Index of papers in Proc. ACL 2010 that mention
  • unlabeled data
Li, Shoushan and Huang, Chu-Ren and Zhou, Guodong and Lee, Sophia Yat Mei
Introduction
Since the unlabeled data is ample and easy to collect, a successful semi-supervised sentiment classification system would significantly minimize the involvement of labor and time.
Introduction
Therefore, given the two different views mentioned above, one promising application is to adopt them in co-training algorithms, which has been proven to be an effective semi-supervised learning strategy of incorporating unlabeled data to further improve the classification performance (Zhu, 2005).
Introduction
Finally, a co-training algorithm is proposed to incorporate unlabeled data for semi-supervised sentiment classification.
Related Work
Semi-supervised methods combine unlabeled data with labeled training data (often small-scaled) to improve the models.
Unsupervised Mining of Personal and Impersonal Views
Semi-supervised learning is a strategy which combines unlabeled data with labeled training data to improve the models.
Unsupervised Mining of Personal and Impersonal Views
The co-training algorithm is a specific semi-supervised learning approach which starts with a set of labeled data and increases the amount of labeled data using the unlabeled data by bootstrapping (Blum and Mitchell, 1998).
Unsupervised Mining of Personal and Impersonal Views
L—impersonal The unlabeled data U containing personal sentence set S l and impersonal sentence set
unlabeled data is mentioned in 13 sentences in this paper.
Topics mentioned in this paper:
Prettenhofer, Peter and Stein, Benno
Abstract
We report on analyses that reveal quantitative insights about the use of unlabeled data and the complexity of inter-language correspondence modeling.
Cross-Language Structural Correspondence Learning
Note that the support of 2125 and 2127 can be determined from the unlabeled data Du.
Cross-Language Structural Correspondence Learning
Input: Labeled source data D5 Unlabeled data Du 2 D5,” U D1,,
Experiments
Due to the use of task-specific, unlabeled data , relevant characteristics are captured by the pivot classifiers.
Experiments
Unlabeled Data The first row of Figure 2 shows the performance of CL-SCL as a function of the ratio of labeled and unlabeled documents.
Related Work
In the basic domain adaptation setting we are given labeled data from the source domain and unlabeled data from the target domain, and the goal is to train a classifier for the target domain.
Related Work
SCL then models the correlation between the pivots and all other features by training linear classifiers on the unlabeled data from both domains.
Related Work
Ando and Zhang (2005b) present a semi-supervised learning method based on this paradigm, which generates related tasks from unlabeled data .
unlabeled data is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Turian, Joseph and Ratinov, Lev-Arie and Bengio, Yoshua
Introduction
By using unlabelled data to reduce data sparsity in the labeled training data, semi-supervised approaches improve generalization accuracy.
Unlabled Data
Unlabeled data is used for inducing the word representations.
Unlabled Data
(2009), we found that all word representations performed better on the supervised task when they were induced on the clean unlabeled data , both embeddings and Brown clusters.
Unlabled Data
Note that cleaning is applied only to the unlabeled data , not to the labeled data used in the supervised tasks.
unlabeled data is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Dhillon, Paramveer S. and Talukdar, Partha Pratim and Crammer, Koby
A <— METRICLEARNER(X, 3,1?)
In this case, we treat the set of test instances (without their gold labels) as the unlabeled data .
Introduction
IDML-IT (Dhillon et al., 2010) is another such method which exploits labeled as well as unlabeled data during metric learning.
Metric Learning
The ITML metric learning algorithm, which we reviewed in Section 2.2, is supervised in nature, and hence it does not exploit widely available unlabeled data .
Metric Learning
In this section, we review Inference Driven Metric Learning (IDML) (Algorithm 1) (Dhillon et al., 2010), a recently proposed metric learning framework which combines an existing supervised metric learning algorithm (such as ITML) along with transductive graph-based label inference to learn a new distance metric from labeled as well as unlabeled data combined.
unlabeled data is mentioned in 4 sentences in this paper.
Topics mentioned in this paper: