[PDF] Top 20 Injecting Logical Background Knowledge into Embeddings for Relation Extraction

... schema knowledge base using matrix factorization suffers from a problem all distantly-supervised techniques share: you can only reliably learn relations that ap- pear frequently enough in the knowledge ... See full document

... scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation ...perform relation identification with cross- entropy loss and ... See full document

... Biaffine Relation Attention Network, based on the Transformer self-attention model (Vaswani et ...CID relation extraction task, as compared to the impact of the full model ... See full document

... Compositional embedding models build a rep- resentation for a linguistic structure based on its component word embeddings. While re- cent work has combined these word embed- dings with hand crafted features for ... See full document

... transfer knowledge from data-rich and semantically similar head classes to data-poor tail classes (Wang et ...long-tail relation /peo- ple/deceased person/place of burial and head relation ... See full document

... Lao et al. (2012) proposed the first work aiming to perform RE employing both KB data and text, using a rule-based random walk method. Recently, Riedel et al. (2013) proposed another joint approach based on collaborative ... See full document

... for relation extraction to handle unseen ...a relation pair or not. (3) We propose a novel Word Relation Autoencoder (WRAE) which can effectively reduce ... See full document

... through Relation Extraction (RE), the task of predicting KB relations expressed in text mentioning entities known to the ...KB Embeddings (KBE) for link ... See full document

... entity embeddings that are jointly learned with skip gram (Mikolov et ...learned embeddings are ...entity embeddings preserve the relationships between entities, which help to build a highly accurate ... See full document

... Manirupa Das, Eric Fosler-Lussier, Simon Lin, Soheil Moosavinasab, David Chen, Steve Rust, Yungui Huang and Rajiv Ramnath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... See full document

... relation extraction. Zeng et al. (2014) showed that CNN with position embeddings is effective for rela- tion ...auxiliary embeddings (Lee et ...distance relation patterns (Xu et ... See full document

... word embeddings that are based on sub-word in- formation capture spelling variation and morpho- logical information better than the embeddings that take word as a ...fasttext embeddings ... See full document

... The models that take the context into account perform similar to the baselines at the smallest re- call numbers, but start to positively deviate from them at higher recall rates. In particular, the ContextAtt model ... See full document

... Domain Knowledge In the domain of knowledge bases, the notion of entity types is the side information that commonly exists and dictates whether some entities can be legitimate arguments of a given ...the ... See full document

... any relation-specific training data and allows to incorporate global constraints general express- ing domain ...the relation extraction task, explain how to incorporate meaningful con- straints, and ... See full document

... microscopic evaluation the preparations were counterstained with hematoxylin and mounted. The preparations were evaluated under a BH−2 Olympus light microscope. The localization, dis− tribution, and intensity of ... See full document

... of relation tuples is available in the knowledge ...the knowledge base for training, ...and relation are used ...mation extraction task. Information retrieval for Web relation ... See full document

... training data due to their high-precision low-recall features, which were originally proposed by Mintz et al. (2009). We present a reliable and novel way to address these issues and achieve significant improvement over ... See full document

... Since then, it has gain popularity (Mintz et al., 2009; Bunescu and Mooney, 2007; Wu and Weld, 2007; Riedel et al., 2010; Hoffmann et al., 2011; Sur- deanu et al., 2012; Nguyen and Moschitti, 2011). To tolerate noisy ... See full document

... TextRunner (Banko et al., 2007) was the first Open IE system, which trains a Naïve Bayes classifier with POS and NP-chunk features to extract relationships between entities. The subsequent work showed that employing the ... See full document