151 - 160 of 183 results for: LINGUIST

Practical training in the collection and analysis of linguistic data from native speakers of a language largely unknown to the investigator. Documentation of endangered languages. Research goals, field trip preparation, ethics (including human subjects, cooperation with local investigators, and governmental permits), working in the community, technical equipment, and analytical strategies. Emphasis is on the use of recording devices and computers in collection and analysis. Students are strongly encouraged to make a commitment to both 274A and 274B in the same year. Prerequisites: One course in phonetics or phonology and syntax, or permission of the instructor. Open to undergraduates with permission of instructor only.

Continuation of 274A, with a focus on student projects in a targeted language. Prerequisite: 274A or consent of instructor. Graduate students are strongly encouraged to make a commitment to both 274A and 274B in the same year. For full credit, students are expected to work privately with the consultant outside of class time.

Prerequisites include one quarter of phonology and one quarter of syntax or permission of instructor. Graduate students are heavily encouraged to make a commitment to both 274B and 274C in the same year

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.

Introduction to methods for collecting and analyzing quantitative linguistic data, with a primary focus on the use of corpora in exploring theoretical questions in various areas of linguistics. Topics include the access and retrieval of corpus data (including web-based corpora), data annotation, and statistical modeling. Practical experience with R, Python scripting, and setting up online experiments through Amazon Mechanical Turk.

Issues that commonly arise in the design and implementation of linguistic experiments and in the statistical analysis of empirical results. Topics in experimental design include selection of stimuli, blocking, and power analysis and sample size calculation. How to fit and interpret statistical models using the multilevel regression and Bayesian inference, as implemented in software packages R and Bugs. Topics include interpretation of model coefficients for fixed and random effects, collinearity, model criticism, as well as comparison and reporting of models. Theoretical issues worked out at lab sessions using examples from experiments and corpus studies, including those provided by students.

Computer programming techniques for collecting and analyzing data in linguistic research. Introduction to the UNIX, regular expressions, and Python scripting. Hands-on experience gathering, formatting, and manipulating corpus, field, and experimental data, combining data from multiple sources, and working with existing tools. Knowledge of computer programming not required.

Extracting meaning, information, and structure from human language text, speech, web pages, genome sequences, social networks. Methods include: string algorithms, edit distance, language modeling, the noisy channel, naive Bayes, inverted indices, collaborative filtering, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, chatbots, genomic sequence alignment, spell checking, speech processing, recommender systems. Prerequisite: CS103, CS107, CS109.

This seminar will explore the computational properties of a small set of formalisms from phonology, morphology, syntax, and semantics, the choice depending on the interests of the participants. Possible topics include, but are not limited to, finite-state techniques, Optimality Theory, Unification-based grammar, Montague Grammar, Sound change, Corpus-based exploration, and Translation.