98,649 research outputs found
Acquiring Word-Meaning Mappings for Natural Language Interfaces
This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted
Examples), that acquires a semantic lexicon from a corpus of sentences paired
with semantic representations. The lexicon learned consists of phrases paired
with meaning representations. WOLFIE is part of an integrated system that
learns to transform sentences into representations such as logical database
queries. Experimental results are presented demonstrating WOLFIE's ability to
learn useful lexicons for a database interface in four different natural
languages. The usefulness of the lexicons learned by WOLFIE are compared to
those acquired by a similar system, with results favorable to WOLFIE. A second
set of experiments demonstrates WOLFIE's ability to scale to larger and more
difficult, albeit artificially generated, corpora. In natural language
acquisition, it is difficult to gather the annotated data needed for supervised
learning; however, unannotated data is fairly plentiful. Active learning
methods attempt to select for annotation and training only the most informative
examples, and therefore are potentially very useful in natural language
applications. However, most results to date for active learning have only
considered standard classification tasks. To reduce annotation effort while
maintaining accuracy, we apply active learning to semantic lexicons. We show
that active learning can significantly reduce the number of annotated examples
required to achieve a given level of performance
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
Automatic extraction of paraphrastic phrases from medium size corpora
This paper presents a versatile system intended to acquire paraphrastic
phrases from a representative corpus. In order to decrease the time spent on
the elaboration of resources for NLP system (for example Information
Extraction, IE hereafter), we suggest to use a machine learning system that
helps defining new templates and associated resources. This knowledge is
automatically derived from the text collection, in interaction with a large
semantic network
Recognizing cited facts and principles in legal judgements
In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, κ=0.65 and κ=0.95 for inter- and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall κ of 0.72 with the human-annotated gold standard
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