[et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” custom_padding=”||17px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” background_color=”#EFEFEF” width=”86%” max_width=”1206px” module_alignment=”center” border_radii=”on|17px|17px|17px|17px” border_width_all=”1px” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.7″ _module_preset=”default” header_font=”–et_global_body_font|700|||||||” header_text_align=”center” header_font_size=”37px” global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”2_3,1_3″ _builder_version=”4.27.4″ _module_preset=”default” max_width=”1202px” custom_padding=”|||0px||” global_colors_info=”{}”][et_pb_column type=”2_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”About the Program” admin_label=”About the Program” _builder_version=”4.27.4″ _module_preset=”default” title_font=”–et_global_body_font||||||||” custom_margin=”||18px|||” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.7″ _module_preset=”default” text_font_size=”16px” global_colors_info=”{}”]<\/p>\n
This program is run jointly between the Department of Computer Science and the Department of Linguistics, TESOL and Communication Science and Disorders.<\/p>\n
Computational linguistics is a field that brings current computational techniques to the analysis of human language. The goal of this program is to develop professional and analytical skills that will enable our graduates to serve in industry, public sector and NGO roles where training in these areas is in demand. Computational Linguistics is central to much of the most important emerging work in the computational sciences and related industry, including large language models, language-learning applications, automatic speech recognition, text-to-speech, application development for under-resourced languages, and computational therapies for patients with speech and language disorders.<\/p>\n
The program is designed to build skills in computational text processing, application development, and language analysis. Specifically, the coursework in formal and experimental linguistics includes content in acoustic phonetics and natural language phonology central to application development for automatic speech recognition, speech-to-text and text-to-speech in natural languages, as well as content in natural language syntax and morphology and corpus analysis central to search applications and large language models. In addition the curriculum includes training in mathematics, statistics, data structures, basic coding and application as currently used in industry in language-based applications.<\/p>\n
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[\/et_pb_text][et_pb_button button_url=”https:\/\/www.qc.cuny.edu\/admissions\/apply-to-queens-college\/” url_new_window=”on” button_text=”Apply Now to Queens College!” button_alignment=”center” _builder_version=”4.27.4″ _module_preset=”default” custom_button=”on” button_text_size=”20px” button_text_color=”#FFFFFF” button_bg_color=”#E71939″ button_border_color=”#E71939″ global_colors_info=”{}”][\/et_pb_button][et_pb_accordion icon_color=”#E71939″ use_icon_font_size=”on” icon_font_size=”20px” _builder_version=”4.27.7″ _module_preset=”default” toggle_font=”–et_global_body_font|600|||||||” toggle_font_size=”18px” custom_margin=”||33px||false|false” custom_padding=”||0px||false|false” db_initial_state=”all_closed” border_width_left=”7px” border_color_left=”#E71939″ global_colors_info=”{}”][et_pb_accordion_item title=”Requirements for the Major” open=”on” _builder_version=”4.27.7″ _module_preset=”default” body_font_size=”16px” global_colors_info=”{}”]<\/p>\n
Required courses (24 credits)<\/strong><\/p>\n LCD 101, MATH 114 OR 241, LCD 116, LCD 120, LCD 150, LCD 151, LCD 220, LCD 250, LCD 251, CSCI 357, CSCI 366.<\/p>\n Electives (6 credits from among the following)<\/strong><\/p>\n LCD 205, LCD 209, LCD\/ANTH 281, LCD 306, CSCI 313, CSCI 325, LCD 360, CSCI 363<\/p>\n Note:\u00a0 No course will count toward this major with a grade lower than C-.<\/em><\/p>\n<\/div>\n<\/div>\n [\/et_pb_accordion_item][et_pb_accordion_item title=”Course Descriptions” _builder_version=”4.27.7″ _module_preset=”default” body_font_size=”16px” global_colors_info=”{}” open=”off”]<\/p>\n LCD 101. Introduction to Language<\/strong>. 3 hr.; 3 cr. A survey of the study of language: Structure, language and society, first and second language acquisition, and other related topics. Fulfils College Option: Language. Taught Fall, Spring, and Summer.<\/p>\n MATH 114.\u00a0 Elementary Probability & Statistics.<\/strong> 3 hr.; 3 cr. An introduction to mathematical probability and statistics for the general student. Not open to mathematics, physics, or chemistry majors. Not open to mathematics, physics, or chemistry majors, or to students who are taking or have passed MATH 114W, 241, 611, 621, 633, BIOL 230, ECON 249, PSYCH 107, SOC 205, 206, 207. Not open to students who will be receiving transfer credit or advanced placementcredit for MATH 114.<\/span> Taught Fall, Spring, and Summer.<\/p>\n MATH 241. Introduction to Probability and Mathematical Statistics<\/span>.<\/strong> 3 hr.; 3 cr. An introduction to the basic concepts and techniques of probability and statistics with an emphasis on applications. Topics to be covered include the axioms of probability, combinatorial methods, conditional probability, discrete and continuous random variables and distributions, expectations, confidence interval estimations, and tests of hypotheses using the normal, t- and chi-square distributions. Students taking this course may not subsequently receive credit for MATH 114, except by permission of the chair. Not open to students who are taking or have received credit for MATH 611<\/span>.<\/span> Taught Fall, Spring.\u00a0 Note:<\/b> students are advised to select MATH 114 or 241 for their General Education Math and Quantitative Reasoning course.\u00a0 Otherwise, students will be required to complete MATH 114 or 241 in lieu of one three-credit elective, under advisement.<\/p>\n LCD 116. Introduction to Morphology.\u00a0<\/strong>3 hr; 3 cr. Morphological theory; how words are formed; rules for determining the meaning and pronunciation of words cross-linguistically. Taught Fall.<\/p>\n LCD 120.<\/strong>\u00a0Understanding English Grammar (Syntax I).<\/strong>\u00a03 hr.; 3 cr. Introduction to the salient characteristics and major patterns of words, phrases, and sentences in English. Taught Fall and Summer.<\/p>\n LCD 150.<\/strong>\u00a0Linguistic Phonetics.<\/strong>\u00a03 hr.; 3 cr. An introduction to phonetic science as used in linguistic theory and research. It covers how speech sounds in the world\u2019s languages are articulated and transcribed with the International Phonetic Alphabet as well as the use of acoustic analysis software to reveal acoustic properties of consonants, vowels, and prosody. Taught Fall.<\/p>\n LCD 151. Methods in Computational Linguistics I.<\/strong>\u00a03 hr.; 3 cr. An introductory course for students in the Computational Linguistics (CL) sequence or those students who wish to learn about Python and its application to Natural Language Processing (NLP). The course will introduce the students to basic Linux commands and programming concepts in Python, focusing on techniques required to work with natural language data. The students will learn about standard corpora used in NLP and CL. The students will write simple Python programs for processing and analyzing language corpora and will perform various linguistic and statistical analysis of the natural language data. The course will also introduce data structures and algorithms that are used to describe and analyze language data. Taught Fall.<\/p>\n LCD 205.<\/strong>\u00a0Sociolinguistics.\u00a0<\/strong>3 hr.; 3 cr. Prereq.: LCD 101 or 102 or 104 or 105\u00a0 or Anth 108. Introduction to the study of the relationship between language and society. Socio-cultural factors which influence language form, use, and history. Taught Spring and Summer.<\/p>\n LCD 220. Advanced English Syntax (Syntax II).<\/strong>\u00a03 hr.; 3 cr. Prereq.: LCD 101 or 102 and 120 or Anth 108. Developing a theoretical framework for the analysis of simple and complex sentences in English. Taught Spring.<\/p>\n LCD 250. Phonology.\u00a0<\/strong>3 hr; 3 cr.\u00a0<\/strong>Prereq.:\u00a0 LCD 101 or 102 or Anth 108 and LCD 150. This course examines the major sound patterns of human language, as gleaned from a wide variety of languages.\u00a0 It teaches in a step-by-step fashion the techniques of phonological analysis and the fundamental theories that underpin it. Students will learn how to analyze phonological data, how to think critically about data, how to formulate rules and hypotheses, and how to test them.<\/p>\n LCD 251. Methods in Computational Linguistics II.\u00a0<\/strong>3 hr; 3 cr.\u00a0<\/strong>Prereq.:\u00a0 LCD 151. The second course in the sequence for Computational Linguistics (CL) students and Computer Science students who wish to take courses in Natural Language Processing (NLP) and Machine Learning. The course will include more advanced topics on data structures and algorithms that are used in NLP, basic algorithms for NLP tasks, including supervised machine learning for part-of-speech tagging, parsing, classification. The students will write simple programs using existing tools and corpora, and will learn how to evaluate the performance using standard NLP evaluation methods. Taught Spring.<\/p>\n LCD 281. Analyzing Language in Action. <\/strong>3 hr; 3 cr. \u00a0This course introduces the theories, approaches, and methods of discourse analysis, the study of human discourse or socially situated language use. We examine how people express themselves, do things, become who they are, and make things happen using language, and how discourse(s) on social beliefs and behaviors influence (and are used in) the construction and production of meaning.<\/p>\n LCD 306. Semantics and Pragmatics.<\/strong>\u00a03 hr.; 3 cr. Prereq. or coreq.: LCD 220. A survey of properties of meaning in language (semantics) and communication strategies people use when they talk to each other (pragmatics). There is a substantial writing commitment in this course.<\/p>\n CSCI 313. Data Structures.<\/strong>\u00a03 hr.; 3 cr. Prereq. or coreq.: CSCI 211, CSCI 212, CSCI 220. Fundamentals of data structures and their implementations: stacks, queues, trees (binary and AVL), heaps, graphs, hash tables. Searching and sorting algorithms. Runtime analysis. Examples of problem-solving using greedy-algorithm, divide-and-conquer, and backtracking.<\/p>\n CSCI 325. Machine Learning.<\/strong>\u00a03 hr.; 3 cr. Prereq. or coreq.: CSCI 313. Foundations of machine learning, linear regression, logistic regression (classification), nonlinear transformation, regularization, validation, random forest, support-vector machine, neural network, deep learning, Unsupervised learning (K-Means), dimensionality reduction (PCA), machine learning pipeline. Programming projects. Taught Fall, Spring.<\/p>\n CSCI 357. Corpus Analysis<\/strong>. 3 hr.; 3 cr. Prereq.: LCD 151, LCD 251. Introduction to tools and methods used in Computational Linguistics and Natural Language Processing (NLP). The students will learn about basic NLP tasks (part-of-speech tagging, tokenization, parsing, entity recognition, sentiment analysis) and how to perform these tasks and train machine learning models, using off-the-shelf software (NLTK, sckit-learn, spaCy, pytorch, etc.).<\/p>\n LCD 360. Issues in Linguistic Research<\/strong>. 3 hr.; 3 cr. Prereq.: LCD 101 or Anth 108. This course focuses on contemporary issues in any of the major branches of linguistics. May be repeated for credit when topics vary sufficiently. There is a substantial writing commitment in this course. Taught Infrequently.<\/p>\n CSCI 363. Artificial Intelligence.<\/strong>\u00a03 hr.; 3 cr. Prereq.: CSCI 313. Principles of artificial intelligence (AI). Topics include search and optimization; probabilistic reasoning; decision processes; reinforcement learning; machine learning; and ethics of AI. Programming projects..<\/p>\n CSCI 366. Natural Language Processing<\/strong>. 3 hr.; 3 cr. Prereq.: LCD 151, LCD 251, CSCI 357. Language models, part-of-speech tagging, syntactic parsing, machine translation, supervised learning, na\u00efve bayes, logistic regression, word embeddings, neural networks. Programming projects.<\/p>\n [\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”Contact Us” admin_label=”Useful Links Heading” _builder_version=”4.27.4″ _module_preset=”default” title_font=”–et_global_body_font||||||||” custom_margin=”-15px||12px||false|false” custom_padding=”14px|||12px|false|false” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.7″ _module_preset=”default” text_font_size=”16px” custom_padding=”|||16px|false|false” global_colors_info=”{}”]<\/p>\n Program Directors: Bill Haddican<\/a><\/strong> and Alla Rozovskaya<\/a><\/strong><\/p>\n To declare the major, please contact\u00a0the program advisor, <\/span>Bill Haddican.<\/a>\u00a0To book appointments, please use the Navigate app<\/a>.<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Bachelor of Science in Computational LinguisticsThis program is run jointly between the Department of Computer Science and the Department of Linguistics, TESOL and Communication Science and Disorders. 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