An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Digital processing of speech signals

https://www.cs.tufts.edu/comp/150AIH/pdf/NadkarniOC11.pdf

Natural language processing: an introduction.

C4.5: Programs for Machine Learning (書評)

Each year the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their systems on exactly the same data sets, in order to better compare systems. The tenth CoNLL (CoNLL-X) saw a shared task on Multilingual Dependency Parsing. In this paper, we describe how treebanks for 13 languages were converted into the same dependency format and how parsing performance was measured. We also give an overview of the parsing approaches that participants took and the results that they achieved. Finally, we try to draw general conclusions about multi-lingual parsing: What makes a particular language, treebank or annotation scheme easier or harder to parse and which phenomena are challenging for any dependency parser?

/pdf/conll-x-shared-task-on-multilingual-dependency-parsing-wnslqiaqbu.pdf

CoNLL-X Shared Task on Multilingual Dependency Parsing

Detecting uncertain and negative assertions is essential in most BioMedical Text Mining tasks where, in general, the aim is to derive factual knowledge from textual data. This article reports on a corpus annotation project that has produced a freely available resource for research on handling negation and uncertainty in biomedical texts (we call this corpus the BioScope corpus). The corpus consists of three parts, namely medical free texts, biological full papers and biological scientific abstracts. The dataset contains annotations at the token level for negative and speculative keywords and at the sentence level for their linguistic scope. The annotation process was carried out by two independent linguist annotators and a chief linguist – also responsible for setting up the annotation guidelines – who resolved cases where the annotators disagreed. The resulting corpus consists of more than 20.000 sentences that were considered for annotation and over 10% of them actually contain one (or more) linguistic annotation suggesting negation or uncertainty. Statistics are reported on corpus size, ambiguity levels and the consistency of annotations. The corpus is accessible for academic purposes and is free of charge. Apart from the intended goal of serving as a common resource for the training, testing and comparing of biomedical Natural Language Processing systems, the corpus is also a good resource for the linguistic analysis of scientific and clinical texts.

/pdf/the-bioscope-corpus-biomedical-texts-annotated-for-4c6iasd9fg.pdf

The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes

The CoNLL-2010 Shared Task was dedicated to the detection of uncertainty cues and their linguistic scope in natural language texts. The motivation behind this task was that distinguishing factual and uncertain information in texts is of essential importance in information extraction. This paper provides a general overview of the shared task, including the annotation protocols of the training and evaluation datasets, the exact task definitions, the evaluation metrics employed and the overall results. The paper concludes with an analysis of the prominent approaches and an overview of the systems submitted to the shared task.

/pdf/the-conll-2010-shared-task-learning-to-detect-hedges-and-40vj2cbdoz.pdf

The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text

Bin packing approximation algorithms: Survey and classification

This article reports on a corpus annotation project that has produced a freely available resource for research on handling negation and uncertainty in biomedical texts (we call this corpus the BioScope corpus). The corpus consists of three parts, namely medical free texts, biological full papers and biological scientific abstracts. The dataset contains annotations at the token level for negative and speculative keywords and at the sentence level for their linguistic scope. The annotation process was carried out by two independent linguist annotators and a chief annotator -- also responsible for setting up the annotation guidelines -- who resolved cases where the annotators disagreed. We will report our statistics on corpus size, ambiguity levels and the consistency of annotations.

/pdf/the-bioscope-corpus-annotation-for-negation-uncertainty-and-1qqnsqtgq6.pdf

János Csirik

Papers

The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes

The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text

Bin packing approximation algorithms: Survey and classification

The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts

On-line Packing and Covering Problems