Combining Argument Mining Techniques
J. Lawrence and C. Reed, "Combining argument mining techniques," in Proceedings of the 2nd
Workshop on Argumentation Mining, 2015, pp. 127-136.
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Combining Argument Mining Techniques
•Argument Mining is the automatic identification of the argumentative structure contained within apiece of
natural language text.
•Purpose: improving the argument structure identification
•Method: Combining three different methods
•Data Base: AIFdb
• AIFdb: Infrastructure for the Argument Web
• http://www.aifdb.org/search
RA: Related Argument
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http://www.aifdb.org
The
databases
here
include
examples from a number of popular
tools including:
Rationale, Carneades and Araucaria,
along with additional arguments
analysed and produced as a part of
the EPSRC-funded Dialectical
Argumentation Machines project.
B yi kopyalamak yasaktir
A yi kopyalamak yasaktir
A=B
https://arg-tech.org/index.php/infrastructure-for-the-argument-web-released/
J. Lawrence, F. Bex, C. Reed, and M. Snaith, "AIFdb: Infrastructure for the Argument Web," in COMMA, 2012, pp. 515-516.
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Three Individual Argument Mining
Approaches
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1. Discourse Indicators
 Firstly, we look at using the presence of discourse indicators,
linguistic expressions of the relationship between statements, to
determine relationships between the propositions in a piece of
text.
 Discourse indicator tells us that two propositions are connected,
and that the relation between them (support or attack).
The issue is:
◦
They limit their search here to specific terms appearing between two sequential
propositions the original text (Table 1).
◦
While, there are another Discourse Indicators such as: Similar indicators (e.g. …)
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2. Topical Similarity
They look at the semantic similarity of propositions
◦ They use WordNet to determine the similarity between the synsets
of each word in the first proposition and each word in the second.
◦ Relatedness score for the proposition pair between 0 and 1
◦ The results of performing this process using a threshold of 0.2
WORDNET
WordNet is a large lexical database of English words.
Nouns, verbs, adjectives, and adverbs are grouped into sets of cognitive
synonyms called ‘synsets’, each expressing a distinct concept.
Notice:
•This method is not suitable for our project. Because the
sentences are long. It will have a very high time complexity
•NP-hard problem
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https://towardsdatascience.com/%EF%B8%8Fwordnet-a-lexical-taxonomy-of-english-words-4373b541cfff
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3. Argumentation Scheme Structure
Finally, we consider using a supervised machine learning
approach based on argumentation schemes (Walton et al.,
2008), to classify argument components and determine the
connections between them.
One of the first attempts to use this kind of classification is
presented in (Moens et al., 2007), where a
1. text is first to split into sentences
2. and then features of each sentence are used to classify
them as “Argument” or“Non-Argument”.
This classification was performed with a Na¨ive Bayes
classifier implemented using
Precision, recall and F-score
2022
D. Walton and22F.Mart
Macagno,
"A classification system for argumentation schemes," Argument & Computation, vol. 6, no. 3, pp. 219-245, 2015.
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Combined Techniques
The results of applying each approach separately are given in the first part of Table 8.
Based on these results, they combine the methods as follows:
Precision, Recall and F-score
 firstly, if discourse indicators are present, then they are
assumed to be a correct indication of a connection;
 next, we identify scheme instances and connect the
component parts in accordance with the scheme structure;
 finally, we look at the topic similarity and use this to
connect any propositions that have previously been left out
of the al ready identified structure.
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Identifying Persian Words’ Senses Automatically by
Utilizing the Word Embedding Method
M. Ghayoomi, "Identifying Persian Words’ Senses Automatically by Utilizing the Word
Embedding Method," Iranian Journal of Information processing and Management, vol. 35, no. 1,
pp. 25-50, 2019.
Amaç:
◦ Bir cümledeki bir kelimenin anlamı nedir?
◦ Eşanlamlı kelimelerin sayısı nedir?
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Argument 1
?
Argument 2
Argument 3
Argument 4
.
.
.
Argument n
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Hibrit Yöntemi nedir?
Veri Hazırlama
VERİ
Kümeleme
Kümeleme
SONUÇ
VERİ
Kümeleme
Sınıflandırma
SONUÇ
VERİ
Sınıflandırma
Kümeleme
SONUÇ
VERİ
Sınıflandırma
Sınıflandırma
SONUÇ
Kümeleme
Sınıflandırma
Tsai, C.-F., & Chen, M.-L. (2010). Credit rating by hybrid machine learning techniques. Applied soft computing, 10(2), 374-380.
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Veri Hazırlama:
Vectorization:
1.
Term frequency
Words within a sentence are weighted (TF_IDF Method)
How Construct
is TF-IDF calculated?
2.
a vector for each word (Neural Network method)
• The TF-IDF is the product of two statistics, term frequency and inverse document frequency.
3. There
Creating
texture
forfor
text
(Sentence-based
Context-based)(Feature
selection)
are various
ways
determining
the exact/values
of both statistics.
• A formula that aims to define the importance of a keyword or phrase within a document or a
4. web
Using
the above step, the weighted average of the target words will be calculated.
page.
W3
W4
• So, if the word is very common andW1
appears in W2
many documents,
this number
will approach 0. Inverse document frequency
W1
W2
W3
W4
W1
W2
W1
W1
W3
W2
W2
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W3
W4
W5
W3
W4
W6
W4
W5
W6
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Data clustering:
• Argument 3
Argument 1
Argument 2
• Argument 4
• Argument 1
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Argument 3
.
.
.
Argument n
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EN iyi küme sayısı:
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Ensemble learning
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Ampersand: Argument mining for
persuasive online discussions
T. Chakrabarty, C. Hidey, S. Muresan, K. McKeown, and A. Hwang, "Ampersand: Argument mining
for persuasive online discussions," arXiv preprint arXiv:2004.14677, 2020.
They propose a computational model for argument mining in online persuasive discussion
forums that brings together the micro-level (argument as product) and macro-level (argument
as process) models of argumentation.
We additionally propose a candidate selection method to automatically pre-dict what parts of
one’s argument will be tar-geted by other participants in the discussion.
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