1,222 research outputs found
Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge
Citation texts are sometimes not very informative or in some cases inaccurate
by themselves; they need the appropriate context from the referenced paper to
reflect its exact contributions. To address this problem, we propose an
unsupervised model that uses distributed representation of words as well as
domain knowledge to extract the appropriate context from the reference paper.
Evaluation results show the effectiveness of our model by significantly
outperforming the state-of-the-art. We furthermore demonstrate how an effective
contextualization method results in improving citation-based summarization of
the scientific articles.Comment: SIGIR 201
TiFi: Taxonomy Induction for Fictional Domains [Extended version]
Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
We consider the task of inferring is-a relationships from large text corpora.
For this purpose, we propose a new method combining hyperbolic embeddings and
Hearst patterns. This approach allows us to set appropriate constraints for
inferring concept hierarchies from distributional contexts while also being
able to predict missing is-a relationships and to correct wrong extractions.
Moreover -- and in contrast with other methods -- the hierarchical nature of
hyperbolic space allows us to learn highly efficient representations and to
improve the taxonomic consistency of the inferred hierarchies. Experimentally,
we show that our approach achieves state-of-the-art performance on several
commonly-used benchmarks
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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