Language and Computation advanced course:
An introduction to minimalist grammars.

• Gregory Kobele ()
• Jens Michaelis ()

Course material: main.pdf gaertner_michaelis-mtsat10-esslli07.pdf Kobele06-2.pdf michaelis-lacl01-esslli-09.pdf minimalism-lacl98-esslli09.pdf

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Research in the tradition of Chomsky's minimalist program is often inaccessible to non-minimalists, partly because of the highly intuitive level at which much of the work in this tradition is conducted. This course will show how major components of recent Chomskian syntax can be expressed in formal grammars inspired by Stabler's "minimalist grammar" (MG). Many MG variants have been rigorously related to MC-TAGs and other well-understood formalisms, so that now a wide range of Chomskian proposals can be understood and assessed by formally minded linguists from every linguistic tradition. Considering especially recent (empirically consequential) proposals about locality, copying operations, adjunction, and interfaces (phonetic, morphological, semantic), this formal treatment sometimes reveals surprising aspects of those proposals---in particular when being depicted against the "classical" background of parsing complexity and generative capacity---that have been obscured in the informal literature.

Language and Computation advanced course:
Computational Psycholinguistics.

• Roger Levy ()

Over the last two decades, computational linguistics has been revolutionized by increases in computing power, large linguistic datasets, and a paradigm shift toward the view that language processing by computers is best approached through the tools of statistical inference. During roughly the same time frame, there have been similar theoretical developments in cognitive psychology towards a view of major aspects of human cognition as instances of rational statistical inference, exemplified by work such as Anderson (1990) and Tenenbaum & Griffiths (2001). Developments in these two fields have set the stage for renewed interest in computational approaches to human language processing. Correspondingly, this course covers some of the most exciting developments in computational psycholinguistics over the past decade. The course focuses on probabilistic knowledge and memory in language processing, covering models, algorithms, and key empirical results in the literature.

Language and Computation advanced course:
Psycho-computational issues in Morphology Learning and Processing.

• Vito Pirrelli ()

By providing a comprehensive overview of current machine-learning, psycholinguistic and theoretical linguistic literature on the topic, the course is intended to answer the following questions. How are words singled out of their embedding input stream? How are they processed and eventually understood in working memory? Are morphologically complex words stored in long-term memory as a whole or are they rather composed "on-line" in working memory from sub-lexical constituents? Do formal regularity and morpho-semantic transparency play any role in this? Does word-level knowledge require parallel development of form and meaning representations, or do the latter develop independently at a different pace to interact only at later stages? To what extent does past knowledge affect on-line word processing? What principles govern this knowledge? Are they morphology-specific or are they rather based on brain memory structures generically devoted to the ordered activation of items in time? Do they capture local, syntagmatic relations among sub-lexical co-occurring constituents, or also enforce more global paradigmatic constraints over classes of such constituents in complementary distribution?

Language and Computation advanced course:
Linguistic Information Visualization.

• Gerald Penn ()
• Sheelagh Carpendale ()

Course material: carpendale_penn.pdf

Much of what computational linguists fall back upon to improve natural language processing and model language "understanding" is structure that has, at best, only an indirect attestation in observable data. The sheer complexity of these structures, and the observable patterns on which they are based, however, usually limits their accessibility, often even to the researchers creating or studying them. Traditional statistical graphs and custom-designed data illustrations fill the pages of CL papers, providing insight into linguistic and algorithmic structures, but visual 'externalizations' such as these are almost exclusively used in CL for presentation and explanation.

Visualizations can also be used as an aid in the process of research itself. There are special statistical methods, falling under the rubric of "exploratory data analysis", and visualization techniques just for this purpose, in fact, but these are not widely used or even known in CL. These novel data visualization techniques offer the potential for creating new methods that reveal structure and detail in data. Visualization can provide new methods for interacting with large corpora, complex linguistic structures, and can lead to a better understanding of the states of stochastic processes.

Instructed by a team of computational linguists and information visualization researchers, this tutorial will bridge computational linguistic and information visualization expertise, providing attendees with a basis from which they can begin to leverage information visualization in their own research. It will equip participants with: - An understanding of the importance and applicability of information visualization techniques to computational linguistics research; - Knowledge of the basic principles of information visualization theory; - The ability to identify appropriate visualization software and techniques that are available for immediate use and for prototyping; - A working knowledge of research to date in the area of linguistic visualization.

This tutorial will be an extended version of the 3-hour tutorial offered at ACL-2008, which had 25 attendees. The instructors have previously taught portions of the content in advanced undergraduate and graduate courses as well. Students are expected to have a solid background in computational linguisics. No experience with visualization is required.

TUTORIAL OUTLINE

Day 1: Introduction; Information Visualization Theory (representational theory, cognitive psychology, preattentive processing, interaction & animation, assessing and validating visualizations)

Days 2 and 4: Review of Linguistic Visualizations (document content visualizations, text collection analysis, literary analysis, streaming data visualization, convergence of linguistic data and social network analysis, corpora exploration, visualization uncertainty in statistical NLP output, linguistic analysis, visualization of speech data)

Day 3: Tools for Visualization (software solutions: Excel, Tableau, Spotfire, programming toolkits: prefuse, processing, flare, InfoVis Toolkit, online tools: ManyEyes, Swivel, collaborative visualization tools in development)

Day 5: Case Study: Visualization for Statistical MT; Open Research Problems (CL problems that could benefit from visualization, Visualization of language areas that need CL expertise); Closing

Language and Computation advanced course:
Distributional Semantic Models - Theory and Empirical Results.

• Stefan Evert ()
• Alessandro Lenci ()

External page

Distributional semantic models (DSMs) are based on the assumption that the meaning of a word can (at least to a certain extent) be inferred from its usage, i.e. its distribution in text. Therefore, these models dynamically build semantic representations "in the form of multi-dimensional vector spaces" through a statistical analysis of the contexts in which words occur.

With their distributed vector-space representations, DSMs challenge traditional symbolic accounts of conceptual and semantic structures. However, their true ability to address key issues of lexical meaning is still poorly understood, and will have to be carefully evaluated in linguistic and cognitive research.

This course aims to equip participants with the necessary background knowledge for carrying out such research. In addition to the mathematical foundations of DSMs and their application to semantic analysis, we will put particular emphasis on relating the computational models to fundamental issues of semantic theory.