132,331 research outputs found
Extracting protein-protein interactions from text using rich feature vectors and feature selection
Because of the intrinsic complexity of natural language, automatically extracting accurate information from text remains a challenge. We have applied rich featurevectors derived from dependency graphs to predict protein-protein interactions using machine learning techniques. We present the first extensive analysis of applyingfeature selection in this domain, and show that it can produce more cost-effective models. For the first time, our technique was also evaluated on several large-scalecross-dataset experiments, which offers a more realistic view on model performance.
During benchmarking, we encountered several fundamental problems hindering comparability with other methods. We present a set of practical guidelines to set up ameaningful evaluation.
Finally, we have analysed the feature sets from our experiments before and after feature selection, and evaluated the contribution of both lexical and syntacticinformation to our method. The gained insight will be useful to develop better performing methods in this domain
CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information
Open Information Extraction (OpenIE) methods extract (noun phrase, relation
phrase, noun phrase) triples from text, resulting in the construction of large
Open Knowledge Bases (Open KBs). The noun phrases (NPs) and relation phrases in
such Open KBs are not canonicalized, leading to the storage of redundant and
ambiguous facts. Recent research has posed canonicalization of Open KBs as
clustering over manuallydefined feature spaces. Manual feature engineering is
expensive and often sub-optimal. In order to overcome this challenge, we
propose Canonicalization using Embeddings and Side Information (CESI) - a novel
approach which performs canonicalization over learned embeddings of Open KBs.
CESI extends recent advances in KB embedding by incorporating relevant NP and
relation phrase side information in a principled manner. Through extensive
experiments on multiple real-world datasets, we demonstrate CESI's
effectiveness.Comment: Accepted at WWW 201
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