This document describes the methods and results for our participation in the BioNLP’09 Shared Task #1 on Event Extraction. It also contains some error analysis and a brief discussion of the results. Previous shared tasks in the BioNLP community have focused on extracting gene and protein names, and on finding (direct) protein-protein interactions (PPI). This year’s task was slightly different, ...

Unsupervised word sense disambiguation (WSD) methods are an attractive approach to all-words WSD due to their non-reliance on expensive annotated data. Unsupervised estimates of sense frequency have been shown to be very useful for WSD due to the skewed nature of word sense distributions. This paper presents a fully unsupervised topic modelling-based approach to sense frequency estimation, whic...

In this work, we analyse the performance of different density-based algorithms on short-text and narrow domain short-text corpora. We attempt to determine to what extent the features of this kind of corpora impact on the density computation of the clusterings obtained and how robust these algorithms to the different complexity levels are.

The dataset of physical properties for the proposed CIGS solar cell with Cd-free AlGaAs buffer layer has been depicted in this data article. The cell performance outcome due to different AlGaAs buffer layer band gap is reported along with optimum solar cell performance parameters for instance, open circuit voltage [Formula: see text], short circuit current density ([Formula: see text], fill fac...

Categorical compositional distributional model of [9] suggests a way to combine grammatical composition of the formal, type logical models with the corpus based, empirical word representations of distributional semantics. This paper contributes to the project by expanding the model to also capture entailment relations. This is achieved by extending the representations of words from points in me...

We introduce a discriminative approach to vocabulary independent term frequency estimation. Using two separate corpora and recognition systems, we show that our model can perform significantly better than a previously established generative model at this task.

We present Song2Quartet, a system for generating string quartet versions of popular songs by combining probabilistic models estimated from a corpus of symbolic classical music with the target audio file of any song. Song2Quartet allows users to add novelty to listening experience of their favorite songs and gain familiarity with string quartets. Previous work in automatic arrangement of music o...

This study was intended first to categorize the L2 learners in terms of their learning style preferences and second to investigate if their learning preferences are related to lexical inferencing. Moreover, strategies used for lexical inferencing and text related issues of text density and parts of speech were studied to determine their moderating effects and the best predictors of lexical infe...