Мета Классификатор

method ID: metaclassifier

Related Papers

Multilingual Subjectivity: Are More Languages Better?

Authors: nan

Publication: TIPSTER (2010)

DOI: nan

While subjectivity related research in other languages has increased, most of the work focuses on single languages. This paper explores the integration of features originating from multiple languages into a …...

6806

Using Mostly Native Data to Correct Errors in Learners’ Writing

Authors: nan

Publication: TIPSTER (2010)

DOI: nan

We present results from a range of experi- ments on article and preposition error correc- tion for non-native speakers of English. We first compare a language model and error- specific …...

23910

Indonesian Dependency Treebank: Annotation and Parsing

Authors: nan

Publication: TIPSTER (2012)

DOI: nan

We introduce and describe ongoing work in our Indonesian dependency treebank. We de- scribed characteristics of the source data as well as describe our annotation guidelines for creating the dependency …...

18991

Re-examining Machine Translation Metrics for Paraphrase Identification

Authors: nan

Publication: TIPSTER (2012)

DOI: nan

We propose to re-examine the hypothesis that automated metrics developed for MT evalu- ation can prove useful for paraphrase iden- tification in light of the significant work on the development …...

23950

JOINT_FORCES: Unite Competing Sentiment Classifiers with Random Forest

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

In this paper, we describe how we cre- ated a meta-classifier to detect the mes- sage-level sentiment of tweets. We par- ticipated in SemEval-2014 Task 9B by combining the results …...

22829

Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

AbstractIn this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, …...

15647

Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

We describe a classifier for predicting message-level sentiment of English micro- blog messages from Twitter. This paper describes our submission to the SemEval- 2015 competition (Task 10). Our approach is …...

22647