Michael Elhadad

Topics in Natural Language Processing (202-2-5381) Fall 2020

Meets: Sun 10-12 Bdg 90 Room 233

News:

  1. 27 Oct 19: Welcome to NLP 20 - Lecture 1
  2. 03 Nov 19: Language Modeling - Lecture 2, Quizz 01
  3. 10 Nov 19: Intro to Deep Learning for NLP - Lecture 3, Quizz 02
  4. 24 Nov 19: Word Embeddings - Lecture 4, Quizz 03
  5. 01 Dec 19: Classification and POS Tagging - Lecture 5, Quizz 04
  6. 07 Dec 19: HW1 posted - due date Mon 23 Dec 2019
  7. 08 Dec 19: Basic Statistical Concepts for ML in NLP, Quizz 05
  8. 15 Dec 19: Sequence Classification, Quizz 06
  9. 22 Dec 19: More on Sequence Classification (CRF), Quizz 07
  10. 05 Jan 20: Syntactic Analysis (1/2): PCFGs, Quizz 08
  11. 05 Jan 20: HW2 posted - due date Thu 16 Jan 2020
  12. 05 Jan 20: Registration for HW1 grading - email me to reserve your preferred slot.
  13. 12 Jan 20: Syntactic Analysis (2/2): Transition-based Depedency Parsing, Quizz 09
  14. 15 Jan 20: HW3 posted - due date Tue 28 Jan 2020
  15. 12 Mar 20: I could not schedule frontal grading sessions in the past weeks due to illness. I will complete grading offline and update you through email.

Contents

Each week there will be a short quizz on the material from the previous week. You are expected to read the material of the week marked with (*) before the lecture on Sunday morning.
  1. General Intro to NLP - Linguistic Concepts
  2. Language Modeling
  3. Intro to Deep Learning for Neural Language Processing
  4. Word Embeddings
  5. Classification and POS Tagging
  6. Basic Statistical Concepts for ML in NLP
  7. Sequence Classification (1/2) HMM
  8. Sequence Classification (2/2): Log-linear models, MEMM and CRF
  9. Syntactic Analysis (1/2): PCFGs
  10. Syntactic Analysis (2/2): Dependency Parsing
  11. Semantic Parsing

Objectives

The course is an introduction to Natural Language Processing. The main objective of the course is to learn how to develop practical computer systems capable of performing intelligent tasks on natural language: analyze, understand and generate written text. This task requires learning material from several fields: linguistics, machine learning and statistical analysis, and core natural language techniques.
  1. Acquire basic understanding of linguistic concepts and natural language complexity: variability (the possibility to express the same meaning in many different ways) and ambiguity (the fact that a single expression can refer to many different meanings in different contexts); levels of linguistic description (word, sentence, text; morphology, syntax, semantics, pragmatics). Schools of linguistic analysis (functional, distributional, Chomskyan); Empirical methods in Linguistics; Lexical semantics; Syntactic description; Natural language semantics issues.
  2. Acquire basic understanding of machine learning techniques as applied to text: supervised, semi-supervised and unsupervised methods; training vs. testing; classification; regression; distributions, KL-divergence; Bayesian methods; Support Vector Machines; Perceptron; Deep Learning methods in NLP; RNNs, LSTMs, sequence to sequence models and attention and transformers.
  3. Natural language processing techniques: word and sentence tokenization; parts of speech tagging; lemmatization and morphological analysis; chunking; named entity recognition; language models; probabilistic context free grammars; probabilistic dependency grammars; text simplification; paraphrase detection; summarization; text generation; topic modelling and semantic parsing.
Topics covered in class include:
  1. Descriptive linguistic models
  2. Language Models -- Statistical Models of Unseen Data (n-gram, smoothing, recurrent neural networks language models)
  3. Language Models and deep learning -- word embeddings, continuous representations, neural networks, sequence to sequence models, attention, transformers
  4. Parts of speech tagging, morphology, non categorical phenomena in tagging
  5. Information Extraction / Named Entity Recognition
  6. Using Machine Learning Tools: Classification, Sequence Labeling / Supervised Methods / SVM. CRF, Perceptron, Logistic Regression
  7. Bayesian Statistics, generative models, topic models, LDA
  8. Syntactic descriptions: Parsing sentence, why, how, PCFGs, Dependency Parsing
  9. Text Summarization
  10. Semantic Parsing


Lecture Notes
  1. 27 Oct 19: General Intro to NLP - Linguistic Concepts

    Things to do:

    1. Find a way to estimate how many words exist in English. In Hebrew. What method did you use? What definition of word did you use? (Think derivation vs. inflection)
    2. Experiment with Google Translate: find ways to make Google Translate "fail dramatically" (generate very wrong translations). Explain your method and collect your observations. Document attempts you made that did NOT make Google Translate fail. (Think variability and ambiguity; Think syntactic complexity; think lexical sparsity, unknown words).
    3. Think of reasons why natural languages have evolved to become ambiguous (Think: what is the communicative function of language; who pays the cost for linguistic complexity and who benefits from it; is ambiguity created willingly or unconsciously?)


  2. 03 Nov 19: Language Modeling
    1. * Complete reading material from Intro on sections: Ambiguity, Variability, Vagueness, Discrete and Sparse Data, Levels of Linguistic Description, Words: Tokens, Parts of Speech and Morphology, Sentences and Syntax
    2. * N-gram Language Modeling Chapter 3 from Speech and Language Processing (SPL3), Jurafsky and Martin, 3rd Ed, 2016. (Focus on 3.1-3.4). If you prefer - the same material covered in slides format.
    3. * Peter Norvig: How to Write a Spelling Corrector (2007).

      This is a toy spelling corrector illustrating the statistical NLP method (probability theory, dealing with large collections of text, learning language models, evaluation methods). Read an extended version of the material with more applications (word segmentation, n-grams, smoothing, more on bag of words, secret code decipher): How to Do Things with Words (Use this local copy adapted to Python 3 / html version and the support files: big.txt, count_1w.txt, count_2w.txt).

    4. Spelling Correction and the Noisy Channel, Appendix B from SPL3, B.1-B.2. This covers similar material as Norvig's piece above in a more formal manner.
    5. Things to do:
      1. * Read about Probability axioms and in more details in the notes from Fredrik Engstrom:
      2. Read about Edit Distance and in more details, a review of minimum edit distance algorithms using dynamic programming from Dan Jurafsky.
      3. * Install Python: I recommend installing the Anaconda distribution (choose the Python 3.7 version). (Note: Many of the code samples you will see are written in Python 2 - which is not exactly compatible with Python 3 - the main annoying difference is that in Python 2 you can write: print x -- in Python 3 it must be print(x). Python3 comes with a utility 2to3 which converts most of the differences between Python 2 and Python 3.]

        The Anaconda distribution includes a large set of Python packages ready to use that we will find useful. (630MB download, 3GB disk space needed.) In particular, Anaconda includes the nltk, pandas, numpy, scipy and scikit-learn packages.

      4. Execute Norvig's spell checker for English (you will neeed the Python code from the article and the large file of text used for training big.txt).
      5. How many tokens are there in big.txt? How many distinct tokens? What are the 10 most frequent words in big.txt?
      6. In a very large corpus (discussed in the ngram piece quoted below), the following data is reported:
        The 10 most common types cover almost 1/3 of the tokens, the top 1,000 cover just over 2/3.
        What do you observe on the much smaller big.txt corpus?
      7. Read more from Norvig's piece on ngrams.
      8. Execute the word segmentation example from Norvig's ngram chapter (code in this notebook).

        Note the very useful definition of the @memo decorator in this example, which is an excellent method to implement dynamic programming algorithms in Python. From Python Syntax and Semantics:

        A Python decorator is any callable Python object that is used to modify a function, method or class definition. A decorator is passed the original object being defined and returns a modified object, which is then bound to the name in the definition. Python decorators were inspired in part by Java annotations, and have a similar syntax; the decorator syntax is pure syntactic sugar, using @ as the keyword:
        @viking_chorus
        def menu_item():
            print("spam")
        	
        is equivalent to:
        def menu_item():
            print("spam")
        menu_item = viking_chorus(menu_item)
        	
      9. This corpus includes a list of about 40,000 pairs of words (error, correction). It is too small to train a direct spell checker that would map word to word. Propose a way to learn a useful error model (better than the one used in Norvig's code) using this corpus. Hint: look at the model of weighted edit distance presented in Jurafsky's lecture cited above.

  3. 10 Nov 2019: Intro to Deep Learning Intro for Natural Language Processing

    Things to do:

    1. * Learn Python if you don't know it (About 4 hours)
    2. Install a good Python environment (About 2 hours) The default environment is pyCharm (the free Community Edition is good for our needs 127MB download). If you already have experienced with it, VSCode also has very good support for Python.
    3. * Follow the NumPy tutorial to learn how to manipulate arrays and tensors efficiently in Python. (2 hours) Check your knowledge with numpy exercises (1 hour).
    4. * Install Pytorch in your environment (1 hour):
      Install Anaconda for Python 3.7.
      
      Install PyTorch: follow instructions from pytorch.org and download the stable Python 3.7.
      If you have an nVidia GPU on your machine, select the appropriate CUDA version that you have installed.
      (Usually, the command to execute is "conda install pytorch -c pytorch").
      		
    5. * Explore the PyTorch tutorials for text - starting from Deep Learning NLP Tutorial - cover the first 3 segments (3 hours).


  4. 24 Nov 19 Word Embeddings

    1. * View Chris Manning's Lecture on Word2Vec Winter 2019 (1h20)
    2. * Read corresponding slides
    3. Execute the following PyTorch tutorials on dense word representations in deep learning:

    Further Reading::

    1. Read Vector Semantics, Chapter 6 of Speech and Language Processing 3rd Ed, Jurafsky and Martin 2018.
    2. Read Efficient Estimation of Word Representations in Vector Space from Mikolov et al (2013) and the Word2vec site.
    3. Read Deep contextualized word representations, Peters et al NAACL 2018 (ELMO)
    4. Read BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Devlin et al NAACL 2019 (BERT).
    5. Read Neural Word Embedding as Implicit Matrix Factorization by Levy and Goldberg, 2014.
    6. Read king - man + woman is queen; but why? by Piotr Migdal, Jan 2017. Good summary of word embeddings with interactive visualization tools, including word2viz word analogies explorer.
    7. FastText provides pre-trained word embeddings in many languages.

    Things to do:

    1. * Install Gensim in your environment (run "conda install gensim") and run the Gensim Word2vec tutorial.
    2. Experiment with Sense2vec with spaCy and Gensim, Source code a tool to compute word embeddings taking into account multi-word expressions and POS tags.


  5. 01 Dec 2019: Parts of Speech Tagging (ipynb)

    Things to do:

    1. Read about the Universal Parts of Speech Tagset (About 2 hours)
    2. Install NLTK: if you have installed Anaconda, it is already installed. Make sure to download the corpora included with nltk.
    3. Explore the Brown corpus of parts-of-speech tagged English text using NLTK's corpus reader and FreqDist object: Use the Universal tagset for all work (About 1 hour)
      • What are the 10 most common words in the corpus?
      • What are the 5 most common tags in the corpus?
    4. Read Chapter 5 of the NLTK book (About 2 hours)
    5. Advanced topics in POS tagging: we will get back to the task of POS tagging with different methods in the following chapters, for more advanced sequenced labeling methods (HMM), Deep Learning based methods using Recurrent Neural Networks, feature-based classifier methods for tagging (CRF), and as a test case for unsupervised EM techniques and Bayesian techniques. You can look at the source code of the nltk.tag module for a feeling of how the tag.hmm, tag.crf and tag.tnt methods are implemented.

      The following papers give a good feeling of the current state of the art in POS tagging: Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss, Barbara Plank, Anders Søgaard and Yoav Goldberg, ACL 2016.

    6. Read A good POS tagger in 200 lines of Python, an Averaged Perceptron implementation with good features, fast, reaches 97% accuracy (by Matthew Honnibal, 2013).
    7. Execute pos-tagging-skl.py, which implements a POS tagger using the Scikit-Learn model, with similar good features, fast, reaches 97% accuracy (80 lines)


  6. 08 Dec 2019: Basic Statistical Concepts for Supervised Machine Learning in NLP

    Things to do:

    1. * Bayesian concept learning from Tenenbaum 1999 - reported in Murphy 2012 Chapter 3. (I also warmly recommend this slightly related ICML 2018 lecture of Josh Tenenbaum - 1h11 Building Machines that Learn & Think Like People).
    2. * Read Deep Learning by Goodfellow, Bengio and Courville, 2016 Chapters 3 and 5. (About 4 hours)
    3. * Watch the 15mn video (ML 7.1) Bayesian inference - A simple example by Mathematical Monk.
    4. Read Statistical Data Analysis and specifically Introduction to Bayesian methods from Cyril Rossant, and execute the associated Jupyter notebooks. (About 1 hour)
    5. Learn how to use Scipy and Numpy - Chapter 1 in Scipy Lectures (Focus on 1.3 and 1.5 - about 5 hours)
    6. Write Python code using numpy, scipy and matplotlib.pyplot to draw the graphs of the Beta distribution that appear in the lecture notes (About 1 hour)
    7. Given a dataset for a Bernouilli distribution (that is, a list of N bits), generate a sequence of N graphs illustrating the sequential update process, starting from a uniform prior until the Nth posterior distribution. Each graph indicates the distribution over μ, the parameter of the Bernouilli distribution (which takes value in the [0..1] range). (About 2 hours)
    8. Learn how to draw Dirichlet samples using numpy.random.mtrand.dirichlet. A sample from a Dirichlet distribution is a multinomial distribution. Understand the example from the Wikipedia article on Dirichlet distributions about string cutting:
         import numpy as np
         import matplotlib.pyplot as plt
         s = np.random.dirichlet((10, 5, 3), 20).transpose()
         plt.barh(range(20), s[0])
         plt.barh(range(20), s[1], left=s[0], color='g')
         plt.barh(range(20), s[2], left=s[0]+s[1], color='r')
         plt.title("Lengths of Strings")
         plt.show()
         
      (About 2 hours)
    9. Compute the MLE estimator μMLE of a binomial distribution Bin(m|N, μ).
    10. Mixture Priors: assume we contemplate two possible modes for the value of our Beta-Binomial model parameter μ. A flexible method to encode this belief is to consider that our prior over the value of μ has the form:
         μ ~ k1Beta(a, b) + k2Beta(c, d)
         where k1 + k2 = 1
         m ~ Bin(μ N)
         
      A prior over μ of this form is called a mixture prior - as it is a linear combination of simple priors.
      1. Prove that the mixture prior is a proper probabilistic distribution.
      2. Compute the posterior density over μ for a dataset where (N = 10, m=8, N-m=2) where k1=0.8 and k2=0.2 and the prior distributions are Beta(1,10) and Beta(10,1). Write Python code to draw the prior density of μ and its posterior density. (About 2 hours)
      3. Experiment with a very simple form of Stochastic Gradient Descent (SGD) with a custom loss function by running this notebook. More examples available on the Autograd project homepage.


  7. 15 Dec 19 Sequence Classification 1/2

    1. * Read Chapter 6: Learning to Classify Text of the NLTK Book (About 2 hours).
    2. * Read Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression, Tom Mitchell, 2015. (About 3 hours)
    3. * Read Michael Collin's notes on Tagging Problems, and Hidden Markov Models: POS tagging and Named Entity Recognition as tagging problems (with BIO tag encoding), generative and noisy channel models, generative tagging models, trigram HMM, conditional independence assumptions in HMMs, estimating the parameters of an HMM, decoding HMMs with the Viterbi algorithm. (about 2 hours)
    4. Watch (ML 8.1) Naive Bayes Classification a 15mn video on Naive Bayes Classification by Mathematical Monk and the following chapter (ML 8.3) about Bayesian Naive Bayes (20 minutes).
    Practical work:
    1. Explore the documentation of the nltk.classify module.
    2. Read the code of the NLTK Naive Bayes classifier and run nltk.classify.naivebayes.demo()
    3. Read the code of the NLTK classifier demos: names_demo and wsd_demo.
    4. Read the documentation on feature extraction in Scikit-learn.
    5. Run the example on document classification in Scikit-learn and the 20 newsgroup dataset using Logistic Regression.
    6. Experiment with the example of classifications in this iPython notebook (code) which shows how to run NLTK classifiers in a variety of ways.
    7. Experiment with the Reuters Dataset notebook (code) illustrating document classification with bag of words features and TF-IDF transformation.


  8. 22 Dec 19 - Sequence Classification 2/2
    1. * Finish reading Michael Collin's notes on Tagging Problems, and Hidden Markov Models
    2. * Read Log-Linear Models, MEMMs, and CRFs by Michael Collins.
    3. * Read Sequence Models and LSTM - PyTorch Tutorial.
    Practical Work:
    1. Explain why the problem of decoding (see 2.5.4 in Tagging Problems, and Hidden Markov Models) requires a dynamic programming algorithm (Viterbi) while we did not need such a decoding step when we discussed classification using Logistic Regression and Naive Bayes?
    2. Implement Algorithm 2.5 (Viterbi with backpointers) from Tagging Problems, and Hidden Markov Models in Python. Test it on the Brown POS tagging dataset using MLE for tag transitions estimation (parameters q) and a discounting language model for each tag in the Universal taget for parameters e(x|tag) for each tag.
    3. Run Sequence Models and LSTM in PyTorch.
    4. CKY parsing interactive demo in Javascript
    5. NLTK tools for PCFG parsing
    6. Notes on computing KL-divergence


  9. 05 Jan 20 Syntax and Parsing
    1. * Context Free Grammars Parsing
    2. * Probabilistic Context Free Grammars Parsing
    3. * Michael Collins's lecture on CFGs and CKY
    4. * Michael Collins's lecture on Lexicalized PCFGs:
      1. Why CFGs are not adequate for describing treebanks: lack of sensitivity to lexical items + lack of sensitivity to structural preferences.
      2. How to lexicalize CFGs with Head propagation.
      3. How to parse a lexicalized PCFG.
    5. CKY parsing interactive demo in Javascript
    6. NLTK tools for PCFG parsing
    7. Notes on computing KL-divergence


  10. 12 Jan 20 Dependency Parsing
    1. * Dependency Parsing by Graham Neubig. Graham's teaching page with github page for exercises.
    2. * Dependency Parsing: Past, Present, and Future, Chen, Li and Zhang, 2014 (Coling 2014 tutorial)
    3. * NLTK Dependency Parsing Howto
    4. Parsing English with 500 lines of Python, an implementation by Matthew Honnibal of Training Deterministic Parsers with Non-Deterministic Oracles, Yoav Goldberg and Joakim Nivre, TACL 2013. (Complete Python code)
    5. Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations, Eliyahu Kiperwasser and Yoav Goldberg, 2016, state of the art neural dependency parser.




Software
Resources

Last modified 12 Mar 2020 Michael Elhadad