71 - 80 of 162 results for: LINGUIST

Linguistics 265 is a new, advanced course on African American Vernacular English, intended for graduate students in Linguistics, Education and other fields, and fornundergraduate majors in Linguistics. Students who have taken Linguistics 65 or its equivalent, or who have had an undergraduate introduction to linguistics, arenalso eligible to take this course.nnThe course will discuss in detail some of the descriptive, historical and sociolinguistic literature on AAVE, beginning with the classic book length works on AAVE writtennby William Labov, Walt Wolfram and Ralph Fasold, but including some of the most recent research-based articles on the subject in current and recent journals. Researchninterests of students in the course will help to determine the specific foci within these broad parameters.

Introduction to probability and statistical inference, with a focus on conceptual and practical issues relevant to theoretical, experimental, and corpus linguistics. Data analysis and modeling using R. Course project will involve reproducing a published modeling result or statistical analysis in full detail.

Extracting meaning, information, and structure from human language text, speech, web pages, genome sequences, social networks, or any less structured information. Methods include: string algorithms, edit distance, language modeling, naive Bayes, inverted indices, vector semantics. Applications such as question answering, sentiment analysis, information retrevial, text classification, social network models, machine translation, genomic sequence alignment, spell checking, speech processing. Prerequisite: CS103, CS107, CS109.

Construction of computer programs for linguistic processes such as string search, morphological, syntactic, and semantic analysis and generation, and simple machine translation. Emphasis is on the algorithms that have proved most useful for solving such problems.

Methods for processing human language information and the underlying computational properties of natural languages. Syntactic and semantic processing from linguistic and algorithmic perspectives. Focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, translation, and interpretation; and representative systems. Prerequisites: CS124 or CS121/221.

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: classification algorithms, latent semantic indexing, taxonomy induction; Web search engines including crawling and indexing, link-based algorithms, and web metadata. 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.

Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Prerequisites: one of LINGUIST 180, CS 124, CS 224N, CS224S, or CS221; and logical/semantics such as LINGUIST 130A or B, CS 157, or PHIL150

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.