Kuzman Ganchev

Overview of BioCreative II gene mention recognition

Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all the methods used and a statistical analysis of the results. We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.

Globally Normalized Transition-Based Neural Networks

We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of-speech tagging, dependency parsing and sentence compression results. Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models. We discuss the importance of global as opposed to local normalization: a key insight is that the label bias problem implies that globally normalized models can be strictly more expressive than locally normalized models.

Dependency Grammar Induction via Bitext Projection Constraints

Broad-coverage annotated treebanks necessary to train parsers do not exist for many resource-poor languages. The wide availability of parallel text and accurate parsers in English has opened up the possibility of grammar induction through partial transfer across bitext. We consider generative and discriminative models for dependency grammar induction that use word-level alignments and a source language parser (English) to constrain the space of possible target trees. Unlike previous approaches, our framework does not require full projected parses, allowing partial, approximate transfer through linear expectation constraints on the space of distributions over trees. We consider several types of constraints that range from generic dependency conservation to language-specific annotation rules for auxiliary verb analysis. We evaluate our approach on Bulgarian and Spanish CoNLL shared task data and show that we consistently outperform unsupervised methods and can outperform supervised learning for limited training data.

Automatic Code Assignment to Medical Text

Code assignment is important for handling large amounts of electronic medical data in the modern hospital. However, only expert annotators with extensive training can assign codes. We present a system for the assignment of ICD-9-CM clinical codes to free text radiology reports. Our system assigns a code configuration, predicting one or more codes for each document. We combine three coding systems into a single learning system for higher accuracy. We compare our system on a real world medical dataset with both human annotators and other automated systems, achieving nearly the maximum score on the Computational Medicine Centers challenge.

Censored exploration and the dark pool problem

Dark pools are a recent type of stock exchange in which information about outstanding orders is deliberately hidden in order to minimize the market impact of large-volume trades. The success and proliferation of dark pools have created challenging and interesting problems in algorithmic trading---in particular, the problem of optimizing the allocation of a large trade over multiple competing dark pools. In this work, we formalize this optimization as a problem of multi-venue exploration from censored data, and provide a provably efficient and near-optimal algorithm for its solution. Our algorithm and its analysis have much in common with well-studied algorithms for managing the exploration--exploitation trade-off in reinforcement learning. We also provide an extensive experimental evaluation of our algorithm using dark pool execution data from a large brokerage.

Learning tractable word alignment models with complex constraints

Word-level alignment of bilingual text is a critical resource for a growing variety of tasks. Probabilistic models for word alignment present a fundamental trade-off between richness of captured constraints and correlations versus efficiency and tractability of inference. In this article, we use the Posterior Regularization framework (Graça, Ganchev, and Taskar 2007) to incorporate complex constraints into probabilistic models during learning without changing the efficiency of the underlying model. We focus on the simple and tractable hidden Markov model, and present an efficient learning algorithm for incorporating approximate bijectivity and symmetry constraints. Models estimated with these constraints produce a significant boost in performance as measured by both precision and recall of manually annotated alignments for six language pairs. We also report experiments on two different tasks where word alignments are required: phrase-based machine translation and syntax transfer, and show promising improvements over standard methods.

Semi-Automated Named Entity Annotation

We investigate a way to partially automate corpus annotation for named entity recognition, by requiring only binary decisions from an annotator. Our approach is based on a linear sequence model trained using a k-best MIRA learning algorithm. We ask an annotator to decide whether each mention produced by a high recall tagger is a true mention or a false positive. We conclude that our approach can reduce the effort of extending a seed training corpus by up to 58%.

PostCAT - Posterior Constrained Alignment Toolkit

PostCAT - Posterior Constrained Alignment Toolkit In this paper we present a new open-source toolkit for statistical word alignments - Posterior Constrained Alignment Toolkit (PostCAT). The toolkit implements three well known word alignment algorithms (IBM M1, IBM M2, HMM) as well as six new models. In addition to the usual Viterbi decoding scheme, the toolkit provides posterior decoding with several flavors for tuning the threshold. The toolkit also provides an implementation of alignment symmetrization heuristics and a set of utilities for analyzing and pretty printing alignments. The new models have already been shown to improve intrinsic alignment metrics and also to lead to better translations when integrated into a state of the art machine translation system. The toolkit is developed in Java and available in source at its website1. We encourage other researchers to build on our work by modifying the toolkit and using it for their research.