decomposition [10] Rohde, D. L., Gonnerman, L. M., & Plaut, D. C. (2006). An improved model of semantic similarity based on lexical co-occurrence. Communications of the ACM, 8(627-633), 116. 32
  33. 33. Word embeddings - come calcolarli (2) direct prediction: Word2Vec [11] [11] Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. ICLR Workshop, 2013. 33
  34. 34. Word embeddings - come calcolarli (3) ibridi: GloVe [12] [12] Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543). subword-level: FastText [13] [13] Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2016). Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606. 34
  35. 35. Word embeddings: su cosa calcolarli A partire da corpus: - Wikipedia: https://dumps.wikimedia.org/ ~1-15GB - CommonCrawl: http://statmt.org/ngrams/ ~100GB-5TB Precalcolati: - GloVe: https://nlp.stanford.edu/projects/glove/ - FastText: https://github.com/facebookresearch/fastText 35
  36. 36. Word embeddings: applicazioni (1) - vicinanza semantica 36
  37. 37. Word embeddings: applicazioni (2) - visualizzazione spaziale visualizzazione spaziale t-SNE [14] [14] Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t- SNE. Journal of machine learning research, 9(Nov), 2579-2605. 37
  38. 38. Word embeddings: applicazioni (3) - relazioni e operazioni 38
  39. 39. Word embeddings: applicazioni (4) - input layer per reti neurali 39
  40. 40. Riferimenti Natural Language Processing with Deep Learning, Stanford http://web.stanford.edu/class/cs224n/ http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through- time-and-vanishing-gradients/ http://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/ https://www.tensorflow.org/tutorials/word2vec http://slides.com/smerity/common-crawl-for-nlp queste slides: http://bit.ly/dl-nlp-hkn 40

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