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Papers in this group tagged with "relation extraction"

1 - 20 of 21
  1. This paper describes a method for learning which relations are highly associated with a given seed relation such as marriage or working for a company. Relation instances taken from a large knowledge base are used as seeds for obtaining candidate…
  2. A large number of Open Relation Extraction approaches have been proposed recently, covering a wide range of NLP machinery, from “shallow” (e.g., part-of-speech tagging) to “deep” (e.g., semantic role labeling–SRL). A natural question then is what is…
  3. This paper describes an unsupervised method for extracting product attributes and their values from an e-commerce product page. Previously, distant supervision has been applied for this task, but it is not applicable in domains where no reliable…
  4. An ontology is defined using concepts and relationships between the concepts. In this paper, we focus on second problem: relation extraction from plain text. Generic Knowledge Bases like YAGO, Freebase, and DBPedia have made accessible huge…
  5. Relation Extraction methods based on Distant Supervision rely on true tuples to retrieve noisy mentions, which are then used to train traditional supervised relation extraction methods. In this paper we analyze the sources of noise in the mentions,…
  6. This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on two scoring functions that operate by learning low-dimensional…
  7. The past decade has seen the emergence of web-scale structured and linked semantic knowledge resources (e.g., Freebase, DBPedia). These semantic knowledge graphs provide a scalable “schema for the web”, representing a significant opportunity for the…
  8. Distant supervision has attracted recent interest for training information extraction systems because it does not require any human annotation but rather employs existing knowledge bases to heuristically label a training corpus. However, previous…
  9. Distant supervision, heuristically labeling a corpus using a knowledge base, has emerged as a popular choice for training relation extractors. In this paper, we show that a significant number of “negative“ examples generated by the labeling process…
  10. Existing semantic parsing research has steadily improved accuracy on a few domains and their corresponding databases. This paper introduces FreeParser, a system that trains on one domain and one set of predicate and constant symbols, and then can…
  11. The Web contains more text than any other source in human history, and continues to expand rapidly. Computer algorithms to process and extract knowledge from Web text have the potential not only to improve Web search, but also to collect a sizable…
  12. In relation extraction, distant supervision seeks to extract relations between entities from text by using a knowledge base, such as Freebase, as a source of supervision. When a sentence and a knowledge base refer to the same entity pair, this…
  13. We describe the use of a hierarchical topic model for automatically identifying syntactic and lexical patterns that explicitly state ontological relations. We leverage distant supervision using relations from the knowledge base FreeBase, but do not…
  14. Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e.g., person,…
  15. We present a method for training a semantic parser using only a knowledge base and an unlabeled text corpus, without any individually annotated sentences. Our key observation is that multiple forms of weak supervision can be combined to train an…
  16. Classically, training relation extractors relies on high-quality, manually annotated training data, which can be expensive to obtain. To mitigate this cost, NLU researchers have considered two newly available sources of less expensive (but…
  17. We present a novel approach to relation extraction that integrates information across documents, performs global inference and requires no labelled text. In particular, we tackle relation extraction and entity identification jointly. We use distant…