GUCL: Computational Linguistics @ Georgetown
We are a group of Georgetown University faculty, student, and staff researchers at the intersection of language and computation. Our areas of expertise include natural language processing, corpus linguistics, information retrieval, text mining, and more. Members belong to the Linguistics and/or Computer Science departments.
- 9/8/21: Congratulations to the Corpling lab on winning the DISRPT 2021 shared task on discourse processing!
- 8/27/20: First-Year Student Presented Paper at Prestigious Computational Linguistics Conference (Aryaman Arora)
- 9/10/18: #MeToo Movement on Twitter (Lisa Singh)
- 8/29/18: Cliches in baseball (Nathan Schneider)
- 1/20/18: The Coptic Scriptorium project (Amir Zeldes)
- Congratulations to Arman Cohan, Nazli Goharian, and Georgetown alum Andrew Yates for winning a Best Long Paper award at EMNLP 2017! The paper is entitled "Depression and Self-Harm Risk Assessment in Online Forums."
- Congratulations to Ophir Frieder, who has been named to the European Academy of Sciences and Arts (EASA)!
- 9/19/16: "Email" Dominates What Americans Have Heard About Clinton (Lisa Singh)
- 7/12/16: Searching Harsh Environments (Ophir Frieder)
Mailing list: Contact Nathan Schneider to subscribe!
- Nianwen Xue (Brandeis): Linguistics, Thurs. 12/1/22, 3:30 in Poulton 230
- James Mayfield (JHU): CS, 2/10/23, 11:15 in STM 326/hybrid
- GURT 2023: Computational and Corpus Linguistics (conference on campus), 3/9-12/23
- Gabriella Pasi (University of Milano-Bicocca): CS, 3/24/23, 11:00 via Zoom
- Luca Soldaini (AI2): CS, 4/14/23, 11:00 in room TBA
- Carolyn Rosé (CMU): Linguistics, 4/14/23, 3:30 in Poulton 230
- Rada Mihalcea (UMich): CS, 5/4/23, 11:00 in room TBA
- Previous talks
Overview of CL course offerings
Document listing courses in CS, Linguistics, and other departments that are most relevant to students interested in computational linguistics. Includes estimates of when each course will be offered.
COSC-483/LING-463 | Dialogue Systems
Matthew Marge Upperclass Undergraduate & Graduate
Nearly all of us interact with dialogue systems -- from calling up banks and hotels, to talking with intelligent assistants like Siri, Alexa, or Cortana, dialogue systems enable people to get tasks done with software agents using language. Since the interaction is bi-directional, we must consider the fundamentals of how people engage in conversation so as to manage users’ expectations and track how information is exchanged in dialogue. Dialogue systems require an array of technologies to come together for them to work well, including speech recognition, natural language understanding, dialogue management, natural language generation, and speech synthesis. This course will explore what makes dialogue systems effective in commercial and research applications (ranging from personal assistants and chatbots to embodied conversational agents and language-directed robots) and how this contrasts with everyday human-human dialogue.
This course will introduce students to the fundamentals of dialogue systems, expanding on technologies and algorithms that are used in today’s dialogue systems and chatbots. There will also be emphasis on the psycholinguistic properties of human conversation (turn-taking, grounding) so as to prepare students for designing effective, user-friendly dialogue systems. The course will also include examining datasets and dialogue annotations used to train dialogue systems with machine learning algorithms. Coursework will consist of lectures, writing and programming assignments, and student-led presentations on special topics in dialogue. A final project will give students a chance to build their own dialogue system using open source and freely available software. This course is intended for students that are already comfortable with limited amounts of programming (in Python).
COSC-488 | Information Retrieval
Nazli Goharian Upperclass Undergraduate & Graduate
Information retrieval is the identification of textual components, be them web pages, blogs, microblogs, documents, medical transcriptions, mobile data, or other big data elements, relevant to the needs of the user. Relevancy is determined either as a global absolute or within a given context or view point. Practical, but yet theoretically grounded, foundational and advanced algorithms needed to identify such relevant components are taught.
The Information-retrieval techniques and theory, covering both effectiveness and run-time performance of information-retrieval systems are covered. The focus is on algorithms and heuristics used to find textual components relevant to the user request and to find them fast. The course covers the architecture and components of the search engines such as parser, index builder, and query processor. In doing this, various retrieval models, relevance ranking, evaluation methodologies, and efficiency considerations will be covered. The students learn the material by building a prototype of such a search engine. These approaches are in daily use by all search and social media companies.
COSC-586 | Text Mining & Analysis
Nazli Goharian Graduate
This course covers various aspects and research areas in text mining and analysis. Text may be a document, query, blog, tag description, etc. The structure of the course is a combination of lectures & students' presentations. The lectures will cover Text/Web/query classification, information extraction, word sense disambiguation, opinion mining & sentiment analysis, query log analysis, ontology extraction and integration, and more. The students are assigned a related topic in the field for further study and presentation in the class.
COSC/LING-672 | Advanced Semantic Representation
Nathan Schneider Graduate
Natural language is an imperfect vehicle for meaning. On the one hand, some expressions can be interpreted in multiple ways; on the other hand, there are often many superficially divergent ways to express very similar meanings. Semantic representations attempt to disentangle these two effects by exposing similarities and differences in how a word or sentence is interpreted. Such representations, and algorithms for working with them, constitute a major research area in natural language processing.
This course will examine semantic representations for natural language from a computational/NLP perspective. Through readings, presentations, discussions, and hands-on exercises, we will put a semantic representation under the microscope to assess its strengths and weaknesses. For each representation we will confront questions such as: What aspects of meaning are and are not captured? How well does the representation scale to the large vocabulary of a language? What assumptions does it make about grammar? How language-specific is it? In what ways does it facilitate manual annotation and automatic analysis? What datasets and algorithms have been developed for the representation? What has it been used for? Representations covered in depth will include FrameNet (http://framenet.icsi.berkeley.edu), Universal Cognitive Conceptual Annotation (http://www.cs.huji.ac.il/~oabend/ucca.html), and Abstract Meaning Representation (http://amr.isi.edu/). Term projects will consist of (i) innovating on a representation's design, datasets, or analysis algorithms, or (ii) applying it to questions in linguistics or downstream NLP tasks.
LING-362 | Introduction to Natural Language Processing
Amir Zeldes Upperclass Undergraduate & Graduate
This course will introduce students to the basics of Natural Language Processing (NLP), a field which combines insights from linguistics and computer science to produce applications such as machine translation, information retrieval, and spell checking. We will cover a range of topics that will help students understand how current NLP technology works and will provide students with a platform for future study and research. We will learn to implement simple representations such as finite-state techniques, n-gram models and basic parsing in the Python programming language. Previous knowledge of Python is not required, but students should be prepared to invest the necessary time and effort to become proficient over the course of the semester. Students who take this course will gain a thorough understanding of the fundamental methods used in natural language understanding, along with an ability to assess the strengths and weaknesses of natural language technologies based on these methods.
LING-367 | Computational Corpus Linguistics
Amir Zeldes Upperclass Undergraduate & Graduate
Digital linguistic corpora, i.e. electronic collections of written, spoken or multimodal language data, have become an increasingly important source of empirical information for theoretical and applied linguistics in recent years. This course is meant as a theoretically founded, practical introduction to corpus work with a broad selection of data, including non-standardized varieties such as language on the Internet, learner corpora and historical corpora. We will discuss issues of corpus design, annotation and evaluation using quantitative methods and both manual and automatic annotation tools for different levels of linguistic analysis, from parts-of-speech, through syntax to discourse annotation. Students in this course participate in building the corpus described here: https://corpling.uis.georgetown.edu/gum/
COSC-288 | Introduction to Machine Learning
Mark Maloof Undergraduate
This undergraduate course surveys the major research areas of machine learning focusing on classification. Through traditional lectures and programming projects, students learn (1) to understand the foundations of machine learning, (2) to design and implement methods of machine learning, (3) to evaluate methods of machine learning, and (4) to conduct empirical evaluations of multiple methods of machine learning. The course compares and contrasts machine learning with related endeavors, such as statistical learning, pattern classification, data mining, and information retrieval. Topics include instance-based approaches, naive Bayes, decision trees, rule induction, linear classifiers, support vector machines, neural networks, ensemble methods, evaluation, and applications. Students complete five programming projects using Java. There are midterm and final exams.
COSC/LING-572 | Empirical Methods in Natural Language Processing
Nathan Schneider Graduate
Systems of communication that come naturally to humans are thoroughly unnatural for computers. For truly robust information technologies, we need to teach computers to unpack our language. Natural language processing (NLP) technologies facilitate semi-intelligent artificial processing of human language text. In particular, techniques for analyzing the grammar and meaning of words and sentences can be used as components within applications such as web search, question answering, and machine translation.
This course introduces fundamental NLP concepts and algorithms, emphasizing the marriage of linguistic corpus resources with statistical and machine learning methods. As such, the course combines elements of linguistics, computer science, and data science. Coursework will consist of lectures, programming assignments (in Python), and a final team project. The course is intended for students who are already comfortable with programming and have some familiarity with probability theory.
COSC-578 | Statistical Machine Learning
Grace Hui Yang Graduate
Statistical machine learning brings together statistics and computational sciences such as computer science, system science, and optimization. The recent developments in bioinformatics, signal processing, information management, finance, and artificial intelligence have been largely influenced by statistical machine learning. With a focus on mathematical and algorithmic theories, this class offers basics in statistical methodology in dealing with applied problems in science and technology. Topics covered in the class include probability, mathematical statistics, inference, sampling, optimization, and their applications in machine learning. The class will have lectures, mathematical homework, exams, and a programming-based project.
COSC-688 | Experimental Artificial Intelligence (AI)
Grace Hui Yang Graduate
This course offers opportunities for students to have an in-depth understanding and hands-on experience with practical AI systems for state-of-the-art evaluation campaigns. It includes seminar-style classroom presentations and a significant project component. Students will be guided to go through the design and implementation of AI systems in different domains. The course will review recent AI and Machine Learning publications and lead students to work in small groups to build systems. Students are expected to have strong programming skills and previous experience in machine learning, deep learning, and/or AI.
LING-464 | Social Factors in Computational Linguistics and AI
Shabnam Tafreshi Upperclass Undergraduate & Graduate
Advances in technologies for processing human languages have increasingly brought computational linguistics into contact with people. As such, what language reveals about people—and how AI algorithms make decisions affecting people based on their language—is of paramount concern. At the same time, contemporary algorithms for processing language offer powerful new tools for studying people and society on a large scale. Designed for students with grounding in computational linguistics, this course will examine the intersection of people, language, and algorithms with technical precision as well as an appreciation for human context. Topics will include: computational models of conversational interaction and power dynamics; emotions, sentiment, subjectivity, and politeness; toxic language; sociolinguistic variation; detection of attributes such as race and gender; issues of privacy, ethics, bias, and fairness, with special attention to minoritized speakers, languages, and dialects; and the use of large-scale language data for studying political framing and social movements like #MeToo and Black Lives Matter.
LING-472/ANLY-521 | Computational Linguistics with Advanced Python
Elizabeth Merkhofer Upperclass Undergraduate & Graduate
This course teaches advanced topics in programming for linguistic data analysis and processing using the Python language. A series of assignments will give students hands-on practice implementing core algorithms for linguistic tasks. By the end of the course, students will be able to transform pseudocode into well-written code for algorithms that make sense of textual data, and to evaluate the algorithms quantitatively and qualitatively. Linguistic tasks will include edit distance, semantic similarity, authorship detection, and named entity recognition. Python topics will include the appropriate use of data structures; mathematical objects in numpy; exception handling; object-oriented programming; and software development practices such as code documentation and version control.
Requirements: Basic Python programming skills are required (for example satisfied by LING-362, Intro to NLP)