88 research outputs found
A sequence modeling approach for structured data extraction from unstructured text
No full textExtraction of structured information from unstructured text has always been a problem of interest for NLP community. Structured data is concise to store, search and retrieve; and it facilitates easier human & machine consumption. Traditionally, structured data extraction from text has been done by using various parsing methodologies, applying domain specific rules and heuristics. In this work, we leverage the developments in the space of sequence modeling for the problem of structured data extraction. Initially, we posed the problem as a machine translation problem and used the state-of-the-art machine translation model. Based on these initial results, we changed the approach to a sequence tagging one. We propose an extension of one of the attractive models for sequence tagging tailored and effective to our problem. This gave 4.4% improvement over the vanilla sequence tagging model. We also propose another variant of the sequence tagging model which can handle multiple labels of words. Experiments have been performed on Wikipedia Infobox Dataset of biographies and results are presented for both single and multi-label models. These models indicate an effective alternate deep learning technique based methods to extract structured data from raw text.</p
LiveDoc: Showing Contextual Information Using Topic Modeling Techniques
No full textWe present a solution named LiveDoc, which augments natural language text documents with relevant contextual background information. This background information helps readers to understand the context of the discourse better by fetching relevant information from other sources such as Wikipedia. Often the readers do not possess all background and supplementary information required for comprehending the purport of a narrative such as a news op-ed article. At the same time, it is not possible for authors to provide all contextual information while addressing a particular topic. LiveDoc processes the information in a document; uses extracted entities to fetch relevant background information in the context of the document from various sources (as defined by user) using semantic matching and topic modeling techniques like Latent Dirichlet Allocation and Hierarchical Dirichlet Process; and presents the background information to the user by augmenting the original document with the fetched information. Reader is then equipped better to understand the document with this additional background information. We present the effectiveness of our solution through extensive experimentation and associated results.</p
Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques
No full textDuplicate Bug Detection is the problem of identifying whether a newly reported bug is a duplicate of an existing bug in the system and retrieving the original or similar bugs from the past. This is required to avoid costly rediscovery and redundant work. In typical software projects, the number of duplicate bugs reported may run into the order of thousands, making it expensive in terms of cost and time for manual intervention. This makes the problem of duplicate or similar bug detection an important one in Software Engineering domain. However, an automated solution for the same is not quite accurate yet in practice, in spite of many reported approaches using various machine learning techniques. In this work, we propose a retrieval and classification model using Siamese Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) for accurate detection and retrieval of duplicate and similar bugs. We report an accuracy close to 90% and recall rate close to 80%, which makes possible the practical use of such a system. We describe our model in detail along with related discussions from the Deep Learning domain. By presenting the detailed experimental results, we illustrate the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.</p
Semantic Question Classification Datasets
No full textThis is the datasets used in the following paper:Can Taxonomy Help? Improving Semantic Question Matching using Question TaxonomyPaper: http://aclweb.org/anthology/C18-1042If you use the dataset please cite the following paper:@InProceedings{C18-1042,
author = "Gupta, Deepak
and Pujari, Rajkumar
and Ekbal, Asif
and Bhattacharyya, Pushpak
and Maitra, Anutosh
and Jain, Tom
and Sengupta, Shubhashis",
title = "Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "499--513",
location = "Santa Fe, New Mexico, USA",
url = "http://aclweb.org/anthology/C18-1042"
}
</div
Scalable and Distributed Mechanisms for Integrated Scheduling and Replication in Data Grids
No full text
Small Farmers, Moneylenders and Trading Activity.
No full textIn this paper, the authors show how an interlinkage between the credit and output markets is an endogenous outcome of credit market imperfections. Also, such interlinkages are Pareto optimal. This is worked out under the assumption of infinite availability of funds with the money lender. If, however, the lender faces a loanable funds constraint, it is not optimal for him to interlink, and he charges monopoly rates of interest. The paper, therefore, endogenously develops various forms of trading and lending activities. Copyright 1987 by Royal Economic Society.
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