Difference between revisions of "DmitryDavidov et al. CoNLL"
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It's '''not a self-contained''' paper, it '''depends on another paper''' heavily. It's '''not a creative work''', and I strongly suggest '''not to recommend''' to future students. | It's '''not a self-contained''' paper, it '''depends on another paper''' heavily. It's '''not a creative work''', and I strongly suggest '''not to recommend''' to future students. | ||
| − | This paper address the [[ | + | This paper address the [[AddressesProblem::Sarcasm Detection]] problem in Twitter and Amazon review posts. They propose to use some [[UsesMethod::semi-supervised learning]] methods to automatically generate patterns, and feed those patterns to some machine learning algorithm to detect sarcasm. However, from this paper, I have no idea how they used the unlabeled text, and they didn't provide any explanation about the classification algorithm they used, i.e. [[UsesMethod::k-Nearest Neighbor]]. |
They experimented on 2 dataset: [[UsesDataset::Twitter Dataset for Sarcasm|Twitter Dataset]] and [[UsesDataset::Amazon Dataset for Sarcasm|Amazon Dataset]] | They experimented on 2 dataset: [[UsesDataset::Twitter Dataset for Sarcasm|Twitter Dataset]] and [[UsesDataset::Amazon Dataset for Sarcasm|Amazon Dataset]] | ||
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== Discussion == | == Discussion == | ||
| − | First of all, I have to say it's '''not a self-contained''' paper, it '''depends on another paper''' heavily and it's '''not a creative work'''. This paper didn't change much from the | + | First of all, I have to say it's '''not a self-contained''' paper, it '''depends on another paper''' heavily and it's '''not a creative work'''. This paper didn't change much from the [[Tsur_et_al_ICWSM_10|AAAI 2010 paper]]. The only thing that this paper did is changed some small setting of previous paper: the algorithm follows [[Tsur_et_al_ICWSM_10|AAAI 2010 paper]], the feature follows the ACL 2006 paper [http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CCYQFjAA&url=http%3A%2F%2Fleibniz.cs.huji.ac.il%2Ftr%2F884.pdf&ei=f4doUN7hOq-O0QGYv4CQCg&usg=AFQjCNHmMVwq0zPYDEhpaScToMm5iVNO0A&sig2=jp-5-01q-OzlAY3AbIhntQ] |
| + | The weak points of the paper includes: | ||
| + | 1. It haven't any significant change to previous methods | ||
| + | 2. It depended on another paper so heavy that the algorithm is not complete without that paper. | ||
| + | 3. It didn't consider any baseline algorithms. For example, they can compare their method to other semi-supervised methods or related sarcasm detection methods. | ||
| − | + | Strong point: | |
| − | 1. It | + | 1. It's among some first attempt to solve this problem using semi-supervised learning method. |
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== Related papers == | == Related papers == | ||
Latest revision as of 14:34, 2 October 2012
Contents
Citation
Semi-supervised recognition of sarcastic sentences in twitter and amazon,
Dmitry Davidov, Oren Tsur and Ari Rappoport, CoNLL 2010
Online version
Semi-supervised recognition of sarcastic sentences in twitter and amazon
Summary
It's not a self-contained paper, it depends on another paper heavily. It's not a creative work, and I strongly suggest not to recommend to future students.
This paper address the Sarcasm Detection problem in Twitter and Amazon review posts. They propose to use some semi-supervised learning methods to automatically generate patterns, and feed those patterns to some machine learning algorithm to detect sarcasm. However, from this paper, I have no idea how they used the unlabeled text, and they didn't provide any explanation about the classification algorithm they used, i.e. k-Nearest Neighbor.
They experimented on 2 dataset: Twitter Dataset and Amazon Dataset
Evaluation
In this paper, it proposed several feature extraction methods and a data enrichment method. In the evaluation part, it mainly compared the performance between those methods. Moreover, the authors used two settings to test the robustness, one is traditional in-domain cross validation and the other is cross domain test. It reported promising results on both settings.
Discussion
First of all, I have to say it's not a self-contained paper, it depends on another paper heavily and it's not a creative work. This paper didn't change much from the AAAI 2010 paper. The only thing that this paper did is changed some small setting of previous paper: the algorithm follows AAAI 2010 paper, the feature follows the ACL 2006 paper [1]
The weak points of the paper includes:
1. It haven't any significant change to previous methods 2. It depended on another paper so heavy that the algorithm is not complete without that paper. 3. It didn't consider any baseline algorithms. For example, they can compare their method to other semi-supervised methods or related sarcasm detection methods.
Strong point:
1. It's among some first attempt to solve this problem using semi-supervised learning method.
Related papers
- Paper:Icwsm - a great catchy name: Semi-supervised recognition of sarcastic sentences in product reviews:[2]
- Paper:Efficient unsupervised discovery o word categories using symmetric patterns and high frequency words:[3]
- Paper:Automatic satire detection: Are you having a laugh?:[4]
Study plan
As a typical incremental work, the original works includes:
- Paper:Icwsm - a great catchy name: Semi-supervised recognition of sarcastic sentences in product reviews:[5]
- Paper:Efficient unsupervised discovery o word categories using symmetric patterns and high frequency words:[6]
And the classification algorithm used:
- Article: k-Nearest Neighbor:k-Nearest Neighbor
