COMP90042 Web Search and Text Analysis The aims for this subject is for students to develop an understanding of the main algorithms used in natural language processing and text retrieval, for use in a diverse range of applications including text classification, information retrieval, machine translation, and question answering. Topics to be covered include vector space models, part-of-speech tagging, n-gram language modelling, syntactic parsing and neural sequence models. The programming language used is Python, see for more information on its use in the workshops, assignments and installation at home. Class hours Lectures Tue 11-12pm Redmond Barry-104 (Medley Theatre) Wed 2:15-3:15pm Chemistry-189 (Masson Theatre) Workshops from week 2 onwards. You will be assigned to one of the following timeslots Mon 11-12pm Alice Hoy-108 Mon 7:15-8:15pm Alice Hoy-108 Tues 10-11pm Alice Hoy-222 Fri 2:15-3:15pm Alice Hoy-236 Fri 5:15-6:15pm Alice Hoy-211 Please see for each week's worksheet. Python Tutorial in the first week, optional Fri 2:15-3:15pm Alice Hoy-236 The instructors for the subject are Trevor Cohn and Julian Brooke.

Office hour If you have questions, please attend the weekly office hour run by one of Julian or Trevor (UPDATED!): Tuesday 2:15-3:15pm Doug McDonell 10. Kktc Telefon Rehberi Indir 2010. 22 Also feel free to post your questions, preferably to the discussion forum in the LMS, or else by e-mail (Trevor; Julian ). Please don't expect an immediate response, but we aim to get back to you within a business day or two.

Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Daniel Jurafsky and James H. Draft of September 28, 1999. Do not cite without permission. Contributing writers: Andrew Kehler, Keith Vander Linden, Nigel Ward. Prentice Hall. Speech and Language Processing (second edition). Daniel Jurafsky and James H. (Stanford University and University of Colorado at Boulder). Ronan Keating When You Say Nothing At All Rapidshare Downloads. Pearson Prentice Hall, 2009, xxxi+988 pp; hardbound, ISBN 978-0-13-187321-6, $115.00. Vlado Keselj. Dalhousie University.

The tutors for the subject are Karl Grieser, Yuan Li, and Jeremy Nicholson. Textbooks There are three four main textbooks for the class: JM2 Jurafsky, Daniel S.; Martin, James H.; Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, Prentice Hall, Second Edition 2008. Out of print. Selected chapters are hosted in the LMS, under Reading. JM3 Jurafsky, Daniel S.; Martin, James H.;, Third Edition (partial draft). IIR Manning, Christopher D; Raghavan, Prabhakar; Sch端tze, Hinrich;, Cambridge University Press 2008.

Koehn09 Koehn, Philipp;, Cambridge University Press 2009. For the JM texts, we will be using a mixture of chapters from the out-of-print 2nd edition (2008) and the not-yet-released 3rd edition. All the readings will be made available online, please use the links below in the Materials column (or download the PDF from the LMS for JM2). We will be also using the NLTK software tools extensively in this class, so we also recommend: NLPP Steven Bird, Ewan Klein, Edward Loper;, O'Reilly, 2009. Please see for each week's reading. Assignments There will be 4 short homework assignments released every other week (starting Week 2) and due the following week, and a final project due at the end of the class.

Schedule We'll put the lecture slides up here as we cover the material, as well as pointers to the required reading. Date Topic Materials Tue 28/2 Introduction and Preprocessing Slides: Reading: Notebook: Wed 1/3 Text classification Slides: Reading:, Notebook: Fri 3/3 Python introduction (optional) Notebook: from Mon 6/3 Workshop on preprocessing and text classification Worksheet: Tue 7/3 Part of Speech Tagging Slides: Reading: JM2 Ch. 5.1-5.4,5.6-5.8 Notebook: Wed 8/3 Probablisitic Sequence Modelling Slides: Reading: JM2 Ch. 6 Or alternatively Advanced (just for fun) Notebook: from Mon 13/3 Workshop on part of speech tagging and probabilistic sequence modelling Worksheet: Tue 14/3 Context-Free Grammars Slides: Reading: JM2 Ch.

12.1-12.5, JM2 Ch. 13.1-13.4 Notebook: Wed 15/3 Probablisitic Parsing Slides: Reading: JM2 Ch. 14 Notebook: from Mon 20/3 Workshop on context free grammars and probabilistic parsing Worksheet: Tue 21/3 Dependency parsing Slides: Reading: Wed 22/3 Lexical semantics Slides: Reading: JM2 Ch. 19.1-19.4 JM2 Ch.