Entity Attribute Relation Extraction with Attribute-Aware Embeddings (2020)

Abstract: Entity-attribute relations are a fundamental component for building large-scale knowledge bases, which are widely employed in modern search engines. However, most such knowledge bases are manually curated, covering only a small fraction of all attributes, even for common entities. To improve the precision of model-based entity-attribute extraction, we propose attribute-aware embeddings, which embeds entities and attributes in the same space by the similarity of their attributes. Our model, EANET, learns these embeddings by representing entities as a weighted sum of their attributes and concatenates these embeddings to mention level features. EANET achieves up to 91% classification accuracy, outperforming strong baselines and achieves 83% precision on manually labeled high confidence extractions, outperforming Biperpedia (Gupta et al., 2014), a previous state-of-the-art for large scale entity-attribute extraction.

## introduction modern search engines often attempt to provide structured search results that reveal more facets of the search query than explicitly requested. these results rely on knowledge bases that contain tuples of the form (entity, attribute, value). however, the number of known entities and attributes in these knowledge bases is limited and there is a long tail of both entities and attributes that is too large to be manually curated. the goal of automatic entityattribute extraction is to replace manual knowledge acquisition which is expensive and biased towards popular entities @xcite their precision has not been high enough to replace manually curated knowledge bases @xcite @xcite . the key insight of this paper is that entities that share many attributes are often similar. this is an extension of the distributional hypothesis, @xcite , which states that words with similar semantic meanings tend to appear in similar contexts, and builds on work that use referential attributes to estimate semantic relatedness @xcite . for the attribute-aware embeddings, we argue that a good representation for an entity can be inferred from its most common attributes, which we may have access to from an external source of knowledge. in figure 1 , we want to classify two candidate relations given the other known relations. if the model has previously seen a link between teams and league but not movies and league, we can correctly predict that ronaldo should have the league attribute and speilberg should not, despite the surface form in the sentences being similar. we generalize this intuition by learning embeddings for each entity based on the most common attributes for that entity. we propose eanet, a neural model that combines a path-embedding model from dependency parse trees, with attribute-aware embeddings which we describe in this paper. the proposed model captures both the dependency path for a given sentence between potential entity and attribute candidates, as well as a distributional representation for both entities and attributes based on an attribute distributional assumption. our model learns general representations for specific entities with few mentions by learning embeddings based on attributes for those entities observed in the distant supervision. ## experiments we evaluate our model on two basic metrics. (1) the ability of the model to fit the data and generalize to a held-out test set which is measured by accuracy on the test set. ## conclusion we present eanet, a neural model for entityattribute relation extraction from text. we describe a mechanism for learning attribute-aware embeddings for entities and attributes in our training data that capture similarities between entities by embedding the similarities between their known attributes. we show that our model outperforms the previous approaches for entity-attribute relation extraction and that it can be used to learn representations for longtail entities.