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[eu_members at aclweb dot org] NSF Supported Undergraduate Summer Internships in NLP




A Language Engineering Workshop
for Students and Professionals:
Integrating Research and Education

APPLICATION DEADLINE – March 19, 2010
ELECTRONIC SUBMISSION: http://www.clsp.jhu.edu/workshops/ws10/internship.php

The Center for Language and Speech Processing at the Johns Hopkins University is seeking outstanding members of the current junior class to participate in a summer workshop on language engineering from June 7 to July 30, 2010.

No limitation is placed on the undergraduate major. Only relevant skills, employment experience, past academic record and the strength of letters of recommendation will be considered.[1] Students of Biomedical Engineering, Computer Science, Cognitive Science, Electrical Engineering, Linguistics, Mathematics, Physics, Psychology, etc. may apply. Women and minorities are encouraged to apply.[2]

• An opportunity to explore an exciting new area of research.
• A two-week tutorial on speech and language technology.
• Mentoring by an experienced researcher.
• Use of a computer workstation throughout the workshop.
• A $5,000 stipend and $2,520 towards per diem expenses.
• Private furnished accommodation for the duration of the workshop.
• Travel expenses to and from the workshop venue.
• Participation in project planning activities.

The eight-week workshop provides a vigorously stimulating and enriching intellectual environment and we hope it will encourage students to eventually pursue graduate study in the field of human language technologies.

Application forms are available via the Internet and will only be accepted electronically (please go to http://www.clsp.jhu.edu/workshops/ws10/internship.php). Applications must be received at CLSP by Friday, March 19, 2010. For details, contact CLSP, by visiting our website - http://www.clsp.jhu.edu, or calling 410-516-4237.


There are three likely topics for the CLSP Summer Workshops this summer, described below.

Speech Recognition with Segmental Conditional Random Fields

This project will explore an exciting new method for doing speech recognition. Whereas conventional approaches to speech recognition analyze speech in tiny, fixed-length blocks, the proposed Segmental Conditional Random Field (SCRF) approach analyzes it in variable length segments corresponding directly to words. In this approach, we will extract numerous features, each of which measures some aspect of the consistency between the speech segment and the hypothesized word. These features will be combined in a log-linear model, which will allow for the joint training of both acoustic and language modeling features. SCRFs have the potential to make a fundamental impact on the way we do speech recognition, and advances we make in graphical models will have broad relevance to the fields of text and image processing.

Localizing Objects and Actions in Videos with the Help of Accompanying Text

Multimedia content is a growing focus of search and retrieval, personalization, categorization, and information extraction. Video analysis allows us to find both objects and actions in video, but recognition of a large variety of categories is very challenging. Any text accompanying the video, however, can be very good at describing objects and actions at a semantic level, and often outlines the salient information present in the video. Such textual descriptions are often available as closed captions, transcripts or program notes. In this inter-disciplinary project, we will combine natural language processing, computer vision and machine learning to investigate how the semantic information contained in textual sources can be leveraged to improve the detection of objects and complex actions in video. We will parse the text to obtain verb-object dependencies, use lexical knowledge-bases to identify words that describe these objects and actions, use web-wide image databases to get exemplars of the objects and actions, and build models that can detect where in the video the objects and actions are localized.

Synchronous Grammar Induction for Statistical Machine Translation

The last decade of research in Statistical Machine Translation (SMT) has seen rapid progress. The most successful methods have been based on synchronous context free grammars (SCFGs), which encode translational equivalences and license reordering between tokens in the source and target languages. Yet, while closely related language pairs can be translated with a high degree of precision now, the result for distant pairs is far from acceptable. In theory, however, the "right" SCFG is capable of handling most, if not all, structurally divergent language pairs. So we propose to focus on the crucial practical aspects of acquiring such SCFGs from bilingual text. We will take the pragmatic approach of starting with existing algorithms for inducing unlabeled SCFGs (e.g. the popular Hiero model), and then using state-of-the-art hierarchical non-parametric Bayesian methods to iteratively refine the syntactic constituents used in the translation rules of the grammar, hoping to approach, in an unsupervised manner, SCFGs learned from massive quantities of manually "tree-banked" parallel text.



Footnotes:
[1] Four to eight undergraduate students will be selected for next summer’s workshop. It is expected that they will be members of the current junior class. Applicants must be proficient in computer usage, including either C, C++, Perl or Python programming and have exposure to basic probability or statistics. Knowledge of the following will be considered, but is not a prerequisite: Linguistics, Speech Communication, Natural Language Processing, Cognitive Science, Machine Learning, Digital Signal Processing, Signals and Systems, Linear Algebra, Data Structures, Foreign Languages, the Study of Music and experience in reading musical scores or using MatLab and other similar software.

[2] The Johns Hopkins University does not discriminate on the basis of gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, or veteran status in any student program or activity administered by the university or with regard to admission or employment. Questions regarding Title VI, Title IX and Section 504 should be referred to the office of Equal Opportunity and Affirmative Action Programs, Garland Hall, Suite 130, Homewood Campus, 410-516-8075; TTY 410-516-6225.