The CoNLL 2007 shared task on dependency parsing

The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learning systems on the same data sets. In 2007, as in 2006, the shared task has been devoted to dependency parsing, this year with both a multilingual track and a domain adaptation track. In this paper, we define the tasks of the different tracks and describe how the data sets were created from existing treebanks for ten languages. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results

The Impact of Annotation on the Performance of Protein Tagging in Biomedical Text

In this paper we discuss five different corpora annotated for protein names. We present several within- and cross-dataset protein tagging experiments showing that different annotation schemes severely affect the portability of statistical protein taggers. By means of a detailed error analysis we identify crucial annotation issues that future annotation projects should take into careful consideration

Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks

The stream of words produced by Automatic Speech Recognition (ASR) systems is typically devoid of punctuations and formatting. Most natural language processing applications expect segmented and well-formatted texts as input, which is not available in ASR output. This paper proposes a novel technique of jointly modeling multiple correlated tasks such as punctuation and capitalization using bidirectional recurrent neural networks, which leads to improved performance for each of these tasks. This method could be extended for joint modeling of any other correlated sequence labeling tasks.Comment: Accepted in Interspeech 201

Using machine-learning to assign function labels to parser output for Spanish

Data-driven grammatical function tag assignment has been studied for English using the Penn-II Treebank data. In this paper we address the question of whether such methods can be applied successfully to other languages and treebank resources. In addition to tag assignment accuracy and f-scores we also present results of a task-based evaluation. We use three machine-learning methods to assign Cast3LB function tags to sentences parsed with Bikel’s parser trained on the Cast3LB treebank. The best performing method, SVM, achieves an f-score of 86.87% on gold-standard trees and 66.67% on parser output - a statistically significant improvement of 6.74% over the baseline. In a task-based evaluation we generate LFG functional-structures from the function tag-enriched trees. On this task we achive an f-score of 75.67%, a statistically significant 3.4% improvement over the baseline

Recovering capitalization and punctuation marks for automatic speech recognition: case study for Portuguese broadcast news

The following material presents a study about recovering punctuation marks, and capitalization information from European Portuguese broadcast news speech transcriptions. Different approaches were tested for capitalization, both generative and discriminative, using: finite state transducers automatically built from language models; and maximum entropy models. Several resources were used, including lexica, written newspaper corpora and speech transcriptions. Finite state transducers produced the best results for written newspaper corpora, but the maximum entropy approach also proved to be a good choice, suitable for the capitalization of speech transcriptions, and allowing straightforward on-the-fly capitalization. Evaluation results are presented both for written newspaper corpora and for broadcast news speech transcriptions. The frequency of each punctuation mark in BN speech transcriptions was analyzed for three different languages: English, Spanish and Portuguese. The punctuation task was performed using a maximum entropy modeling approach, which combines different types of information both lexical and acoustic. The contribution of each feature was analyzed individually and separated results for each focus condition are given, making it possible to analyze the performance differences between planned and spontaneous speech. All results were evaluated on speech transcriptions of a Portuguese broadcast news corpus. The benefits of enriching speech recognition with punctuation and capitalization are shown in an example, illustrating the effects of described experiments into spoken texts.info:eu-repo/semantics/acceptedVersio