Collaborative Development and Evaluation of Text-processing Workflows in a UIMA-supported Web-based Workbench

Challenges in creating comprehensive text-processing worklows include a lack of the interoperability of individual components coming from different providers and/or a requirement imposed on the end users to know programming techniques to compose such workflows. In this paper we demonstrate Argo, a web-based system that addresses these issues in several ways. It supports the widely adopted Unstructured Information Management Architecture (UIMA), which handles the problem of interoperability; it provides a web browser-based interface for developing workflows by drawing diagrams composed of a selection of available processing components; and it provides novel user-interactive analytics such as the annotation editor which constitutes a bridge between automatic processing and manual correction. These features extend the target audience of Argo to users with a limited or no technical background. Here, we focus specifically on the construction of advanced workflows, involving multiple branching and merging points, to facilitate various comparative evalutions. Together with the use of user-collaboration capabilities supported in Argo, we demonstrate several use cases including visual inspections, comparisions of multiple processing segments or complete solutions against a reference standard, inter-annotator agreement, and shared task mass evaluations. Ultimetely, Argo emerges as a one-stop workbench for defining, processing, editing and evaluating text processing tasks

Building trainable taggers in a web-based, UIMA-supported NLP workbench

Argo is a web-based NLP and text mining workbench with a convenient graphical user interface for designing and executing processing workflows of various complexity. The workbench is intended for specialists and nontechnical audiences alike, and provides the ever expanding library of analytics compliant with the Unstructured Information Management Architecture, a widely adopted interoperability framework. We explore the flexibility of this framework by demonstrating workflows involving three processing components capable of performing self-contained machine learning-based tagging. The three components are responsible for the three distinct tasks of 1) generating observations or features, 2) training a statistical model based on the generated features, and 3) tagging unlabelled data with the model. The learning and tagging components are based on an implementation of conditional random fields (CRF); whereas the feature generation component is an analytic capable of extending basic token information to a comprehensive set of features. Users define the features of their choice directly from Argo’s graphical interface, without resorting to programming (a commonly used approach to feature engineering). The experimental results performed on two tagging tasks, chunking and named entity recognition, showed that a tagger with a generic set of features built in Argo is capable of competing with taskspecific solutions.

Revisiting Unsupervised Relation Extraction

Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, which rely either on hand-crafted features or surface form. However, we demonstrate that by using only named entities to induce relation types, we can outperform existing methods on two popular datasets. We conduct a comparison and evaluation of our findings with other URE techniques, to ascertain the important features in URE. We conclude that entity types provide a strong inductive bias for URE.Comment: 8 pages, 1 figure, 2 tables. Accepted in ACL 202

Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks

When constructing models that learn from noisy labels produced by multiple annotators, it is important to accurately estimate the reliability of annotators. Annotators may provide labels of inconsistent quality due to their varying expertise and reliability in a domain. Previous studies have mostly focused on estimating each annotator's overall reliability on the entire annotation task. However, in practice, the reliability of an annotator may depend on each specific instance. Only a limited number of studies have investigated modelling per-instance reliability and these only considered binary labels. In this paper, we propose an unsupervised model which can handle both binary and multi-class labels. It can automatically estimate the per-instance reliability of each annotator and the correct label for each instance. We specify our model as a probabilistic model which incorporates neural networks to model the dependency between latent variables and instances. For evaluation, the proposed method is applied to both synthetic and real data, including two labelling tasks: text classification and textual entailment. Experimental results demonstrate our novel method can not only accurately estimate the reliability of annotators across different instances, but also achieve superior performance in predicting the correct labels and detecting the least reliable annotators compared to state-of-the-art baselines.Comment: 9 pages, 1 figures, 10 tables, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL2019