40 research outputs found
Extension of TSVM to Multi-Class and Hierarchical Text Classification Problems With General Losses
Transductive SVM (TSVM) is a well known semi-supervised large margin learning
method for binary text classification. In this paper we extend this method to
multi-class and hierarchical classification problems. We point out that the
determination of labels of unlabeled examples with fixed classifier weights is
a linear programming problem. We devise an efficient technique for solving it.
The method is applicable to general loss functions. We demonstrate the value of
the new method using large margin loss on a number of multi-class and
hierarchical classification datasets. For maxent loss we show empirically that
our method is better than expectation regularization/constraint and posterior
regularization methods, and competitive with the version of entropy
regularization method which uses label constraints
Learning to Answer Semantic Queries over Code
During software development, developers need answers to queries about
semantic aspects of code. Even though extractive question-answering using
neural approaches has been studied widely in natural languages, the problem of
answering semantic queries over code using neural networks has not yet been
explored. This is mainly because there is no existing dataset with extractive
question and answer pairs over code involving complex concepts and long chains
of reasoning. We bridge this gap by building a new, curated dataset called
CodeQueries, and proposing a neural question-answering methodology over code.
We build upon state-of-the-art pre-trained models of code to predict answer
and supporting-fact spans. Given a query and code, only some of the code may be
relevant to answer the query. We first experiment under an ideal setting where
only the relevant code is given to the model and show that our models do well.
We then experiment under three pragmatic considerations: (1) scaling to
large-size code, (2) learning from a limited number of examples and (3)
robustness to minor syntax errors in code. Our results show that while a neural
model can be resilient to minor syntax errors in code, increasing size of code,
presence of code that is not relevant to the query, and reduced number of
training examples limit the model performance. We are releasing our data and
models to facilitate future work on the proposed problem of answering semantic
queries over code
Learning from positive and unlabelled examples using maximum margin clustering
Learning from Positive and Unlabelled examples (LPU) has emerged as an important problem in data mining and information retrieval applications. Existing techniques are not ideally suited for real world scenarios where the datasets are linearly inseparable, as they either build linear classifiers or the non-linear classifiers fail to achieve the desired performance. In this work, we propose to extend maximum margin clustering ideas and present an iterative procedure to design a non-linear classifier for LPU. In particular, we build a least squares support vector classifier, suitable for handling this problem due to symmetry of its loss function. Further, we present techniques for appropriately initializing the labels of unlabelled examples and for enforcing the ratio of positive to negative examples while obtaining these labels. Experiments on real-world datasets demonstrate that the non-linear classifier designed using the proposed approach gives significantly better generalization performance than the existing relevant approaches for LPU