The document discusses argument extraction from news, blogs, and social media. It describes argument extraction as identifying claims and premises from text. This is a difficult task, even for humans. Existing methods for argument extraction are reviewed, including rule-based and machine learning approaches. A new two-step method is proposed using supervised classification and conditional random fields to first identify argumentative sentences and then extract claims and premises. Evaluation results show the proposed approach outperforms a simple baseline method.

1. Argument Extraction from News,
Blogs,and Social Media.
Theodosis Goudas, Christos Louizos, Georgios Petasis, Vangelis Karkaletsis.
Presented by :
Sharath T.S
Shubhangi Tandon

2. What is Argument Extraction?
An argument can be usually decomposed into a claim and one or more premises justifying it.
Task of identifying arguments along with their components in text
Difficult even for humans to distinguish whether a part of a sentence contains an argument element or not

3. Why social media?
● Most widely used and accessible platform available to seek advice or express opinion
● Is a storehouse of both meaningful and meaningless information on social media about recent trends and
topics.
● Almost no prior research in this field; only one publication related to product reviews on Amazon.

4. Why is this difficult ?
● Almost no prior research in this field; only one publication related to product reviews on Amazon!
● Text from social media may not always contain arguments
● Expressed in an informal form, and they do not follow any formal guidelines or specific rules
● Absence of widely used corpora in order to comparably evaluate approaches for argument extraction.
● Traditional research in the area, concentrates mainly on law documents and scientific publications.

5. Existing methods● Palau et al. [4,7]
○ Classification at the sentence level by trying to identify possible argumentative sentences. Using NB, SVM, maximum
entropy.
○ Identify groups of sentences that refer to the same argument, using semantic distance based on the relatedness of
words contained
○ Detect clauses of sentences through a parsing tool, which are classified as argumentative or not with a maximum
entropy classifier
○ Argumentative clauses are classified into premises and claims through support vector machines
○ Araucaria corpus and ECHR corpus [11], achieving an accuracy of 73% and 80%
● A rule based system - Input an argumentation scheme and an ontology concerning an object, for example, a camera and its
characteristic features. Argumentation schemes are populated with discourse indicators, domain specific features and rules
are constructed.

6. Proposed Method
The proposed Automatic Argument Extraction is a two step process :
Step A : Identification of Argumentative Sentences (Supervised Classification using standard classifiers : Logistic
regression, Random Forest, Support Vector Machines, Naive Bayes)
Step B :Extraction of Claims and Premises (From output of Step A , using Conditional Random Fields)

7. Feature Selection for Corpus
State of the art features:
Position Comma Token Number Connective Number Verb Number Word Number
Cue words # verbs in passive voice Domain Entities Number Adverb Number Word Mean Length

8. Feature Selection for Corpus (contd.)
New Domain Specific Features:
Adjective number : Number of adjectives in a sentence .Usually in argumentation opinions are expressed towards an entity/claim,
through adjectives.
Entities in previous sentences: Number of entities in the nth previous sentence. History of n = 5 sentences, obtain five features,
Correlates to the probability that the current sentence contains an argument element.
Cumulative number of entities in previous sentences: Total number of entities from the previous n sentences. Considering a history
of n = 5 we obtain four features.
Ratio of distributions: Two Language models created from sentences that contain argument elements and from sentences that do
not contain an argument element. The ratio between these two distributions based on unigrams, bigrams and trigrams of
words. Can be described as :
Distributions over unigrams, bigrams, trigrams of part of speech tags (POS tags): Identical to [4] with the exception that unigrams,
bigrams and trigrams are extracted from the part of speech tags instead of words.

9. Step B: Argument Extraction with CRFWhy Conditional Random Fields ?
Structured prediction algorithm
Can take local context into consideration ( help maintain linguistic aspects such as the word ordering in the sentence)
Features:
The words in these sentences
Gazetteer lists of known entities for the thematic domain related to the arguments we want to extract,
Gazetteer lists of cue words and indicator phrases
Lexica of verbs and adjectives automatically acquired using Term Frequency - Inverse Document Frequency (TF-IDF) between
two “documents” ( With and without argumentative text from Step A )

10. Corpus Preparation
● 204 documents (in Greek) collected from the social media
● Thematic domain of Renewable Energy Sources
● Selected documents were manually annotated with domain entities and text segments that correspond to argument premises.
● Claims are not represented into documents as segments, but implied by the author as positive or negative views
760 sentences:
Annotated as
containing
arguments
16000 sentences
from 204
documents
Final Output
Ellogon
Step A Step B

11. Evaluation : Base Case
Simple base case classifier:
1. Manually annotated segments (argument components) used to form a gazetteer.
2. Applied on the corpus in order to detect all exact matches of all these segments.
a. All segments identified are marked as argumentative segments
b. All sentences that contain at least one argumentative segment identified by the gazetteer, are characterised as an
argumentative sentence.
3. Argumentative segments/sentences are compared to “gold” counterparts, manually annotated by humans.
a. Sentences that contain these recognized fragments are marked as argumentative for the first step base case.
b. Segments marked as argumentative are evaluated for the second step base case.
4. Results are taken through 10-fold
cross validation on the whole corpus (all 16k sentences)

12. Evaluation : Step A
Each sentence represented as a fixed-size vector using features described (including class - Supervised learning )
Tested against classifiers such as : Support Vector Machines, Naive Bayes, Random Forest and Logistic Regression.
Initial Data set is heavily skewed towards non-argumentative documents . Therefore, Data Sampling and Testing was done in two
different ways :
Use Precision , Recall , F-1 Measure and Accuracy for Evaluation
Logistic Regression and Naive Bayes performed the best
Way #1 Way #2
Sampling Randomly ignore negative examples.
Result set contains equal number of instances
from both classes
Split Initial Data set in the ratio 70:30 for
testing and training
Evaluation 10-fold cross validation, achieved high accuracy Achieved 49% accuracy , Discarded

13. Evaluation : Step B
To use CRF, need BIO tagging for sentences:
B for starting a text segment (premise),
I for a token in a premise other than the first, and
O for all other tokens (outside of the premise segment)
Example for “Wind turbines generate noise in the summer”
Final Result after CRF
Baseline Results

14. What did we think ?

15. Questions/ Observations/Inputs?

16. Appendix

17. Evaluation
Results
Step A

18. Evaluation Results :Step A (contd)
Go back

19. Features continued
○ Verb number : is the number of the verbs inside a sentence, which indicates the number of periods
inside a sentence.
○ Number of verbs in passive voice
○ Cue words: this feature indicates the existence and the number of cue words(also known as discourse
indicators). Cue words are identified through a predefined, manually constructed, lexicon. The cue
words in the lexicon are structural words which indicate the connection between periods or
subordinate clauses. Examples - anyway, by the way, furthermore, first, second, then, now, thus,
moreover, therefore, hence, lastly, finally, in summary, and on the other hand.
○ Domain entities number : this feature indicates the existence and the number of entity mentions of
named-entities relevant to our domain, in the context of a sentence.
○ Adverb number : this feature indicates the number of adverbs in the context of a sentence.
○ Word number : the number of words in the context of a sentence. This feature is based in the

20. Proposed method
● Identification of argumentative sentences is a supervised classification task. Logistic regression, Random
Forest, Support Vector Machines, Naive Bayes are the classifiers used.
● From the sentences classified as argumentative, classify then further into premises and claims using CRF.
● Generic features used -
○ Position: this feature indicates the position of the sentence inside the text.
○ Comma token number, is the number of commas inside a sentence. This feature represents the number
subordinate clauses inside a sentence, based on the idea that sentences containing argument elements
may have a large number of clauses.
○ Connective number : is the number of connectives in the sentence, as connectives usually connect
subordinate clauses. This feature is also selected based on the hypothesis that sentences containing
argument elements may have a large number of clauses.

21. Additional features to aid argument detection in social media
● Adjective number : the number of adjectives in a sentence may characterize a sentence as argumentative or
not. We considered the fact that usually in argumentation opinions are expressed towards an entity/claim,
which are usually expressed through adjectives.
● Entities in previous sentences: this feature represents the number of entities in the n th previous sentence.
Considering a history of n = 5 sentences,we obtain five features, with each one containing the number of
entities in the respective sentence. These features correlate to the probability that the current sentence
contains an argument element.
● Cumulative number of entities in previous sentences: This feature contains the total number of entities from
the previous n sentences. Considering a history of n = 5 we obtain four features, with each one containing
the cumulative number of entities from all the previous sentences.
● Ratio of distributions: we created a language model from sentences that contain argument elements and one
from sentences that do not contain an argument element. The ratio between these two distributions was
used used as a feature. We have created three ratios of language models based on unigrams, bigrams and
trigrams of words. The ratio can be described as P(X|sentence contains an argument element)
P(X|sentence does not contain an argument element), where X ∈ {unigrams, bigrams, trigrams}

22. Related Work
Argumentation is a branch of philosophy that studies the act or process of forming reasons and of drawing
conclusions in the context of a discussion, dialogue, or conversation.
The reasons are called premises and the conclusion is called the claim.

23. Existing methods
● Palau et al. [4,7]
○ Classification at the sentence level by trying to identify possible argumentative sentences. Using NB,
SVM, maximum entropy.
○ identify groups of sentences that refer to the same argument, using semantic distance based on the
relatedness of words contained
○ detect clauses of sentences through a parsing tool, which are classified as argumentative or not with a
maximum entropy classifier
○ Then argumentative clauses are classified into premises and claims through support vector machines
○ Araucaria corpus and ECHR corpus [11], achieving an accuracy of 73% and 80%
● A rule based system - The system is given as input an argumentation scheme and an ontology concerning an object, for
example, a camera and its characteristic features. Argumentation schemes are populated and along with discourse indicators
and other domain specific features, the rules are constructed. An interesting aspect of this work is the fact that they applied

24. Existing methods - Continued
●

25. Existing methods - Continued
● Lawrence et al., 2014 - Proposed a machine learning approach to extract propositions from philosophical text,
with a topic model to determine argument structure, without considering whether a piece of text is part of an
argument. Hence, the machine learning algorithm was used in order to define the boundaries and afterwards
classify each word as the beginning or end of a proposition.
● the first step of their task was to identify sentences containing context dependent claims (CDCs) in each article.
Afterwards they used a classifier in order to find the exact boundaries of the CDCs detected. As a final step, the
ranked each CDC in order to isolate the most relevant to the corresponding topic CDCs. That said, their goal is to
automatically pinpoint CDCs within topic related documents.