Unit Segmentation of Argumentative Texts

Abstract

The segmentation of an argumentative text into argument units and their nonargumentative counterparts is the first step in identifying the argumentative structure of the text. Despite its importance for argument mining, unit segmentation has been approached only sporadically so far. This paper studies the major parameters of unit segmentation systematically. We explore the effectiveness of various features, when capturing words separately, along with their neighbors, or even along with the entire text. Each such context is reflected by one machine learning model that we evaluate within and across three domains of texts. Among the models, our new deep learning approach capturing the entire text turns out best within all domains, with an F-score of up to 88.54. While structural features generalize best across domains, the domain transfer remains hard, which points to major challenges of unit segmentation.

Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation

“…A (B) label denotes that the token is at the beginning of an argumentative unit, an (I) label that it lies inside a unit and an (O) label that the token is not part of a unit. This framework has been applied previously for the same task (Stab, 2017;Eger et al, 2017;Ajjour et al, 2017). The architectures proposed in this section build on Ajjour et al (2017), omitting the second Bi-LSTM, which was used to process features other than word embeddings (see section 3.1).…”

“…For example, Eger et al (2017) reported different Long short-term memory (LSTM) (Hochreiter and Schmidhuber, 1997) architectures. Further, Ajjour et al (2017) proposed a setup with three bidirectional LSTMs (Bi-LSTMs) (Schuster and Paliwal, 1997) in total as their best solution.…”

“…In order to evaluate the different architectures, the "Argument annotated Essays (version 2)" corpus (also referred to as Persuasive Essays corpus) is used (Stab and Gurevych, 2017). It was utilized for the same task in previous literature (Ajjour et al, 2017;Eger et al, 2017). The corpus, compiled for parsing argumentative structures in written text, consists of a random sample of 402 student essays.…”

“…One crucial part of the AM pipeline is to segment written text into argumentative and nonargumentative units. Recent research in the area of unit segmentation (Eger et al, 2017;Ajjour et al, 2017) has lead to promising results with F1-scores of up to 0.90 for in-domain segmentation (Eger et al, 2017). Nevertheless, there is still a need for more robust approaches.…”

Segmentation of Argumentative Texts with Contextualised Word Representations

“…as a second step, or as a multi-class one, where identification of argumentative units and their classification into claims and premises are performed as a single step. Typically the granularity of this task is coarse, with most approaches considering sentences as the smallest argumentative unit (Florou et al, 2013;Moens et al, 2007;Song et al, 2014;Swanson et al, 2015), although some works focused on the most difficult task of detecting units at the clause level (Park and Cardie, 2014;Goudas et al, 2014Goudas et al, , 2015Sardianos et al, 2015;Stab, 2017;Ajjour et al, 2017;Eger et al, 2017). According to a recent survey (Lippi and Torroni, 2015a), the performance of proposed approaches depends on highly engineered and sophisticated, manually constructed, features.…”