Document level semantic orientation and argumentation
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Document-level Semantic Orientation and Argumentation. Presented by Marta Tatu CS7301 March 15, 2005.  or ? Semantic Orientation Applied to Unsupervised Classification of Reviews. Peter D. Turney ACL-2002. Overview.

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Document level semantic orientation and argumentation

Document-level Semantic Orientation and Argumentation

Presented by Marta Tatu


March 15, 2005

Or semantic orientation applied to unsupervised classification of reviews

 or ? Semantic Orientation Applied to Unsupervised Classification of Reviews

Peter D. Turney




  • Unsupervised learning algorithm for classifying reviews as recommended or not recommended

  • The classification is based on the semantic orientation of the phrases in the review which contain adjectives and adverbs



Input: review

  • Identify phrases that contain adjectives or adverbsby using a part-of-speech tagger

  • Estimate the semantic orientation of each phrase

  • Assign a class to the given review based on the average semantic orientation of its phrases

    Output: classification ( or )

Step 1

Step 1

  • Apply Brill’s part-of-speech tagger on the review

  • Adjective are good indicators of subjective sentences. In isolation:

    • unpredictable steering () / plot ()

  • Extract two consecutive words: one is an adjective or adverb, the other provides the context

Step 2

Step 2

  • Estimate the semantic orientation of the extracted phrases using PMI-IR (Turney, 2001)

  • Pointwise Mutual Information (Church and Hanks, 1989):

  • Semantic Orientation:

  • PMI-IR estimates PMI by issuing queries to a search engine (Altavista, ~350 million pages)

Step 2 continued

Step 2 – continued

  • Added 0.01 to hits to avoid division by zero

  • If hits(phrase NEAR “excellent”) and hits(phrase NEAR “poor”)≤4, then eliminate phrase

  • Added “AND (NOT host:epinions)” to the queries not to include the Epinions website

Step 3

Step 3

  • Calculate the averagesemantic orientation of the phrases in the given review

  • If the average is positive, then 

  • If the average is negative, then 



  • 410 reviews from Epinions

    • 170 (41%) ()

    • 240 (59%) ()

    • Average phrases per review: 26

  • Baseline accuracy: 59%



  • What makes the movies hard to classify?

    • The average SO tends to classify a recommended movies as not recommended

    • Evil characters make good movies

    • The whole is not necessarily the sum of the parts

  • Good beaches do not necessarily add up to a good vacation

  • But good automobile parts usually add up to a good automobile



  • Summary statistics for search engines

  • Summarization of reviews

    • Pick out the sentence with the highest positive/negative semantic orientation given a positive/negative review

  • Filtering “flames” for newsgroups

    • When the semantic orientation drops below a threshold, the message might be a potential flame


Questions ?

  • Comments ?

  • Observations ?

Sentiment classification using machine learning techniques

? Sentiment Classification using Machine Learning Techniques

Bo Pang, Lillian Lee and Shivakumar Vaithyanathan




  • Consider the problem of classifying documents by overall sentiment

  • Three machine learning methods besides the human-generated lists of words

    • Naïve Bayes

    • Maximum Entropy

    • Support Vector Machines

Experimental data

Experimental Data

  • Movie-review domain

  • Source: Internet Movie Database (IMDb)

  • Stars or numerical value ratings converted into positive, negative, or neutral » no need to hand label the data for training or testing

  • Maximum of 20 reviews/author/sentiment category

    • 752 negative reviews

    • 1301 positive reviews

    • 144 reviewers

List of words baseline

List of Words Baseline

  • Maybe there are certain words that people tend to use to express strong sentiments

  • Classification done by counting the number of positive and negative words in the document

  • Random-choice baseline: 50%

Machine learning methods

Machine Learning Methods

  • Bag-of-features framework:

    • {f1,…,fm} predefined set of m features

    • ni(d) = number of times fi occurs in document d

  • (Naïve Bayes)

Machine learning methods continued

Machine Learning Methods – continued

  • (Maximum Entropy)

    where Fi,c is a feature/class function:

  • Support vector machines: Find hyperplane that maximizes the margin. The constraint optimization problem:

    • cj is the correct class of document dj



  • 700 positive-sentiment and 700 negative-sentiment documents

  • 3 equal-sized folds

  • The tag “NOT_” was added to every word between a negation word (“not”, “isn’t”, “didn’t”) and the first punctuation mark

    • “good” is opposite to “not very good”

  • Features:

    • 16,165 unigrams appearing at least 4 times in the 1400-document corpus

    • 16,165 most often occurring bigrams in the same data



  • POS information added to differentiate between: “I love this movie” and “This is a love story”



  • Results produced by the machine learning techniques are better than the human-generated baselines

    • SVMs tend to do the best

    • Unigram presence information is the most effective

  • Frequency vs. presence: “thwarted expectation”, many words indicative of the opposite sentiment to that of the entire review

  • Some form of discourse analysis is necessary


Questions ?

  • Comments ?

  • Observations ?

Summarizing scientific articles experiments with relevance and rhetorical status

Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status

Simone Teufel and Marc Moens




  • Summarization of scientific articles: restore the discourse context of extracted material by adding the rhetorical status of each sentence in the document

  • Gold standard data for summaries consisting of computational linguistics articles annotated with the rhetorical status and relevance for each sentence

  • Supervised learning algorithm which classifies sentences into 7 rhetorical categories

Document level semantic orientation and argumentation


  • Knowledge about the rhetorical status of the sentence enables the tailoring of the summaries according to user’s expertise and task

    • Nonexpert summary: background information and the general purpose of the paper

    • Expert summary: no background, instead differences between this approach and similar ones

  • Contrasts or complementarity among articles can be expressed

Rhetorical status

Rhetorical Status

  • Generalizations about the nature of scientific texts + information to enable the construction of better summaries

  • Problem structure: problems (research goals), solutions (methods), and results

  • Intellectual attribution: what the new contribution is, as opposed to previous work and background (generally accepted statements)

  • Scientific argumentation

  • Attitude toward other people’s work: rival approach, prior approach with a fault, or an approach contributing parts of the authors’ own solution

Metadiscourse and agentivity

Metadiscourse and Agentivity

  • Metadiscourse is an aspect of scientific argumentation and a way of expressing attitude toward previous work

    • “we argue that”, “in contrast to common belief, we”

  • Agent roles in argumentation: rivals, contributors of part of the solution (they), the entire research community, or the authors of the paper (we)

Citations and relatedness

Citations and Relatedness

  • Just knowing that an article cites another is often not enough

  • One needs to read the context of the citation to understand the relation between the articles

    • Article cited negatively or contrastively

    • Article cited positively or in which the authors state that their own work originates from the cited work

Rhetorical annotation scheme

Rhetorical Annotation Scheme

  • Only one category assigned to each full sentence

  • Nonoverlapping, nonhierarchical scheme

  • The rhetorical status is determined on the basis of the global context of the paper



  • Select important content from text

  • Highly subjective » low human agreement

  • Sentence is considered relevant if it describes the research goal or states a difference with a rival approach

  • Other definitions: relevant sentence if it shows a high level of similarity with a sentence in the abstract



  • 80 conference articles

    • Association for Computational Linguistics (ACL)

    • European Chapter of the Association for Computational Linguistics (EACL)

    • Applied Natural Language Processing (ANLP)

    • International Joint Conference on Artificial Intelligence (IJCAI)

    • International Conference on Computational Linguistics (COLING).

  • XML markups added

The gold standard

The Gold Standard

  • 3 tasked-trained annotators

  • 17 pages of guidelines

  • 20 hours of training

  • No communication between annotators

  • Evaluation measures of the annotation:

    • Stability

    • Reproducibility

Results of annotation

Results of Annotation

  • Kappa coefficient K(Siegel and Castellan, 1988)

    where P(A)= pairwise agreement and P(E)= random agreement

  • Stability: K=.82, .81, .76(N=1,220 and k=2)

  • Reproducibility: K=.71

The system

The System

  • Supervised machine learning Naïve Bayes



  • Absolute location of a sentence

    • Limitations of the author’s own method can be expected to be found toward the end, while limitations of other researchers’ work are discussed in the introduction

Features continued

Features – continued

  • Section structure: relative and absolute position of sentence within section:

    • First, last, second or third, second-last or third-last, or either somewhere in the first, second, or last third of the section

  • Paragraph structure: relative position of sentence within a paragraph

    • Initial, medial, or final

Features continued1

Features – continued

  • Headlines: type of headline of current section

    • Introduction, Implementation, Example, Conclusion, Result, Evaluation, Solution, Experiment, Discussion, Method, Problems, Related Work, Data, Further Work, Problem Statement, or Non-Prototypical

  • Sentence length

    • Longer or shorter than 12 words (threshold)

Features continued2

Features – continued

  • Title word contents: does the sentence contain words also occurring in the title?

  • TF*IDF word contents

    • High values to words that occur frequently in one document, but rarely in the overall collection of documents

    • Do the 18 highest-scoring TF*IDF words belong to the sentence?

  • Verb syntax: voice, tense, and modal linguistic features

Features continued3

Features – continued

  • Citation

    • Citation (self), citation (other), author name, or none + location of the citation in the sentence (beginning, middle, or end)

  • History: most probable previous category

    • AIM tends to follow CONTRAST

    • Calculated as a second pass process during training

Features continued4

Features – continued

  • Formulaic expressions: list of phrases described by regular expressions, divided into 18 classes, comprising a total of 644 patterns

    • Clustering prevents data sparseness

Features continued5

Features – continued

  • Agent: 13 types, 167 patterns

    • The placeholder WORK_NOUN can be replaced by a set of 37 nouns including theory, method, prototype, algorithm

    • Agent classes with a distribution very similar with the overall distribution of target categories were excluded

Features continued6

Features – continued

  • Action: 365 verbs clustered into 20 classes based on semantic concepts such as similarity, contrast

    • PRESENTATION_ACTIONs: present, report, state

    • RESEARCH_ACTIONs: analyze, conduct, define, and observe

    • Negation is considered

System evaluation

System Evaluation

  • 10-fold-cross-validation

Feature impact

Feature Impact

  • The most distinctive single feature is Location, followed by SegAgent, Citations, Headlines, Agent and Formulaic


Questions ?

  • Comments ?

  • Observations ?

Thank you

Thank You !

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