Sequence modeling in language Language modeling I Language modeling is the task of assigning a probability to a sequence (for evaluating translation candidates). I It is also the task of assigning probability of a word to follow a sequence of words (for language generation). I Perfect performance in language modeling means being able to predict […]
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Recurrent Neural Networks (RNNs) for sequence labeling Long distance dependency in sequence labeling Limitation of window based feature extraction for linear sequence models can reach a very high accuracy, but are insucient in some cases: I POS tagging: The man who whistles tunes VBZ pianos I Named Entity Recognition: Norma Jean’s song entitled “Pretty soon
程序代写代做代考 C chain html algorithm Recurrent Neural Networks (RNNs) for sequence labeling Read More »
Perceptron learning algorithm The multiclass preceptron learning algorithm procedure Perceptron(x1:N,y1:N) t 0 ✓(0) 0 repeat tt+1 Select an instance i yˆ argmaxy ✓(t1) ·f(x(i),y) ifyˆ6=y(i)then 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: else 11: ✓(t) 12: end if 13: until tired 14: end procedure . Online training ✓(t) ✓(t1) + f (x(i),
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The general paradigm of supervised learning I The goal of a large family of machine learning is to minimize the prediction errors of the model I Ideally we want to predict the true errors, errors made by the I model when it is used in realistic scenarios That is hard to do, so the common
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Linear Text classification Problem defintion: Given a text document, assign it a discrete label y 2 Y where Y is the set of possible labels. Many possible applications: I Spam filter: Y = {Spam, non-spam} I Sentiment: Y = {Positive, negative, neutral} I Genre classification: Y = {sports, fiction, news, · · · } Bag-of-words
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Generative approaches: Hidden Markov Models Hidden Markov Models (HMM): the generative story The generative story: first, the tags are drawn from a prior distribution; next, the tokens are drawn from a conditional likelihood. y0 ⌃,m 1 repeat ym ⇠ Categorical(ym1) . sample the current tag wm ⇠ Categorical (ym ) . sample the current word
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Transition-based dependency parsing Transition systems for dependency parsing Transitions are between configurations that are represented as triples C ! (, , A), where is the stack, is the input bu↵er, and A is the list of arcs that have been created. Transition systems: I Arc-standard I Arc-eager The arc-standard system The arc-standard system is closely
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Transition-based syntactic parsing Transition-based syntactic parsing I Transition-based constituent parsing I Transition-based dependency parsing I Transition-based AMR parsing Transition-based Constituent Parsing I A transition-based constituent parsing model is a quaduple C = (S,T,s0,St) where: I S is a set of parser states or configurations, I T is a set of actions, e.g., shift, reduce, I
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COMP3311 20T3 The University of New South Wales Database Systems COMP3311 Database Systems 20T3 [Instructions] [Notes] [Database] [Course Website] [Cheat Sheets] [Q1] [Q2] [Q3] [Q4] [Q5] [Q6] [Q7] [Q8] [Q9] [Q10] [Q11] [Q12] Question 11 (6 marks) Consider the following spreadsheet used by a lecturer to manage marks in her courses: A B C D
Syntactic parsing approaches I Grammar-based approach with CKY decoding I PCFG, a generative approach that extends the Naive Bayes Model I Lexicalization, parent annotation I Discrminative approaches: linear and neural models I Perceptron and CRF training with discrete features I Neural models I Transition-based approach: the shift-reduce algorithm with greedy or beam search I Linear
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