We explore methods to extract relations between named entities from free text in an unsupervised setting. In addition to standard feature extraction, we develop a novel method to re-weight word embeddings. We alleviate the problem of features sparsity using an individual feature reduction. Our approach exhibits a significant improvement by 5.8% over the state-of-the-art relation clustering scor...

In this paper, we present a novel combination of two types of language resources dedicated to the detection of relevant relations (RE) such as events or facts across sentence boundaries. One of the two resources is the sar-graph, which aggregates for each target relation ten thousands of linguistic patterns of semantically associated relations that signal instances of the target relation (Uszko...

Relation extraction between entities from text plays an important role in information extraction and knowledge discovery related tasks. Relation extraction systems produce a large number of candidates where many of them are not correct. A relation validation method justifies a claimed relation based on the information provided by a system. In this paper, we propose some features by analyzing th...

Traditional relation extraction seeks to identify pre-specified semantic relations within natural language text, while open Information Extraction (Open IE) takes a more general approach, and looks for a variety of relations without restriction to a fixed relation set. With this generalization comes the question, what is a relation? For example, should the more general task be restricted to rel...

We consider semi-supervised learning of information extraction methods, especially for extracting instances of noun categories (e.g., ‘athlete,’ ‘team’) and relations (e.g., ‘playsForTeam(athlete,team)’). Semisupervised approaches using a small number of labeled examples together with many unlabeled examples are often unreliable as they frequently produce an internally consistent, but neverthel...

We present INDREX-MM, a main memory database system for interactively executing two interwoven tasks, declarative relation extraction from text and their exploitation with SQL. INDREXMM simplifies these tasks for the user with powerful SQL extensions for gathering statistical semantics, for executing open information extraction and for integrating relation candidates with domain specific data. ...

Relation extraction is a part of Information Extraction and an established task in Natural Language Processing. This paper presents an overview of the main directions of research and recent advances in the field. It reviews various techniques used for relation extraction including knowledge-based, supervised and self-supervised methods. We also mention applications of relation extraction and id...