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1 Empirical Learning Methods in Natural Language Processing Ido Dagan Bar Ilan University, Israel.

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1 1 Empirical Learning Methods in Natural Language Processing Ido Dagan Bar Ilan University, Israel

2 2 Introduction Motivations for learning in NLP 1.NLP requires huge amounts of diverse types of knowledge – learning makes knowledge acquisition more feasible, automatically or semi-automatically 2.Much of language behavior is preferential in nature, so need to acquire both quantitative and qualitative knowledge

3 3 Introduction (cont.) Apparently, empirical modeling obtains (so far) mainly “first-degree” approximation of linguistic behavior –Often, more complex models improve results only to a modest extent –Often, several simple models obtain comparable results Ongoing goal – deeper modeling of language behavior within empirical models

4 4 Linguistic Background (?) Morphology Syntax – tagging, parsing Semantics –Interpretation – usually out of scope –“Shallow” semantics: ambiguity, semantic classes and similarity, semantic variability

5 5 Information Units of Interest - Examples Explicit units: –Documents –Lexical units: words, terms (surface/base form) Implicit (hidden) units: –Word senses, name types –Document categories –Lexical syntactic units: part of speech tags –Syntactic relationships between words – parsing –Semantic relationships

6 6 Data and Representations Frequencies of units Co-occurrence frequencies –Between all relevant types of units (term-doc, term-term, term-category, sense-term, etc.) Different representations and modeling –Sequences –Feature sets/vectors (sparse)

7 7 Tasks and Applications Supervised/classification: identify hidden units (concepts) of explicit units –Syntactic analysis, word sense disambiguation, name classification, relations, categorization, … Unsupervised: identify relationships and properties of explicit units (terms, docs) –Association, topicality, similarity, clustering Combinations

8 8 Using Unsupervised Methods within Supervised Tasks Extraction and scoring of features Clustering explicit units to discover hidden concepts and to reduce labeling effort Generalization of learned weights or triggering-rules from known features to similar ones (similarity or class based) Similarity/distance to training as the basis for classification method (nearest neighbor)

9 9 Characteristics of Learning in NLP –Very high dimensionality –Sparseness of data and relevant features –Addressing the basic problems of language: Ambiguity – of concepts and features –One way to say many things Variability –Many ways to say the same thing

10 10 Supervised Classification Hidden concept is defined by a set of labeled training examples (category, sense) Classification is based on entailment of the hidden concept by related elements/features –Example: two senses of “sentence”: word, paragraph, description Sense1 judge, court, lawyer Sense2 Single or multiple concepts per example –Word sense vs. document categories

11 11 Supervised Tasks and Features Typical Classification Tasks: –Lexical: Word sense disambiguation, target word selection in translation, name-type classification, accent restoration, text categorization (notice task similarity) –Syntactic: POS tagging, PP-attachment, parsing –Complex: anaphora resolution, information extraction Features (“feature engineering”): –Adjacent context: words, POS In various relationships – distance, syntactic possibly generalized to classes –Other: morphological, orthographic, syntactic

12 12 Learning to Classify Two possibilities for acquiring the “entailment” relationships: –Manually: by an expert time consuming, difficult – “expert system” approach –Automatically: concept is defined by a set of training examples training quantity/quality Training: learn entailment of concept by features of training examples (a model) Classification: apply model to new examples

13 13 Supervised Learning Scheme Classification Model “Labeled” Examples New Examples Classifications Training Algorithm Classification Algorithm

14 14 Avoiding/Reducing Manual Labeling Basic supervised setting – examples are annotated manually by labels (sense, text category, part of speech) Settings in which labeled data can be obtained without manual annotation: –Anaphora, target word selection The system displays the file on the monitor and prints it. Bootstrapping approaches  Sometimes referred as unsupervised learning, though it actually addresses a supervised task of identifying an externally imposed class (“unsupervised” training)

15 15 Learning Approaches Model-based: define entailment relations and their strengths by training algorithm –Statistical/Probabilistic: model is composed of probabilities (scores) computed from training statistics –Iterative feedback/search (neural network): start from some model, classify training examples, and correct model according to errors Memory-based: no training algorithm and model - classify by matching to raw training (compared to unsupervised tasks)

16 16 Evaluation Evaluation mostly based on (subjective) human judgment of relevancy/correctness –In some cases – task is objective (e.g. OCR), or applying mathematical criteria (likelihood) Basic measure for classification – accuracy In many tasks (extraction, multiple class per- instance, …) most instances are “negative”; therefore using recall/precision measures, following information retrieval (IR) tradition Cross validation – different training/test splits

17 17 Evaluation: Recall/Precision Recall: #correct extracted/total correct Precision: #correct extracted/total extracted Recall/precision curve - by varying the number of extracted items, assuming the items are sorted by decreasing score

18 18 Micro/Macro averaging Often results are evaluated for multiple tasks –Many categories, many ambiguous words Macro-averaging: compute results separately for each category and average Micro-averaging (common): refer to all classification instances, from all categories, as one pile and compute results –Gives more weight to common categories

19 19 Course Organization Material organized mostly by types of learning approaches, while demonstrating applications as we go along Emphasis on demonstrating how computational linguistics tasks can be modeled (with simplifications) as statistical/learning problems Some sections covering the lecturer’s personal work perspective

20 20 Course Outline Sequential modeling –POS tagging –Parsing Supervised (instance-based) classification –Simple statistical models –Naïve Bayes classification –Perceptron/Winnow (one layer NN) –Improving supervised classification Unsupervised learning - clustering

21 21 Course Outline (1) Supervised classification Basic/earlier models: PP-attachment, decision list, target word selection Confidence interval Naive Bayes classification Simple smoothing -- add-constant Winnow Boosting

22 22 Course Outline (2) Part-of-speech tagging Hidden Markov Models and the Viterbi algorithm Smoothing -- Good-Turing, back-off Unsupervised parameter estimation with Expectation Maximization (EM) algorithm Transformation-based learning Shallow parsing Transformation based Memory based Statistical parsing and PCFG (2 hours) Full parsing - Probabilistic Context Free Grammar (PCFG)

23 23 Course Outline (3) Reducing training data Selective sampling for training Bootstrapping Unsupervised learning Word association Information theory measures Distributional word similarity, similarity-based smoothing Clustering

24 24 Misc. Major literature sources: –Foundations of Statistical Natural Language Processing, by Manning & Schutze, MIT Press –Articles Additional slide credits: –Prof. Shlomo Argamon, Chicago –Some slides from the book web-site

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