Abstract Multimodal integration of text, layout and visual information has achieved SOTA results in visually rich document understanding (VrDU) tasks, including relation extraction (RE). However, despite its importance, evaluation the relative predictive capacity these modalities is less prevalent. Here, we demonstrate value shared representations for RE tasks by conducting experiments which ea...

Temporal relation extraction is important for understanding the ordering of events in narrative text. We describe a method for increasing the number of high-quality training instances available to a temporal relation extraction task, with an adaptation to different annotation styles in the clinical domain by taking advantage of the Unified Medical Language System (UMLS). This method notably imp...

We report on experiments to measure the effect of speech recognition errors and automatic punctuation insertion errors on the performance of information extraction (entity and relation extraction). The outputs of several recognition systems with a range of word error rates (WER), along with punctuation insertion, were fed into a system that extracts entities and relations from the recognized te...

Information extraction from text databases is a useful paradigm to populate relational tables and unlock the considerable value hidden in plain-text documents. However, information extraction can be expensive, due to various complex text processing steps necessary in uncovering the hidden data. There are a large number of text databases available, and not every text database is necessarily rele...

The task of Relation Extraction from texts is one of the main challenges in the area of Information Extraction, considering the required linguistic knowledge and the sophistication of the language processing techniques employed. This task aims at identifying and classifying semantic relations that occur between entities recognized in a given text. In this paper, we evaluated a Conditional Rando...

SpaceEval (SemEval 2015 Task 8), which concerns spatial information extraction, builds on the spatial role identification tasks introduced in SemEval 2012 and used in SemEval 2013. Among the host of subtasks presented in SpaceEval, we participated in subtask 3a, which focuses solely on spatial relation extraction. To address the complexity of a MOVELINK, we decompose it into smaller relations s...

Relation extraction is the task of recognizing semantic relations among entities. Given a particular sentence supervised approaches to Relation Extraction employed feature or kernel functions which usually have a single sentence in their scope. The overall aim of this paper is to propose methods for using knowledge and resources that are external to the target sentence, as a way to improve rela...