Nathan Schneider

• Currently: Ph.D. student at Carnegie Mellon University
  • Department: Language Technologies Institute in the School of Computer Science
  • Advisor: Noah Smith (Lab: Noah’s ARK)
• Previously: Undergraduate at UC Berkeley, majoring in Computer Science and Linguistics
• Somewhat outdated CV (PDF)

I study the semantics of natural language text. My research aims to address the questions: What is the nature of linguistic knowledge that participates in the communication of meaning? How can computers represent and recover aspects of this knowledge for the benefit of natural language processing applications?

My work draws techniques and insights from statistical machine learning, descriptive linguistics, construction grammar, and cognitive linguistics. Here are some topics, with superscripts denoting languages other than English:

This homepage is brought to you by the letter θ, a friend to linguists and statisticians alike.

• Dipanjan Das, Desai Chen, André F. T. Martins, Nathan Schneider, and Noah A. Smith (2013). Frame-semantic parsing. Computational Linguistics. Frame semantics (Fillmore 1982) is a linguistic theory that has been instantiated for English in the FrameNet lexicon (Fillmore, Johnson, and Petruck 2003). We solve the problem of frame- semantic parsing using a two-stage statistical model that takes lexical targets (i.e., content words and phrases) in their sentential context and predicts frame-semantic structures. Given a target in context, the first stage disambiguates it to a semantic frame. This model employs latent variables and semi-supervised learning to improve frame disambiguation for targets unseen at training time. The second stage finds the target’s locally expressed semantic arguments. A fast exact dual decomposition algorithm collectively predicts all the arguments of a frame at once in order to respect declaratively stated linguistic constraints at inference time, resulting in qualitatively better structures than naïve local predictors. Both components are feature-based and discriminatively trained on a small set of annotated frame-semantic parses. On a benchmark dataset, the approach, along with a heuristic identifier of frame-evoking targets, outperforms prior state of the art by significant margins. Additionally, we present experiments on a much larger recent dataset and have released our accurate frame-semantic parser as open-source software. To appear. [early manuscript]
• Nathan Schneider, Behrang Mohit, Chris Dyer, Kemal Oflazer, and Noah A. Smith (2013). Supersense tagging for Arabic: the MT-in-the-middle attack. NAACL-HLT. [paper] We consider the task of tagging Arabic nouns with WordNet supersenses. Three approaches are evaluated. The first uses an expert-crafted but limited-coverage lexicon, Arabic WordNet, and heuristics. The second uses unsupervised sequence modeling. The third and most successful approach uses machine translation to translate the Arabic into English, which is automatically tagged with English supersenses, the results of which are then projected back into Arabic. Analysis shows gains and remaining obstacles in four Wikipedia topical domains. @InProceedings{arabic-sst-annotation, author = {Schneider, Nathan and Mohit, Behrang and Dyer, Chris and Oflazer, Kemal and Smith, Noah A.}, title = {Supersense Tagging for {A}rabic: the {MT}-in-the-Middle Attack}, booktitle = {Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, month = jun, year = {2013}, address = {Atlanta, Georgia, USA}, publisher = {Association for Computational Linguistics} }
• Olutobi Owoputi, Brendan O’Connor, Chris Dyer, Kevin Gimpel, Nathan Schneider, and Noah A. Smith (2013). Improved part-of-speech tagging for online conversational text with word clusters. NAACL-HLT. [paper] We consider the problem of part-of-speech tagging for informal, online conversational text. We systematically evaluate the use of large-scale unsupervised word clustering and new lexical features to improve tagging accuracy. With these features, our system achieves state-of-the-art tagging results on both Twitter and IRC POS tagging tasks; Twitter tagging is improved from 90% to 93% accuracy (more than 3% absolute). Qualitative analysis of these word clusters yields insights about NLP and linguistic phenomena in this genre. Additionally, we contribute the first POS annotation guidelines for such text and release a new dataset of English language tweets annotated using these guidelines. Tagging software, annotation guidelines, and large-scale word clusters are available at: http://www.ark.cs.cmu.edu/TweetNLP
  This paper describes release 0.3 of the “CMU Twitter Part-of-Speech Tagger” and annotated data. @InProceedings{arabic-sst-annotation, author = {Owoputi, Olutobi and O'Connor, Brendan and Dyer, Chris and Gimpel, Kevin and Schneider, Nathan and Smith, Noah A.}, title = {Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters}, booktitle = {Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, month = jun, year = {2013}, address = {Atlanta, Georgia, USA}, publisher = {Association for Computational Linguistics} } [data and software]
• Yulia Tsvetkov, Naama Twitto, Nathan Schneider, Noam Ordan, Manaal Faruqui, Victor Chahuneau, Shuly Wintner, and Chris Dyer (2013). Identifying the L1 of non-native writers: the CMU-Haifa system. NLI Shared Task @BEA. [paper] We show that it is possible to learn to identify, with high accuracy, the native language of English test takers from the content of the essays they write. Our method uses standard text classification techniques based on multiclass logistic regression, combining individually weak indicators to predict the most probable native language from a set of 11 possibilities. We describe the various features used for classification, as well as the settings of the classifier that yielded the highest accuracy. @InProceedings{arabic-sst-annotation, author = {Yulia Tsvetkov and Naama Twitto and Nathan Schneider and Noam Ordan and Manaal Faruqui and Victor Chahuneau and Shuly Wintner and Chris Dyer}, title = {Identifying the {L1} of non-native writers: the {CMU}-{H}aifa system }, booktitle = {Proceedings of the 8th Workshop on Innovative Use of {NLP} for Building Educational Applications}, month = jun, year = {2013}, address = {Atlanta, Georgia, USA}, publisher = {Association for Computational Linguistics} }
• Nathan Schneider, Chris Dyer, and Noah A. Smith (2013). Exploiting and expanding corpus resources for frame-semantic parsing. IFNW. [slides]
• Nathan Schneider, Behrang Mohit, Kemal Oflazer, and Noah A. Smith (2012). Coarse lexical semantic annotation with supersenses: an Arabic case study. ACL. [paper] “Lightweight” semantic annotation of text calls for a simple representation, ideally without requiring a semantic lexicon to achieve good coverage in the language and domain. In this paper, we repurpose WordNet’s supersense tags for annotation, developing specific guidelines for nominal expressions and applying them to Arabic Wikipedia articles in four topical domains. The resulting corpus has high coverage and was completed quickly with reasonable inter-annotator agreement. @InProceedings{arabic-sst-annotation, author = {Schneider, Nathan and Mohit, Behrang and Oflazer, Kemal and Smith, Noah A.}, title = {Coarse Lexical Semantic Annotation with Supersenses: An {A}rabic Case Study}, booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics}, month = jul, year = {2012}, address = {Jeju Island, Korea}, publisher = {Association for Computational Linguistics}, pages = {253--258}, url = {http://www.aclweb.org/anthology/P12-2050} } [data]
• Behrang Mohit, Nathan Schneider, Rishav Bhowmick, Kemal Oflazer, and Noah A. Smith (2012). Recall-oriented learning of named entities in Arabic Wikipedia. EACL. [paper] [supplement] We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adaptation setting for which we have no labeled training data in the target domain. To facilitate evaluation, we obtain annotations for articles in four topical groups, allowing annotators to identify domain-specific entity types in addition to standard categories. Standard supervised learning on newswire text leads to poor target-domain recall. We train a sequence model and show that a simple modification to the online learner—a loss function encouraging it to “arrogantly” favor recall over precision—substantially improves recall and F₁. We then adapt our model with self-training on unlabeled target-domain data; enforcing the same recall-oriented bias in the self-training stage yields marginal gains. @InProceedings{mohit-arabic-ner, author = {Mohit, Behrang and Schneider, Nathan and Bhowmick, Rishav and Oflazer, Kemal and Smith, Noah A.}, title = {Recall-Oriented Learning of Named Entities in {A}rabic {W}ikipedia}, booktitle = {Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics}, month = apr, year = {2012}, address = {Avignon, France}, publisher = {Association for Computational Linguistics}, pages = {162--173}, url = {http://www.aclweb.org/anthology/E12-1017} } [data and software]
• Kevin Gimpel, Nathan Schneider, Brendan O’Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, and Noah A. Smith (2011). Part-of-speech tagging for Twitter: annotation, features, and experiments. ACL-HLT. [paper] [slides] We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets. @inproceedings{gimpel-twitter-pos, author = {Gimpel, Kevin and Schneider, Nathan and O'Connor, Brendan and Das, Dipanjan and Mills, Daniel and Eisenstein, Jacob and Heilman, Michael and Yogatama, Dani and Flanigan, Jeffrey and Smith, Noah A.}, title = {Part-of-Speech Tagging for {T}witter: Annotation, Features, and Experiments}, booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}, month = jun, year = {2011}, address = {Portland, Oregon, USA}, publisher = {Association for Computational Linguistics}, pages = {42--47}, url = {http://www.aclweb.org/anthology/P11-2008} } [data and software] [press]
• Desai Chen, Nathan Schneider, Dipanjan Das, and Noah A. Smith (2010). SEMAFOR: Frame argument resolution with log-linear models. SemEval. [paper] [slides] This paper describes the SEMAFOR system’s performance in the SemEval 2010 task on linking events and their participants in discourse. Our entry is based upon SEMAFOR 1.0 (Das et al., 2010), a frame-semantic probabilistic parser built from log-linear models. The extended system models null instantiations, including non-local argument reference. Performance is evaluated on the task data with and without gold-standard overt arguments. In both settings, it fares the best of the submitted systems with respect to recall and F₁. @inproceedings{chen-schneider-das-smith-10, author = {Desai Chen and Nathan Schneider and Dipanjan Das and Noah A. Smith}, title = {{SEMAFOR}: Frame Argument Resolution with Log-Linear Models}, booktitle = {Proceedings of the Fifth International Workshop on Semantic Evaluation (SemEval-2010)}, month = jul, year = {2010}, address = {Uppsala, Sweden}, publisher = {Association for Computational Linguistics}, pages = {264--267}, url = {http://www.aclweb.org/anthology/S10-1059} }
• Dipanjan Das, Nathan Schneider, Desai Chen, and Noah A. Smith (2010). Probabilistic frame-semantic parsing. NAACL-HLT. [paper] [slides] This paper contributes a formalization of frame-semantic parsing as a structure prediction problem and describes an implemented parser that transforms an English sentence into a frame-semantic representation. It finds words that evoke FrameNet frames, selects frames for them, and locates the arguments for each frame. The system uses two feature-based, discriminative probabilistic (log-linear) models, one with latent variables to permit disambiguation of new predicate words. The parser is demonstrated to significantly outperform previously published results. @inproceedings{das-schneider-chen-smith-10, author = {Dipanjan Das and Nathan Schneider and Desai Chen and Noah A. Smith}, title = {Probabilistic Frame-Semantic Parsing}, booktitle = {Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics}, month = jun, year = {2010}, address = {Los Angeles, California}, publisher = {Association for Computational Linguistics}, pages = {948--956}, url = {http://www.aclweb.org/anthology/N10-1138} } [software]
• Nathan Schneider (2010). English morphology in construction grammar. CSDL. [poster]
• Nathan Schneider (2010). Computational cognitive morphosemantics: modeling morphological compositionality in Hebrew verbs with Embodied Construction Grammar. BLS. [slides] [paper] This paper brings together the theoretical framework of construction grammar and studies of verbs in Modern Hebrew to furnish an analysis integrating the form and meaning components of morphological structure. In doing so, this work employs and extends Embodied Construction Grammar (ECG; Bergen and Chang 2005), a computational formalism developed to study grammar from a cognitive linguistic perspective. In developing a formal analysis of Hebrew verbs, I adapt ECG—until now a lexical/syntactic/semantic formalism—to account for the compositionality of morphological constructions, accommodating idiosyncrasy while encoding generalizations at multiple levels. Similar to syntactic constructions, morpheme constructions are related in an inheritance network, and can be productively composed to form words. With the expanded version of ECG, constructions can readily encode nonconcatenative root-and-pattern morphology and associated (compositional or noncompositional) semantics, cleanly integrated with syntactic constructions. This formal, cognitive study should pave the way for computational models of morphological learning and processing in Hebrew and other languages.

• Nathan Schneider and Reut Tsarfaty (June 2013). Design Patterns in Fluid Construction Grammar, Luc Steels (editor). Computational Linguistics 39(2). [paper] @article{schneider-dpfcg-13, title = {{\em Design {P}atterns in {F}luid {C}onstruction {G}rammar}, {Luc Steels} (editor)}, volume = {39}, number = {2}, journal = {Computational Linguistics}, author = {Schneider, Nathan and Tsarfaty, Reut}, month = jun, year = {2013}, pages = {447--453} } [publisher link]

• Olutobi Owoputi, Brendan O’Connor, Chris Dyer, Kevin Gimpel, and Nathan Schneider (September 2012). Part-of-speech tagging for Twitter: word clusters and other advances. Technical Report CMU-ML-12-107. [paper] We present improvements to a Twitter part-of-speech tagger, making use of several new features and large- scale word clustering. With these changes, the tagging accuracy increased from 89.2% to 92.8% and the tagging speed is 40 times faster. In addition, we expanded our Twitter tokenizer to support a broader range of Unicode characters, emoticons, and URLs. Finally, we annotate and evaluate on a new tweet dataset, DailyTweet547, that is more statistically representative of English-language Twitter as a whole. The new tagger is released as TweetNLP version 0.3, along with the new annotated data and large-scale word clusters at http://www.ark.cs.cmu.edu/TweetNLP. @techreport{owoputi-twitter-pos-12-tr, author = {Olutobi Owoputi and Brendan O'Connor and Chris Dyer and Kevin Gimpel and Nathan Schneider}, institution = {Carnegie Mellon University}, address = {Pittsburgh, Pennsylvania}, type = {Technical Report}, number = {CMU-ML-12-107}, title = {Part-of-Speech Tagging for {T}witter: Word Clusters and Other Advances}, year = {2012}, month = sep, url = {http://www.ark.cs.cmu.edu/TweetNLP/owoputi+etal.tr12.pdf} } [data and software]
• Nathan Schneider (5 October 2011). Casting a wider ’Net: NLP for the Social Web. Invited talk, CMU Qatar Computer Science. [slides] Natural language text dominates the information available on the Web. Yet the language of online expression often differs substantially, in both style and substance, from the language found in more traditional sources such as news. Making natural language processing techniques robust to this sort of variation is thus important for applications to behave intelligently when presented with Web text. This talk presents new research applying two sequence prediction tasks—part-of-speech tagging and named entity detection—to text from online social media platforms (Twitter and Wikipedia). For both tasks, we adapt standard forms of annotation to better suit the linguistic and topical characteristics of the data. We also propose techniques to elicit more accurate statistical taggers, including linguistic features inspired by the domain (for part-of-speech tagging of Twitter messages) as well as modifications to the learning algorithm (for named entity detection in Arabic Wikipedia).
• Nathan Schneider, Rebecca Hwa, Philip Gianfortoni, Dipanjan Das, Michael Heilman, Alan W. Black, Frederick L. Crabbe, and Noah A. Smith (July 2010). Visualizing topical quotations over time to understand news discourse. Technical Report CMU-LTI-10-013. [paper] We present the Pictor browser, a visualization designed to facilitate the analysis of quotations about user-specified topics in large collections of news text. Pictor focuses on quotations because they are a major vehicle of communication in the news genre. It extracts quotes from articles that match a user’s text query, and groups these quotes into “threads” that illustrate the development of subtopics over time. It allows users to rapidly explore the space of relevant quotes by viewing their content and speakers, to examine the contexts in which quotes appear, and to tune how threads are constructed. We offer two case studies demonstrating how Pictor can support a richer understanding of news events. @techreport{das-schneider-chen-smith-10-tr, author = {Nathan Schneider and Rebecca Hwa and Philip Gianfortoni and Dipanjan Das and Michael Heilman and Alan W. Black and Frederick L. Crabbe and Noah A. Smith}, institution = {Carnegie Mellon University}, address = {Pittsburgh, Pennsylvania}, type = {Technical Report}, number = {CMU-LTI-10-013}, title = {Visualizing Topical Quotations Over Time to Understand News Discourse}, year = {2010}, month = jul, url = {http://www.cs.cmu.edu/~nasmith/papers/schneider+etal.tr10.pdf} }
• Dipanjan Das, Nathan Schneider, Desai Chen, and Noah A. Smith (April 2010). SEMAFOR 1.0: A probabilistic frame-semantic parser. Technical Report CMU-LTI-10-001. [paper] An elaboration on (Das et al., 2010), this report formalizes frame-semantic parsing as a structure prediction problem and describes an implemented parser that transforms an English sentence into a frame-semantic representation. SEMAFOR 1.0 finds words that evoke FrameNet frames, selects frames for them, and locates the arguments for each frame. The system uses two feature-based, discriminative probabilistic (log-linear) models, one with latent variables to permit disambiguation of new predicate words. The parser is demonstrated to significantly outperform previously published results and is released for public use. @techreport{das-schneider-chen-smith-10-tr, author = {Dipanjan Das and Nathan Schneider and Desai Chen and Noah A. Smith}, institution = {Carnegie Mellon University}, address = {Pittsburgh, Pennsylvania}, type = {Technical Report}, number = {CMU-LTI-10-001}, title = {{SEMAFOR} 1.0: A Probabilistic Frame-Semantic Parser}, year = {2010}, month = apr, url = {http://www.ark.cs.cmu.edu/SEMAFOR/das+schneider+chen+smith.tr10.pdf} } [software]
• Reza Bosagh Zadeh and Nathan Schneider (December 2008). Unsupervised approaches to sequence tagging, morphology induction, and lexical resource acquisition. LS2 course literature review. [paper] [slides] We consider unsupervised approaches to three types of problems involving the prediction of natural language information at or below the level of words: sequence labeling (including part-of-speech tagging); decomposition (morphological analysis and segmentation); and lexical resource acquisition (building dictionaries to encode linguistic knowledge about words within and across languages). We highlight the strengths and weaknesses of these approaches, including the extent of labeled data/resources assumed as input, the robustness of modeling techniques to linguistic variation, and the semantic richness of the output relative to the input.

My research interests are in the intersection of linguistics, cognitive science, and computer science/artificial intelligence. Fundamentally, I want to be able to describe and simulate human and artificial language learning, understanding, and use.

Reseach goals:

• understanding how languages convey meaning
• using computers to model, analyze, and reason about human language
• designing computer interfaces to exploit aspects of human cognition and artificial intelligence, including language processing

Specific problems of interest include:

• Statistical NLP: morphological, syntactic, and semantic parsing; machine translation; grammar learning; figurative language processing
• Cognitive linguistics: Construction Grammar; frame semantics; metaphor and metonymy; conceptual blending and mental spaces; usage-based theories of language learning
• Technology for linguistics: Use of technology to assist linguistic discovery and language revitalization
• Human-computer interaction: NLP-enabled user interfaces and information visualization

The goal of this project was to build models to predict a sentence's frame-semantic structure. Predicting a frame-semantic parse involves finding and disambiguating frame-evoking expressions and matching roles of the evoked frames to arguments in the sentence. We have implemented a probabilistic frame parser for English which outperforms the previous state of the art.

• Probabilistic Frame-Semantic Parsing (Das et al., NAACL 2010)

The AQMAR project (a collaboration with CMU's Qatar campus) aims to advance the state of the art in NLP for Arabic text. We will develop tools for linguistic structure analysis, especially named entity recognition (NER) and semantic tagging, for use in the NLP community, with emphasis on domains other than news (namely, topics found in Arabic Wikipedia).

• Recall-oriented learning of named entities in Arabic Wikipedia (Mohit et al., EACL 2012)
• Recall-oriented learning for named entity recognition in Wikipedia (Mohit et al., TR 2011)

I am currently working on the RAVINE project, an effort combining NLP and information visualization technologies to build an interface facilitating efficient exploration and analysis of content from a large database of news articles. Our system scans articles to extract quotations (and their speakers) for display in an interactive graph. I have been primarily involved in designing the interface and in organizing a user study to evaluate its effectiveness.

Hebrew verbs use a root-and-pattern system, where a three-consonant root is lexicalized in one or more of seven verbal paradigms. Each verb, then, is a pairing of a root, a paradigm, and a meaning. An inflected verb's form is quite predictable, the meaning less so; many verbs have idiosyncratic meanings, but there are some regularities and tendencies which need to be accounted for, e.g. certain frequent alternations between paradigms for a common root. My analysis addresses the following questions:

1. What are the forms and meanings of the morphological components of verbs—roots, paradigms, stems, and inflectional affixes?
2. How do the forms and meanings of these constructions combine to yield actual verbs in sentences?
3. How can these constructions be formalized in a structured representation that can be used for computational analysis?

I argue that construction grammar is an appropriate theoretical framework capable of accounting for the complexities of such a system. In particular, I use the Embodied Construction Grammar formalism to represent the necessary constructions in a manner suitable for automated analysis and simulation. Moreover, I argue that many features of the system are consistent with the notion of language as a best-fit cognitive phenomenon.

As part of an honors thesis under the supervision of Jerry Feldman, I designed a morphological extension to the Embodied Construction Grammar formalism and implemented this extension in the ECG parser.

• Computational Cognitive Morphosemantics: Modeling Morphological Compositionality in Hebrew Verbs with Embodied Construction Grammar (Schneider, BLS 2010)
• SLUgS Symposium abstract and slides

Picurís Tagger

Metonymy Classification

• Pythonathan: miscellaneous Python development tips
• String & Regular Expression Language Reference (Python, Java, Javascript, PHP, Ruby)
• Python 2.7 Format String Reference Guide
• Unix Text Processing Command Reference
• Git Command Overview
• ducttape: A Crash Course (in progress)

Ph.D. student since Fall 2008, Language Technologies Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania. My research is in statistical natural language processing; I am advised by Noah Smith. I have completed the following courses:

• Language & Statistics II (Noah Smith, Fall 2008)
• Grammar Formalisms (Lori Levin, Spring 2009)
• Information Extraction (William Cohen, Fall 2009)
• Reading the Web (Tom Mitchell, Fall 2009)
• Advanced NLP Seminar (Noah Smith, Spring 2009, Spring 2010, Spring 2011)
• NLP Lab (Spring 2009)
• Introduction to Computer Science Education (Leigh Ann Sudol, Jan.–Feb. 2010)
• Alternative Syntactic Theories: Construction Grammar (University of Pittsburgh, Yasuhiro Shirai, Fall 2008)

I have served as the TA for:

• Advanced NLP Seminar (Noah Smith, Spring 2010)
• Natural Language Processing (Noah Smith, Spring 2011)

In 2008 I graduated from the University of California, Berkeley with a double major in Computer Science and Linguistics. Courses include:

Computer Science

• Statistical Natural Language Processing
• Statistical Learning Theory: Graphical Models
• Practical Machine Learning
• Artificial Intelligence
• Algorithms
• Programming Languages and Compilers
• The Neural Basis of Thought and Language

Linguistics

• The Mind and Language
• Advanced Cognitive Linguistics
• Modern Hebrew Linguistics
• Syntax and Semanatics
• Comparative and Historical Linguistics
• Phonology and Morphology
• Phonetics
• The Neural Basis of Thought and Language
• Neural Theory of Language Seminar

Languages

• עיברית מודרנית (Modern Hebrew) – 4 semesters' worth
• français (French) – 1 semester
• العربِيّة (Arabic) – 1 semester

Other

• Linear Algebra and Differential Equations
• Constitutional Law of the United States: Civil Rights and Civil Liberties

I attended Sycamore High School in Cincinnati, Ohio, where I studied computer science for four years, Modern Hebrew for three, and participated in the orchestra (violin), spring musicals (stage crew), academic quiz team, world affairs council, and Scrabble Club.

Academic

• CL+NLP Lunch

Groups

Current:

• All University Orchestra
• J'Burgh

Undergraduate:

• Cal Berkeley Democrats & Smart Ass
• Roosevelt Institution (now Roosevelt Institute)
• Cal Scrabble Club
• SLUgS

Programming

My programming languages of choice are Python and Java; I've also used C, C++, C#, and Scheme. For the web I use JavaScript and PHP.

Hobbies

I play the violin and enjoy table tennis and photography.

My favorite fonts include: Zapf Humanist/Optima, Segoe UI, Perspective Sans, Georgia, and Lucida Bright.

• How to type in Hebrew on Windows_[+PDF]
• IPA keyboard layout for Windows_[+ZIP]

Random Unicode character:

I have listed what to me are indispensible enhancements to the Firefox browsing experience, including:

Adblock Plus

Hide ads on web pages

All-in-One Gestures

Enables simple mouse movements for common browser actions

Firebug

Invaluable tool for web designers; includes a DOM browser, style information, and a JavaScript console

Zotero

This citation manager is priceless (good thing it's free!)—it imports citation information from online catalogs and electronic journals, stores snapshots of web pages, organizes notes and other metadata, and generates bibliographies/exports to BibTeX. I contributed a script which makes papers on the ACL Anthology visible to Zotero.

• NACLO
• LanguageLog
• DailyKos
• NPR Legal Affairs stories
• Slashdot
• Planet Mozilla
• MuggleNet
• Piled Higher and Deeper, xkcd, Dinosaur Comics, Jim Borgman
• Milon Morfix מילון מורפיקס (Hebrew-English/English-Hebrew dictionary)
• My now-defunct blog