MUC 4

This paper reports on the joint GE-CMU Tipster SHOGUN customization effort for MUC-4 , and analyzes the results of the TST3 and TST4 runs in comparison with the GE system …...

In this paper, we present a learning ap- proach to the scenario template task of information extraction, where information filling one template could come from mul- tiple sentences. When tested …...

Many information extraction (IE) systems rely on manually annotated training data to learn patterns or rules for extracting in- formation about events. Manually anno- tating data is expensive, however, and …...

Extraction of relations between entities is an important part of Information Extraction on free text. Previous methods are mostly based on statistical correlation and depend- ency relations between entities. This …...

The efficiency of Information Extraction systems is known to be heavily influenced by domain-specific knowledge but the cost of developing such systems is consider- ably high. In this article, we …...

AbstractDocument-level entity-based extraction (EE), aiming at extracting entity-centric information such as entity roles and entity relations, is key to automatic knowledge acquisition from text corpora for various domains. Most document-level …...

AbstractWe revisit the classic problem of document-level role-filler entity extraction (REE) for template filling. We argue that sentence-level approaches are ill-suited to the task and introduce a generative transformer-based encoder-decoder …...

AbstractWe present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents …...