987 research outputs found
Italian Event Detection Goes Deep Learning
This paper reports on a set of experiments with different word embeddings to
initialize a state-of-the-art Bi-LSTM-CRF network for event detection and
classification in Italian, following the EVENTI evaluation exercise. The net-
work obtains a new state-of-the-art result by improving the F1 score for
detection of 1.3 points, and of 6.5 points for classification, by using a
single step approach. The results also provide further evidence that embeddings
have a major impact on the performance of such architectures.Comment: to appear at CLiC-it 201
ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing
In this paper, we present a novel unsupervised algorithm for word sense
disambiguation (WSD) at the document level. Our algorithm is inspired by a
widely-used approach in the field of genetics for whole genome sequencing,
known as the Shotgun sequencing technique. The proposed WSD algorithm is based
on three main steps. First, a brute-force WSD algorithm is applied to short
context windows (up to 10 words) selected from the document in order to
generate a short list of likely sense configurations for each window. In the
second step, these local sense configurations are assembled into longer
composite configurations based on suffix and prefix matching. The resulted
configurations are ranked by their length, and the sense of each word is chosen
based on a voting scheme that considers only the top k configurations in which
the word appears. We compare our algorithm with other state-of-the-art
unsupervised WSD algorithms and demonstrate better performance, sometimes by a
very large margin. We also show that our algorithm can yield better performance
than the Most Common Sense (MCS) baseline on one data set. Moreover, our
algorithm has a very small number of parameters, is robust to parameter tuning,
and, unlike other bio-inspired methods, it gives a deterministic solution (it
does not involve random choices).Comment: In Proceedings of EACL 201
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