Historically, unsupervised learning techniques have lacked a principled technique for selecting the number of unseen components. Research into non-parametric priors, such as the Dirichlet process, has enabled instead the use of infinite models, in which the number of hidden categories is not fixed, but can grow with the amount of training data. Here we develop the infinite tree, a new infinite model capable of representing recursive branching structure over an arbitrarily large set of hidden categories. Specifically, we develop three infinite tree models, each of which enforces different independence assumptions, and for each model we define a simple direct assignment sampling inference procedure. We demonstrate the utility of our models by doing unsupervised learning of part-of-speech tags from treebank dependency skeleton structure, achieving an accuracy of 75.34%, and by doing unsupervised splitting of part-of-speech tags, which increases the accuracy of a generative dependency parser from 85.11 % to 87.35%. 1

In adding syntax to statistical MT, there is a tradeoff between taking advantage of linguistic analysis, versus allowing the model to exploit linguistically unmotivated mappings learned from parallel training data. A number of previous efforts have tackled this tradeoff by starting with a commitment to linguistically motivated analyses and then finding appropriate ways to soften that commitment. We present an approach that explores the tradeoff from the other direction, starting with a context-free translation model learned directly from aligned parallel text, and then adding soft constituent-level constraints based on parses of the source language. We obtain substantial improvements in performance for translation from Chinese and Arabic to English. 1

This paper presents a comparative study of probabilistic treebank parsing of German, using the Negra and TüBa-D/Z treebanks. Experiments with the Stanford parser, which uses a factored PCFG and dependency model, show that, contrary to previous claims for other parsers, lexicalization of PCFG models boosts parsing performance for both treebanks. The experiments also show that there is a big difference in parsing performance, when trained on the Negra and on the TüBa-D/Z treebanks. Parser performance for the models trained on TüBa-D/Z are comparable to parsing results for English with the Stanford parser, when trained on the Penn treebank. This comparison at least suggests that German is not harder to parse than its West-Germanic neighbor language English. 1

In this paper, we present a novel approach for authorship attribution, the task of identifying the author of a document, using probabilistic context-free grammars. Our approach involves building a probabilistic context-free grammar for each author and using this grammar as a language model for classification. We evaluate the performance of our method on a wide range of datasets to demonstrate its efficacy. 1

A ∗ parsing makes 1-best search efficient by suppressing unlikely 1-best items. Existing k-best extraction methods can efficiently search for top derivations, but only after an exhaustive 1-best pass. We present a unified algorithm for k-best A ∗ parsing which preserves the efficiency of k-best extraction while giving the speed-ups of A ∗ methods. Our algorithm produces optimal k-best parses under the same conditions required for optimality in a 1-best A ∗ parser. Empirically, optimal k-best lists can be extracted significantly faster than with other approaches, over a range of grammar types. 1

Previous work on German parsing has provided confusing and conflicting results concerning the difficulty of the task and whether techniques that are useful for English, such as lexicalization, are effective for German. This paper aims to provide some understanding and solid baseline numbers for the task. We examine the performance of three techniques on three treebanks (Negra, Tiger, and TüBa-D/Z): (i) Markovization, (ii) lexicalization, and (iii) state splitting. We additionally explore parsing with the inclusion of grammatical function information. Explicit grammatical functions are important to German language understanding, but they are numerous, and naïvely incorporating them into a parser which assumes a small phrasal category inventory causes large performance reductions due to increasing sparsity. 1

martin.chodorow

We present a novel approach to grammatical error correction based on Alternating Structure Optimization. As part of our work, we introduce the NUS Corpus of Learner English (NUCLE), a fully annotated one million words corpus of learner English available for research purposes. We conduct an extensive evaluation for article and preposition errors using various feature sets. Our experiments show that our approach outperforms two baselines trained on non-learner text and learner text, respectively. Our approach also outperforms two commercial grammar checking software packages. 1

This paper reports results on grammatical induction for French. We investigate how to best train a parser on the French Treebank (Abeillé et al., 2003), viewing the task as a trade-off between generalizability and interpretability. We compare, for French, a supervised lexicalized parsing algorithm with a semi-supervised unlexicalized algorithm (Petrov et al., 2006) along the lines of (Crabbé and Candito, 2008). We report the best results known to us on French statistical parsing, that we obtained with the semi-supervised learning algorithm. The reported experiments

Recent advances in parsing technology have made treebank parsing with discontinuous constituents possible, with parser output of competitive quality (Kallmeyer and Maier, 2010). We apply Data-Oriented Parsing (DOP) to a grammar formalism that allows for discontinuous trees (LCFRS). Decisions during parsing are conditioned on all possible fragments, resulting in improved performance. Despite the fact that both DOP and discontinuity present formidable challenges in terms of computational complexity, the model is reasonably efficient, and surpasses the state of the art in discontinuous parsing. 1