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External Knowledge Sources For Answer Quality Prediction And Sub-Topic DetectionAuthor: Suggu Sai Praneeth Date: 2021-04-16 Report no: IIIT/TH/2021/34 Advisor:Manish Shrivastava,Manoj Chinnakotla AbstractTo perform any task, humans often rely on certain amount of broad background knowledge obtained from sources outside the context of the task. Similarly, machine learning models can also leverage external knowledge to perform well on a task. External knowledge sources can induce knowledge from across the web. Hand-crafted features are used to solve a wide range of natural language processing (NLP) tasks ranging from part-of-speech tagging to machine translation and sentence simplification. Hand-crafted features generated from external knowledge sources induce broad knowledge from across the web. Using hand-crafted features computed from external knowledge sources in addition to the features computed from the data alone improves the performance of an approach significantly due to the external knowledge induced. Recently, deep learning methods have shown impressive gains on many NLP tasks by using neural networks based on dense vector representations. The deep learning approaches try to automatically identify and learn useful features to better represent the data and solve a problem. They can represent huge amounts of data and draw complex decision boundaries using large neural networks. Deep learning algorithms seek to exploit the unknown structure in the input distribution to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features. Most of the works either use shallow models (with hand-crafted features) or deep learning methods to solve NLP tasks. As deep learning approaches are giving superior performances over methods employing only hand-crafted features, they are being worked upon the most these days. Even though, the deep learning based approaches can automatically find patterns for a task and underlying distribution of data, the hand-crafted features are subjective. Deep learning based models cannot find some patterns which are easily captured by hand-crafted features generated from data and external knowledge bases. Thus, hand-crafted features for a given problem are still important. They enhance the performance of deep learning based algorithms. In most of the NLP tasks, the hand-crafted features are mainly being used in shallow models like SVM, logistic regression, etc. but not in deep learning models and thus missing out the patterns which can be easily captured by hand-crafted features. In this thesis, we propose novel approaches to Predict Answer Quality in Community Question Answering Forums and to Detect Sub-Topics in a tweet. We use external knowledge sources to generate more hand-crafted features in addition to the ones generated from the data alone to generate better representation of the data. Due to these knowledge sources, a lot of information from the web was induced resulting in enhanced performance for both the tasks. We use the hand-crafted features generated from both the data and external knowledge sources in our novel approach to detect sub-topics in tweets. Further, we compute handcrafted features from both the data and external knowledge sources and feed these hand-crafted features as inputs to a deep learning model in our novel approach, “Deep Feature Fusion Network (DFFN)”, to predict answer quality in community question answering forums. In addition to the hand-crafted features, the question and answer representations are given as inputs to the deep learning model. DFFN combines the hand-crafted features in the deep learning model with the processed output of the question and answer representations to predict the quality of an answer. This fusion allowed us to leverage the advantages of both hand-crafted features and deep learning based models and achieve significant improvement. We outperform the all baseline and state-of-the-art approaches for both the tasks discussed in this thesis Full thesis: pdf Centre for Language Technologies Research Centre |
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