Text Analysis and Retrieval

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Text Analysis and Retrieval

Course Description

Most human knowledge is stored in unstructured, textual format. Due to the vast and rapidly growing amount of text data available, text analysis and retrieval systems have become an indispensable part of modern ICT infrastructure. Such systems address diverse information needs of the users and enable the extraction of information from large volumes of unstructured data. Because of the complexity and ambiguity of natural language, text analysis is a non-trivial task, which relies on natural language processing, computational linguistics, and machine learning. This course provides a systematic overview of both traditional and advanced methods for text analysis and retrieval. The first part of the course deals with the fundamentals of information retrieval and natural language processing techniques relevant for text analysis. The second part deals with applications in text analysis and retrieval, with an emphasis on methods based on statistical natural language processing and machine learning.

Learning Outcomes

Summarize the application areas, trends, and challenges in text analysis and retrieval
Describe the fundamental techniques of text analysis and retrieval
Use linguistic preprocessing tools
Design and implement a text analysis/retrieval system
Apply machine learning algorithms to text analysis tasks
Evaluate a text analysis/retrieval system
Organize and formulate a system description paper
Describe, review, analyze, and criticize the main text analysis methods present in scientific papers

Forms of Teaching

Lectures

Seminars and workshops

Independent assignments

Laboratory

Other

Week by Week Schedule

Document classification and tagging, Document clustering, Information needs, relevance, evaluation, effectiveness, Applications in information retrieval and text mining
Computational morphology, Part of speech tagging, Deterministic and stochastic grammars, constituency and dependency grammars (CFG, PCFG), Parsing algorithms (CYK, Chart), lexicalized parsing, dependency parsing, Language models, smoothing, evaluation
Text preprocessing (stemming, phrases, stop lists), Information retrieval models (vector space, probabilistic, Boolean), Information needs, relevance, evaluation, effectiveness, Advanced information retrieval techniques (semantic search, faceted search), Web search (PageRank and HITS)
Markov and hidden Markov models, Conditional random fields, Confusion matrix-based performance measures (accuracy, precision, recall, sensitivity, F-score), Multiclass performance measures, Assessing inter-annotator agreement (Cohen's kappa, Fleiss' kappa)
Latent semantic document models (LSI, LDA), Computational semantics (formal semantics, semantic role labeling), Distributional semantic models
Neural natural language processing, Deep recurrent neural networks: RNN, bidirectional RNN, deep RNN, long short-term memory, sequence modelling, applications
Applications in information retrieval and text mining
Not held
Text information extraction (named entities, keyphrases, relations, etc;), Event detection and tracking
Question answering
Document summarization, multidocument summarization, Textual similarity, paraphrase, and entailment
Textual similarity, paraphrase, and entailment
Sentiment analysis and opinion mining
Authorship analysis and author profiling, Project
Not held