78,665 research outputs found
Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings
We present an unsupervised context-sensitive spelling correction method for
clinical free-text that uses word and character n-gram embeddings. Our method
generates misspelling replacement candidates and ranks them according to their
semantic fit, by calculating a weighted cosine similarity between the
vectorized representation of a candidate and the misspelling context. To tune
the parameters of this model, we generate self-induced spelling error corpora.
We perform our experiments for two languages. For English, we greatly
outperform off-the-shelf spelling correction tools on a manually annotated
MIMIC-III test set, and counter the frequency bias of a noisy channel model,
showing that neural embeddings can be successfully exploited to improve upon
the state-of-the-art. For Dutch, we also outperform an off-the-shelf spelling
correction tool on manually annotated clinical records from the Antwerp
University Hospital, but can offer no empirical evidence that our method
counters the frequency bias of a noisy channel model in this case as well.
However, both our context-sensitive model and our implementation of the noisy
channel model obtain high scores on the test set, establishing a
state-of-the-art for Dutch clinical spelling correction with the noisy channel
model.Comment: Appears in volume 7 of the CLIN Journal,
http://www.clinjournal.org/biblio/volum
Antecedent selection techniques for high-recall roreference resolution
We investigate methods to improve the recall in coreference resolution by also trying to resolve those definite descriptions where no earlier mention of the referent shares the same lexical head (coreferent bridging). The problem, which is notably harder than identifying coreference relations among mentions which have the same lexical head, has been tackled with several rather different approaches, and we attempt to provide a meaningful classification along with a quantitative comparison. Based on the different merits of the methods, we discuss possibilities to improve them and show how they can be effectively combined
Evidence combination for multi-point query learning in content-based image retrieval
In multipoint query learning a number of query representatives are selected based on the positive feedback samples. The similarity score to a multipoint query is obtained from merging the individual scores. In this paper, we investigate three different combination strategies and present a comparative evaluation of their performance. Results show that the performance of multipoint queries relies heavily on the right choice of settings for the fusion. Unlike previous results, suggesting that multipoint queries generally perform better than a single query representation, our evaluation results do not allow such an overall conclusion. Instead our study points to the type of queries for which query expansion is better suited than a single query, and vice versa
Linguistic Optimization
Optimality Theory (OT) is a model of language that combines aspects of generative and connectionist linguistics. It is unique in the field in its use of a rank ordering on constraints, which is used to formalize optimization, the choice of the best of a set of potential linguistic forms. We show that phenomena argued to require ranking fall out equally from the form of optimization in OT's predecessor Harmonic Grammar (HG), which uses numerical weights to encode the relative strength of constraints. We further argue that the known problems for HG can be resolved by adopting assumptions about the nature of constraints that have precedents both in OT and elsewhere in computational and generative linguistics. This leads to a formal proof that if the range of each constraint is a bounded number of violations, HG generates a finite number of languages. This is nontrivial, since the set of possible weights for each constraint is nondenumerably infinite. We also briefly review some advantages of HG
Being Omnipresent To Be Almighty: The Importance of The Global Web Evidence for Organizational Expert Finding
Modern expert nding algorithms are developed under the
assumption that all possible expertise evidence for a person
is concentrated in a company that currently employs the
person. The evidence that can be acquired outside of an
enterprise is traditionally unnoticed. At the same time, the
Web is full of personal information which is sufficiently detailed to judge about a person's skills and knowledge. In this work, we review various sources of expertise evidence out-side of an organization and experiment with rankings built on the data acquired from six dierent sources, accessible through APIs of two major web search engines. We show that these rankings and their combinations are often more realistic and of higher quality than rankings built on organizational data only
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