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Information extraction (IE) holds the promise of generating a large-scale knowledge base from the Web’s natural language text. Knowledge-based weak supervision, using structured data to heuristically label a training corpus, …...

We study how to extend a large knowledge base (Freebase) by reading relational informa- tion from a large Web text corpus. Previous studies on extracting relational knowledge from text show …...

This paper proposes a novel approach for rela- tion extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is …...

Web search can be enhanced in powerful ways if to- ken spans in Web text are annotated with disambiguated entities from large catalogs like Freebase. Entity anno- tators need to …...

Many errors in coreference resolution come from semantic mismatches due to inadequate world knowledge. Errors in named-entity linking (NEL), on the other hand, are of- ten caused by superficial modeling …...

We consider the challenge of learning seman- tic parsers that scale to large, open-domain problems, such as question answering with Freebase. In such settings, the sentences cover a wide variety …...

In this paper, we propose and demonstrate Exploratory Relation Extraction (ERE), a novel approach to identifying and extracting relations from large text corpora based on user-driven and data-guided incremental exploration. …...

Much work in recent years has gone into the construction of large knowledge bases (KBs), such as Freebase, DBPedia, NELL, and YAGO. While these KBs are very large, they are …...

Answering natural language questions us- ing the Freebase knowledge base has re- cently been explored as a platform for ad- vancing the state of the art in open do- main …...

Much recent work focuses on formal in- terpretation of natural question utterances, with the goal of executing the resulting structured queries on knowledge graphs (KGs) such as Freebase. Here we …...

We examine the embedding approach to reason new relational facts from a large- scale knowledge graph and a text corpus. We propose a novel method of jointly em- bedding entities …...

AbstractIn recent years large repositories of structured knowledge (DBpedia, Freebase, YAGO) have become a valuable resource for language technologies, especially for the automatic aggregation of knowledge from textual data. One …...

Existing semantic parsing research has steadily improved accuracy on a few do- mains and their corresponding meaning representations. In this paper, we present a novel supervised semantic parsing algo- rithm, …...

In this paper we study the identification and verification of simple claims about statistical properties, e.g. claims about the population or the inflation rate of a coun- try. We show …...

AbstractWe present an approach to learning a model-theoretic semantics for natural language tied to Freebase. Crucially, our approach uses an open predicate vocabulary, enabling it to produce denotations for phrases …...

Relation extraction is the task of ex- tracting predicate-argument relationships between entities from natural language text. This paper investigates whether back- ground information about entities avail- able in knowledge bases …...

Wikipedia is a resource of choice ex- ploited in many NLP applications, yet we are not aware of recent attempts to adapt coreference resolution to this resource. In this work, …...

Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for K- B completion. The path ranking algorith- m (PRA) is one of the most promising ap- proaches to this …...

Deep inference on a large-scale knowledge base (KB) needs a mass of formulas, but it is almost impossible to create all formulas man- ually. Data-driven methods have been pro- posed …...

We propose a new open question answer- ing framework for question answering over a knowledge base (KB). Our system uses both a curated KB, Freebase, and one that is ex- …...

AbstractThis work focuses on answering single-relation factoid questions over Freebase. Each question can acquire the answer from a single fact of form (subject, predicate, object) in Freebase. This task, simple …...

AbstractQuestion answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing …...

AbstractUniversal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to …...

AbstractQuestion answering over knowledge graphs is an important problem of interest both commercially and academically. There is substantial interest in the class of natural language questions that can be answered …...

AbstractMulti-hop knowledge graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous methods assume that every …...

AbstractPre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks. In this work, we investigate how to learn a general-purpose …...

AbstractThe official voting records of United States congresspeople are preserved as roll call votes. Prediction of voting behavior of politicians for whom no voting record exists, such as individuals running …...

AbstractMulti-hop reasoning has been widely studied in recent years to seek an effective and interpretable method for knowledge graph (KG) completion. Most previous reasoning methods are designed for dense KGs …...