Dimensionality Reduction for Machine Learning-based Argument Mining (2023)

Abstract: Recent approaches to argument mining have focused on training machine learning algorithms from annotated text corpora, utilizing as input high-dimensional linguistic feature vectors. Differently to previous work, in this paper, we preliminarily investigate the potential benefits of reducing the dimensionality of the input data. Through an empirical study, testing SVD, PCA and LDA techniques on a new argumentative corpus in Spanish for an underexplored domain (e-participation), and using a novel, rich argument model, we show positive results in terms of both computation efficiency and argumentative information extraction effectiveness, for the three major argument mining tasks: argumentative fragment detection, argument component classification, and argumentative relation recognition. On a space with dimension around 3-4% of the number of input features, the argument mining methods are able to reach 95-97% of the performance achieved by using the entire corpus, and even surpass it in some cases.

## introduction since its origins in the late 2000s, the argument mining (am) field has witnessed significant advances on the problem of automatically extracting structured argumentative information from text corpora @xcite ), which commonly entails three tasks: the identification of argumentative fragments in an input text, the split or classification of such fragments into argument components (e.g., claims and premises), and the recognition of relations (e.g., support and attack) between pairs of argument components. in particular, previous research has led to the development of effective approaches based on machine learning (ml) @xcite with results almost equal to those obtained with more complex approaches, such as those based on deep learning. hence, argumentative fragment detection @xcite @xcite , argument component classification @xcite , and argument relation recognition @xcite have been modeled as sequence labeling problems, where, in general, each sentence foot_0 is represented as a vector of real-valued linguistic features and has associated certain label or class, e.g., argumentative vs. non-argumentative, and claim vs. premise. ml algorithms are thus trained with sets of labeled sentence vectors in order to predict the class of new sentences. in this context, a variety of features have been considered -ranging from lexical and morphological, to structural and syntactic, and semantic and discourse features @xcite @xcite -and, in general, approaches have dealt with feature vectors of high dimensionality. to the best of our knowledge, only a few research attempts have been made to use a subset of features @xcite . motivated by this fact and the increasing need for more efficient (i.e., less resource-consuming) am model building, in this paper, instead of exploring new argument-related classification algorithms, we investigate the potential benefits of reducing the dimensionality of the input data space. as an innovative research in the am field, we report experiments conducted with the well known svd @xcite , pca @xcite and lda @xcite dimensionality reduction techniques on a novel corpus in spanish with electronic (online) citizen participation discussions, which represent an underexplored domain in the field. considering a rich argument model with several argument relations, and addressing the argumenta-tive fragment detection, argument component classification, and argument relation recognition tasks, we evaluate a number of ml algorithms trained with labeled data without and with dimensionality reduction, achieving favorable results in terms of both computation efficiency and argumentative information extraction effectiveness. with around 3-4% of the number of input features, the argument mining methods are able to reach 95-97% of the performance achieved by using the entire corpus, and even surpass it in some cases. we thus claim the following contributions of our ongoing work: moreover, we make the generated argument model, corpus, annotation tool, software code, and empirical results publicly available foot_2 . before presenting our experiments (section 5) and conclusions (sections 6 and 7), we next survey related work on feature-based machine learning am (section 2), and describe the addressed case study and created corpus (section 3) and the used dimensionality reduction techniques (section 4). ## related work in this section, we survey previous work on applying feature-based machine learning for am. we discard deep learning approaches, since they are appropriate to very large amounts of input data foot_3 . feature-based ml methods model am tasks as sequence labeling problems. they have been pro-posed to separately address the argumentative fragment detection @xcite @xcite @xcite , argument component classification @xcite @xcite , and argument relation recognition @xcite tasks. the surveyed methods consider the sentence as the argument unit, and exploit its linguistic features to classify it. only @xcite also exploited feature information from the previous and next sentences to the target sentence. in all cases, however, the used features are manifold, as we will detail in section 4.1. from our survey, only @xcite addressed the argument relation recognition task. this is not the case in recent word embedding-based deep learning methods, which deal with the three tasks as sequence tagging problems, by commonly following the bio tagging format, e.g., @xcite . with respect to the used ml algorithms, published work has focused on logistic regression @xcite , naive bayes @xcite @xcite , maximum entropy @xcite , decision trees (burhan ud din tahir, 2017; @xcite , random forests @xcite @xcite , and support vector machines @xcite @xcite @xcite @xcite . additionally, as done in deep learning works, the surveyed papers have focused on the traditional domains and corpora of the am field, such as the persuasive student essays corpus @xcite @xcite , the legal texts echr @xcite @xcite and araucariadb @xcite corpora, and the web discourse corpus @xcite , which are mostly in english. to the best of our knowledge, in the am research literature, only @xcite and @xcite have explored feature selection using information gain, reducing the input feature vector space. in this context, a traditional pre-learning di-mensionality reduction approach, such as the ones we evaluate here, has not been explored yet. differently from @xcite @xcite , we do not only focus on classifying a sentence as argumentative or non-argumentative, but also aim to classify the type of argumentative component of a text span, i.e., a premise or a claim, and to recognize the existence of a relation between a pair of components and its type. finally, motivated by the need for addressing other domains and dealing with corpora in languages distinct to english, in our work we explore a novel domain in am and provide a new corpus in spanish, which are described next. ## case study in this section, we introduce the case study for which we have built our argumentative corpus and have implemented and evaluated the machine learning-based argument mining methods with and without dimensionality reduction techniques. citizen participation refers to the active involvement of citizens in influencing on public opinion and being part of democratic decision and policy making processes. it represents one of the most widespread forms of open government, and historically has been conducted through physical interactions like assemblies, meetings and working groups @xcite . nowadays, under the umbrella of eparticipation @xcite , it often occurs on the internet, via online digital tools, in which citizens' opinions and contributions are easily shared, thus generating new opportunities for communication, consultation and collaboration at a large scale @xcite . the majority of current e-participation platforms are based on web forums where citizens upload comments, forming large conversation threads. this makes the processing of the underlying debates challenging and sometimes overwhelming. without functionalities to support critical thinking and argumentation, it is usually very difficult and time-consuming for users to achieve a well-formed view of existing problems and proposed solutions. in our work, we focus our attention on one of such platforms, decide madrid foot_4 , which is an online web portal created by the city council of madrid (spain) to support its annual participatory budgets since september 2015. every year, the city residents use the platform to freely post proposals to address issues and problems in the city, and comment and vote others' proposals. those citizen proposals that receive a certain number of votes and are technically and economically feasible are funded and implemented by the city government. in 2021-2022, the municipal budget allocated to such proposals has been 50 million euro. figures 1 and 2 show an example of a citizen proposal and its comment threads in decide madrid. both proposal descriptions and comments are rich on argumentative information, which may be very valuable for citizens and government stakeholders, and which we aim to extract in our work. for this purpose, we consider the decide madrid dataset used by @xcite , which contains information about 21,744 citizen proposals --classified into 30 categories and 325 topics, geolocated in 21 city districts, and annotated with controversy scores--, and 62,838 comments. from this dataset, we selected the 40 most controversial proposals, and collaboratively searched for and annotated the arguments given by citizens in the proposals descriptions and comments, generating a first version of a corpus that we make publicly available 5 . to ease the manual argument annotation process, and store the identified arguments in a formal, structured format -including the components and relations of the arguments-, we used argael (segura-tinoco and cantador, 2023), an easy-to-use, configurable desktop tool that we have developed to assist with the argument annotation and evaluation task, and which can be freely downloaded 6 . our corpus is composed of 3,254 propositions annotated with 922 claims and 538 premises interconnected, and to the best of our knowledge, ours is one of the first argumentative corpora in spanish in the am field. the underlying argument model is configured in argael and, going beyond the traditional support and attack argumentative links, it includes the following categories of relations between argument components (claims c and premises p): this taxonomy is a compendium of relations used in the am literature @xcite @xcite @xcite , and represent a fine-grained representation of argumentative structures, which entails addressing the argument relation recognition as a multi-class classification problem. specifically, in our corpus, we annotated 538 argument relations distributed by category as: 77 relations belonging to the cause category, 64 to clarification, 76 to consequence, 120 to contrast, and 201 to elaboration. figure 4 shows additional details about the number of argument relations by subcategory in the corpus. ## dimensionality reduction in this section, we introduce the linguistic features of the vector representations exploited by the evaluated ml models to am, and the vector dimensionality reduction techniques applied before building such models. ## experiments in this section, we describe the methodology used to evaluate a number of machine learning models for the three target am tasks (i.e., argumentative fragment detection, argument component classification and argument relation recognition), and report their performance results with and without dimensionality reduction of the input data. ## conclusions although the conducted experiments can be considered preliminary, they have shown promising results about the potential benefits of selecting informative linguistic features and reducing dimensionality in ml-based approaches to am. for the three major am tasks (i.e., argumentative fragment detection, argument component classification, and argument relation recognition), and for almost all the ml algorithms used in the am literature, working on feature spaces of much lower dimensionality generated by svd and pca has entailed not only improvements in training efficiency, but also consistent classification performance of the algorithms, especially logistic regression and support vector machines. in addition to these issues, our work contributes to the am field through the publication of a new argumentative corpus in spanish on e-participation, a novel and relevant domain for the am community. we plan to increase the size and quality of the corpus, and hope it will be of interest for researchers and practitioners. regardless of the impact of dimensionality reduction, the developed am methods and their performance results on our corpus could be of reference for future improvements. moreover, we believe that the corpus may be exploited in different argumentative scenarios. in particular, it could be used to extract argumentative threads from online political discussions @xcite and parliamentary debates, whose transcripts are available as open government datasets.