Analyzing Linguistic Features for Answer Re-Ranking of Why-Questions

Why-type non-factoid questions are ambiguous and involve variations in their answers. A challenge in returning one appropriate answer to user requires the process of appropriate answer extraction, re-ranking and validation. There are cases where the need is to understand the meaning and context of a document rather than finding exact words involved in question. The paper addresses this problem by exploring lexico-syntactic, semantic and contextual query-dependent features, some of which are based on deep learning frameworks to depict the probability of answer candidate being relevant for the question. The features are weighted by the score returned by ensemble ExtraTreesClassifier according to features importance. An answer re-ranker model is implemented that finds the highest ranked answer comprising largest value of feature similarity between question and answer candidate and thus achieving 0.64 Mean Reciprocal Rank (MRR). Further, answer is validated by matching the answer type of answer candidate and returns the highest ranked answer candidate with matched answer type to a user.

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Analyzing Linguistic Features for Answer Re-Ranking of Why-Questions

Journal of Cases on Information Technology ◽

10.4018/jcit.20220701.oa10 ◽

2022 ◽

Vol 24 (3) ◽

pp. 1-16

Author(s):

Manvi Breja ◽

Sanjay Kumar Jain

Keyword(s):

Deep Learning ◽

Linguistic Features ◽

Answer Extraction ◽

Feature Similarity ◽

Question And Answer ◽

Why Questions ◽

Learning Frameworks ◽

Reciprocal Rank ◽

Features Importance

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Automated Dysarthria Severity Classification Using Deep Learning Frameworks

2020 28th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco47968.2020.9287741 ◽

2021 ◽

Author(s):

Amlu Anna Joshy ◽

Rajeev Rajan

Keyword(s):

Deep Learning ◽

Severity Classification ◽

Learning Frameworks

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Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

Current Pharmaceutical Design ◽

10.2174/1381612826666201124112710 ◽

2020 ◽

Vol 26 ◽

Author(s):

Xiaoping Min ◽

Fengqing Lu ◽

Chunyan Li

Keyword(s):

Gene Expression ◽

Deep Learning ◽

Dna Sequences ◽

Experimental Methods ◽

Learning Methods ◽

Comprehensive Review ◽

Genomic Features ◽

Disease Mechanisms ◽

Evaluation Strategies ◽

Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

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