161 - 170 of 183 results for: LINGUIST

Foundational algorithms of non-statistical computational linguistics, including string searching, suffix trees and suffix arrays, finite-state technology for phonology, morphology and dictionary access, classical back-tracking programs for sentence analysis, the use of charts in parsing, generation and translation. Students complete a programming project in one of these areas.

Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. Prerequisites: calculus and linear algebra; CS124 or CS121/221.

Introduction to the variety of writing systems and their behaviors. Classification of all existing scripts as alphabetic, syllabic, ideographic; unifying and differentiating features within each group. How writing captures human language in various ways. The development of the alphabet, from ancient Semitic scripts to modern times. How writing systems are extended to additional languages. Chinese writing, its characteristics and sphere of influence. Japanese writing as a hybrid system that includes Chinese. Korean writing as an ideally designed script. The Indian system of writing as the foundation of numerous Asian syllabic scripts. Unicode as global standard for encoding text in all languages. Font technology: the emulation of human writing in the digital realm. nBasic knowledge of phonetics recommended. Knowledge of foreign languages helpful.

Introduction to spoken language technology with an emphasis on dialogue and conversational systems. Automatic speech recognition, speech synthesis, dialogue management, and applications to digital assistants, search, and spoken language understanding systems. Covers state-of-the-art approaches based on deep learning as well as traditional methods. Prerequisites: CS 124, 221, 224N, or 229

Text information retrieval systems; efficient text indexing; Boolean, vector space, and probabilistic retrieval models; ranking and rank aggregation; evaluating IR systems; text clustering and classification; Web search engines including crawling and indexing, link-based algorithms, web metadata, and question answering; distributed word representations. Prerequisites: CS 107, CS 109, CS 161.

Methods for extracting social meaning (speaker perspectives, emotions and attitudes) from text and speech. Topics include sentiment analysis and summarization, detection of deception, sarcasm, emotion, and personality.nnAnalysis of meaning-bearing characteristics of the speaker and topic, including text, discourse, prosodic and other cues. Prerequisite: CS 124 or 221 or 229 or permission of instructors.

Topics in computational models of language change. Vector semantic models of change in word meaning, word sentiment, and word innovation, computational models of syntactic change and sound change, and models of language evolution like the interated learning paradigm. Prerequisites: LINGUIST 288, LINGUIST 230A, LINGUIST 250, and either LINGUIST 205A or 210A. Or consent of instructor.

Methodology and techniques for teaching languages, using concepts from linguistics and second language acquisition theory and research. Focus is on teaching English, but most principles and techniques applicable to any language. Optional 1-unit seminar in computer-assisted language learning.

Performance with language (speaking, reading, writing, listening, translating or interpreting) is used to measure a person's proficiency or achievement level in the language. Language performance is also used to measure other human characteristics, including psycho-social states and traits. The course will review basic methods in language measurement and cover their use as applied in education, psychology, and commerce. Topics include both traditional and automatic methods for assessing speaking, reading, writing, affect, and language disorders. Students will develop, apply, and evaluate a language test.