4,085 research outputs found
Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling
In this paper we propose and carefully evaluate a sequence labeling framework
which solely utilizes sparse indicator features derived from dense distributed
word representations. The proposed model obtains (near) state-of-the art
performance for both part-of-speech tagging and named entity recognition for a
variety of languages. Our model relies only on a few thousand sparse
coding-derived features, without applying any modification of the word
representations employed for the different tasks. The proposed model has
favorable generalization properties as it retains over 89.8% of its average POS
tagging accuracy when trained at 1.2% of the total available training data,
i.e.~150 sentences per language
Evaluating the semantic web: a task-based approach
The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!
Argumentation mining (AM) requires the identification of complex discourse
structures and has lately been applied with success monolingually. In this
work, we show that the existing resources are, however, not adequate for
assessing cross-lingual AM, due to their heterogeneity or lack of complexity.
We therefore create suitable parallel corpora by (human and machine)
translating a popular AM dataset consisting of persuasive student essays into
German, French, Spanish, and Chinese. We then compare (i) annotation projection
and (ii) bilingual word embeddings based direct transfer strategies for
cross-lingual AM, finding that the former performs considerably better and
almost eliminates the loss from cross-lingual transfer. Moreover, we find that
annotation projection works equally well when using either costly human or
cheap machine translations. Our code and data are available at
\url{http://github.com/UKPLab/coling2018-xling_argument_mining}.Comment: Accepted at Coling 201
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