Cross-Sentence N-ary Relation Extraction with Graph LSTMs

Cross-Sentence N-ary Relation Extraction with Graph LSTMs

Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cr...

N-ary Biographical Relation Extraction using Shortest Path Dependencies

Modern question answering and summarizing systems have motivated the need for complex n-ary relation extraction systems where the number of related entities (n) can be more than two. Shortest path dependency kernels have been proven to be effective in extracting binary relations. In this work, we propose a method that employs shortest path dependency based rules to extract complex n-ary relatio...

Language Resources and Annotation Tools for Cross-Sentence Relation Extraction

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...

Sentence Compression by Deletion with LSTMs

We present an LSTM approach to deletion-based sentence compression where the task is to translate a sentence into a sequence of zeros and ones, corresponding to token deletion decisions. We demonstrate that even the most basic version of the system, which is given no syntactic information (no PoS or NE tags, or dependencies) or desired compression length, performs surprisingly well: around 30% ...

On $n$-ary Hypergroups and Fuzzy $n$-ary Homomorphism