Context-Independent Claim Detection for Argument Mining
Authors: Marco Lippi, Paolo Torroni
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5 we present experiments and results. We built an SVM classifier that exploits a kernel on the constituency parse trees obtained using the Stanford Core NLP 3.5.0 software4. Table 1: Results obtained on the IBM Wikipedia corpus. |
| Researcher Affiliation | Academia | Marco Lippi and Paolo Torroni DISI Universit a degli Studi di Bologna {marco.lippi3,p.torroni}@unibo.it |
| Pseudocode | No | The paper describes its methodology in prose and through figures, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | Dataset and results are available on the following website: http://lia.disi.unibo.it/ ml/argumentationmining/ijcai2015.html. (The website content states: 'The SVM classifier which implements the Tree Kernel is available upon request (the Partial Tree Kernel is part of the SVM-Light-TK software distribution, available here).') |
| Open Datasets | Yes | A smaller publicly available data set consists in a collection of 90 persuasive essays [Stab and Gurevych, 2014a] on a variety of different topics, where a team of annotators provided information regarding claims, major claims, premises (a synonym for evidence) and attack/support relations between such components. |
| Dataset Splits | No | The paper describes a 'leave-one-topic-out procedure' and '10-fold cross validation' for evaluation, which implies training and testing, but does not explicitly provide percentages or counts for distinct training, validation, and test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | Yes | We built an SVM classifier that exploits a kernel on the constituency parse trees obtained using the Stanford Core NLP 3.5.0 software4. |
| Experiment Setup | Yes | To summarize, the methodology we propose consists of the following claim detection pipeline: 1. the given text document is split into sentences using a tokenizer that detects sentence boundaries; 2. each sentence is parsed, to obtain a constituency tree; 3. sentences not containing a verb (VP tag) are discarded; 4. in order to improve generalization, words at leaves are substituted with their stemmed versions; 5. an SVM classifies each sentence as possibly containing a claim or not. |