Articles

s for Evidence Based Medicine Hamed Hassanzadeh† Diego Moll´a♦ Tudor Groza‡ Anthony Nguyen♮ Jane Hunter† †School of ITEE, The University of Queensland, Brisbane, QLD, Australia ♦Department of Computing, Macquarie University, Sydney, NSW, Australia ‡Garvan Institute of Medical Research, Darlinghurst, NSW, Australia ♮The Australian e-Health Research Centre, CSIRO, Brisbane, QLD, Australia …...

This study is a pilot research that explores the effectiveness of a likelihood ratio (LR)-based forensic voice comparison (FVC) system built on non-native speech production. More spe- cifically, it looks at native Hong Kong Can- tonese-speaking male productions of English vowels, and the extent to which FVC can work on …...

Relation extraction is the task of ex- tracting predicate-argument relationships between entities from natural language text. This paper investigates whether back- ground information about entities avail- able in knowledge bases such as FreeBase can be used to improve the accuracy of a state-of-the-art relation extraction sys- tem. We describe a …...

This study investigates the use of unsuper- vised features derived from word embed- ding approaches and novel sequence rep- resentation approaches for improving clin- ical information extraction systems. Our results corroborate previous findings that indicate that the use of word embeddings significantly improve the effectiveness of concept extraction models; however, …...

Language data for the Tesseract OCR system currently supports recognition of a number of languages written in Indic writing scripts. An initial study is de- scribed to create comparable data for Tesseract training and evaluation based on two approaches to character segmen- tation of Indic scripts; logical vs. visual. Results …...

In this paper we use a deep auto-encoder for extractive query-based summarization. We experiment with different input repre- sentations in order to overcome the prob- lems stemming from sparse inputs charac- teristic to linguistic data. In particular, we propose constructing a local vocabulary for each document and adding a small …...

In this paper, we look at three different meth- ods of extracting the argumentative structure from a piece of natural language text. These methods cover linguistic features, changes in the topic being discussed and a supervised machine learning approach to identify the components of argumentation schemes, pat- terns of human …...

Online debates sparkle argumentative discus- sions from which generally accepted argu- ments often emerge. We consider the task of unsupervised identification of prominent argu- ment in online debates. As a first step, in this paper we perform a cluster analysis using se- mantic textual similarity to detect similar argu- ments. …...

We investigate the characteristics of factual and emotional argumentation styles observed in online debates. Using an annotated set of FACTUAL and FEELING debate forum posts, we extract patterns that are highly correlated with factual and emotional arguments, and then apply a bootstrapping methodology to find new patterns in a larger …...

We propose a sentence ordering method to help compose persuasive opinions for debat- ing. In debate texts, support of an opinion such as evidence and reason typically follows the main claim. We focused on this claim- support structure to order sentences, and de- veloped a two-step method. First, we select …...

Argumentation mining obviously involves finding support relations between statements, but many interesting instances of argumen- tation also contain counter-considerations, which the author mentions in order to preempt possible objections by the readers. A counter- consideration in monologue text thus involves a switch of perspective toward an imagi- nary opponent. We …...

The main goal of argumentation mining is to analyze argumentative structures within an argument-rich document, and reason about their composition. Recently, there is also in- terest in the task of simply detecting claims (sometimes called conclusion) in general doc- uments. In this work we ask how this set of detected …...

The paper discusses the architecture and de- velopment of an Argument Workbench, which is a interactive, integrated, modular tool set to extract, reconstruct, and visualise arguments. We consider a corpora with dispersed infor- mation across texts, making it essential to con- ceptually search for argument elements, top- ics, and terminology. …...

Argumentation mining and stance classifica- tion were recently introduced as interesting tasks in text mining. In this paper, a novel framework for argument tagging based on topic modeling is proposed. Unlike other ma- chine learning approaches for argument tag- ging which often require large set of labeled data, the proposed …...

Argument extraction is the task of identifying arguments, along with their components in text. Arguments can be usually decomposed into a claim and one or more premises justifying it. The proposed approach tries to identify seg- ments that represent argument elements (claims and premises) on social Web texts (mainly news …...

Automatic generation of arguments is an im- portant task that can be useful for many ap- plications. For instance, the ability to gener- ate coherent arguments during a debate can be useful when determining strengths of support- ing evidence. However, with limited technolo- gies that automatically generate arguments, the development …...

Park and Cardie (2014) proposed a novel task of automatically identifying appropriate types of support for propositions comprising online user comments, as an essential step toward au- tomated analysis of the adequacy of support- ing information. While multiclass Support Vector Machines (SVMs) proved to work rea- sonably well, they do …...

This paper proposes a shared task for the identification of the argumentative structure in newspaper editorials. By the term “argumen- tative structure” we refer to the sequence of ar- gumentative units in the text along with the re- lations between them. The main contribution is a large-scale dataset with more …...

We advocate a relation based approach to Ar- gumentation Mining. Our focus lies on the extraction of argumentative relations instead of the identification of arguments, themselves. By classifying pairs of sentences according to the relation that holds between them we are able to identify sentences that may be factual when …...

Argument mining studies in natural language text often use lexical (e.g. n-grams) and syn- tactic (e.g. grammatical production rules) fea- tures with all possible values. In prior work on a corpus of academic essays, we demonstrated that such large and sparse feature spaces can cause difficulty for feature selection and …...