PPT-Albert Gatt

LIN3021 Formal Semantics Lecture 8 In this lecture Noun phrases as g eneralised quantifiers Part 1 A bit of motivation Not all NPs are referential Weve mainly considered NPs that refer ie Pick out an individual or a kind in some sense. Corpora and statistical methods. In this lecture. Overview of rules of probability . multiplication rule. subtraction rule. Probability based on prior knowledge. conditional probability. Bayes’ theorem. Corpora and Statistical Methods. In this lecture. Corpora and Statistical Methods. We have considered distributions of words and lexical variation in corpora.. Today we consider collocations:. definition and characteristics. LIN 1180 – Semantics. Lecture 8. Hyponymy and other relations. Part 1. Definition of hyponymy. LIN 1180 -- Semantics. Hyponymy is a . relation of inclusion. .. Arrows can be interpreted as “IS-A” relations.. LIN1180 Semantics. Lecture 4 . Reference and proper names. Continuation of last week. Proper names. Semantics -- LIN 1180. These . seem to be “purely referential”, establishing a direct relationship to things/people in the world.. LIN3021 Formal Semantics. Lecture . 9. In this lecture. Noun phrases as g. eneralised quantifiers: some further concepts. What we’ve said so far. In a. sentence of the form . [[. Det. . NP. ]. subj. LIN1180/LIN5082 . Semantics. Lecture 2. Goals of this lecture. Semantics -- LIN 1180. To introduce some of the central concepts that semanticists use in their work.. To delve a little deeper into the notions of . LIN1180 Semantics. Lecture 5 . Theories of concepts I: Necessary and sufficient conditions. Semantics -- LIN 1180. We considered the pros and cons of the classical theory of concepts:. Necessary and sufficient conditions. LIN 1180 – Semantics. Lecture7. Ambiguity and vagueness. Continuation from last week. Ambiguity vs. Vagueness (I). In context, a word can seem to have several distinct senses. Some may appear more related than others.. LIN3022 Natural Language Processing. Lecture 9. In this lecture. We continue with our discussion of parsing algorithms. We introduce dynamic programming approaches. We then look at . probabilistic context-free grammars. Corpus search. These notes . introduce. some practical tools to find patterns:. regular expressions. A general formalism to represent . finite-state automata. the . corpus query language (. CQL. /CQP. Lecture . 5. In this lecture. We begin to consider the problem of . lexical acquisition . beyond collocations. syntax-semantics interface:. verb subcategorisation frames. prepositional phrase attachment ambiguity. Albert Gatt Corpora and Statistical Methods Lecture 1 2 In this lecture Introduction to Natural Language Generation (NLG ) the use of corpora & statistical models in NLG Summarisation Single-document Lecture 7. In this lecture. We consider the task of Part of Speech tagging. information sources. solutions using markov models. transformation-based learning. POS Tagging . overview. Part 1. The task (graphically). created in 1947. with its headquarter in Geneva. Its motive was to . promote free trade through multilateral trade negotiations.. Three important principles. were:. Non-discrimination:. It meant Most-.