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TDP030 Language Technology


This page contains the study materials for the lectures and specifies the central concepts and procedures that you are supposed to master after each section. For more information about how these contents are examined, see the page on Examination.

Course introduction

Welcome to the course! This introductory lecture introduces you to language technology as an application area and to the content and organization of the course, and reviews some basic concepts from the area of text segmentation.

Materials

Learning objectives

After this section you should be able to explain and apply the following concepts:

  • ambiguity, combinatorial explosion
  • tokenization, normalization, stop word
  • word token, word type

Topic 1: Text classification

Text classification is the task of categorizing text documents into predefined classes. In this lecture you will be introduced to text classification and its applications, learn how to evaluate text classifiers using standard validation methods, and get to know the Naive Bayes classifier, a simple but effective probabilistic model for text classification.

Materials

Learning objectives

After this section you should be able to explain and apply the following concepts:

  • accuracy, precision, recall
  • Naive Bayes classifier, log probabilities
  • maximum likelihood estimation, additive smoothing

After this section you should be able to perform the following procedures:

  • evaluate a text classifier based on accuracy, precision, and recall
  • apply the classification rule of the Naive Bayes classifier to a text
  • estimate the probabilities of a Naive Bayes classifier using maximum likelihood estimation and additive smoothing

Topic 2: Language modelling

Language models predict which sentences are more or less probable in a given language. This unit focuses on n-gram-models, which have a wide range of applications such as predictive text input and machine translation. You will learn how to apply, estimate, and evaluate language models.

Materials

Learning objectives

After this section you should be able to explain and apply the following concepts:

  • n-gram model, unigram, bigram
  • maximum likelihood estimation, additive smoothing
  • perplexity, entropy
  • Levenshtein distance

After this section you should be able to perform the following procedures:

  • learn an n-gram model using additive smoothing
  • evaluate an n-gram model using entropy
  • compute the Levenshtein distance between two words

Materials (advanced)

The advanced material for this lecture is the Wagner–Fischer algorithm for computing the Levenshtein distance between two words.

Topic 3: Part-of-speech tagging

A part-of-speech tagger is a computer program that tags each word in a sentence with its part of speech, such as noun, adjective, or verb. In this lecture you will learn how to evaluate part-of-speech taggers, and be introduced to two methods for part-of-speech tagging: hidden Markov models (which generalize the Markov models that you encountered in the lecture on language modelling) and the multi-class perceptron.

Materials

Learning objectives

After this section you should be able to explain and apply the following concepts:

  • part of speech, part-of-speech tagger
  • accuracy, precision, recall
  • hidden Markov model
  • multi-class perceptron, feature window

After this section you should be able to perform the following procedures:

  • evaluate a part-of-speech tagger based on accuracy, precision, and recall
  • compute the probability of a tagged sentence in a hidden Markov model
  • simulate a part-of-speech tagger based on the multi-class perceptron

Materials (advanced)

The advanced material for this section consists of two parts: the Viterbi algorithm for computing the most probable tag sequence for a sentence under a hidden Markov model, and the generalization of part-of-speech tagging to named entity recognition (NER).

Topic 4: Syntactic analysis

Syntactic analysis, also called syntactic parsing, is the task of mapping a sentence to a formal representation of its syntactic structure. In this lecture you will learn about parsing to two target representations: phrase structure trees and dependency trees. The central model for the parsing to phrase structure trees is that of a probabilistic context-free grammar. For parsing to dependency trees, you will learn about the transition-based dependency parsing algorithm, which is also used by Google.

Materials

Learning objectives

After this section you should be able to explain and apply the following concepts:

  • phrase structure tree, dependency tree
  • probabilistic context-free grammar
  • transition-based dependency parser

After this section you should be able to perform the following procedures:

  • learn a probabilistic context-free grammar from a treebank
  • simulate a transition-based dependency parser

Materials (advanced)

The advanced material for this lecture is the CKY algorithm for computing the most probable parse tree for a sentence under a probabilistic context-free grammar.

Topic 5: Semantic analysis

In this lecture you will learn about word senses and the problems they posed for language technology, as well as about two important problems in semantic analysis: word sense disambiguation and word similarity. For each task you will learn about both knowledge-based and data-driven methods, including the popular continuous skip-gram model used in Google’s word2vec software.

Materials

Concepts

  • word sense, homonymy, polysemy
  • synonymy, antonymy, hyponymy, hypernymy, WordNet
  • Simplified Lesk algorithm
  • word similarity, distributional hypothesis, co-occurrence matrix

Procedures

  • simulate the Simplified Lesk algorithm
  • compute the path length-based similarity of two words
  • derive a co-occurrence matrix from a document collection

Page responsible: Marco Kuhlmann
Last updated: 2021-01-15