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.

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.

Regulation Modelling and Analysis Using Machine Learning During the Covid-19 Pandemic in Russia

Due to the specific circumstances related to the COVID-19 pandemic, many countries have enforced emergency measures such as self-isolation and restriction of movement and assembly, which are also directly affecting the functioning of their respective public health and judicial systems. The goal of this study is to identify the efficiency of the criminal sanctions in Russia that were introduced in the beginning of COVID-19 outbreak using machine learning methods. We have developed a regression model for the fine handed out, using random forest regression and XGBoost regression, and calculated the features importance parameters. We have developed classification models for the remission of the penalty and for setting a sentence using a gradient boosting classifier.

PREDICTION OF CHRONIC KIDNEY DISEASE USING DEEP NEURAL NETWORK

eural Network (DNN) is now applied in disease prediction to detect various ailments such as heart disease and diabetes. Another disease that is causing a threat to our health is kidney disease. This disease is becoming prevalent due to substances and elements we intake. Ignoring the kidney malfunction can cause chronic kidney disease leading to death. Frequently, Chronic Kidney Disease (CKD) and its symptoms are mild and gradual, often go unnoticed for years only to be realized of late. We conducted our research on CKD in Bade, a Local Government Area of Yobe State in Nigeria. The area has been a center of attention by medical practitioners due to the prevalence of CKD. Unfortunately, a technical approach in culminating the disease is yet to be attained. We obtained a record of 1200 patients with 10 attributes as our dataset from Bade General Hospital and used the DNN model to predict CKD's absence or presence in the patients. The model produced an accuracy of 98%. Furthermore, we identified and highlighted the Features importance to rank the features used in predicting the CKD. The outcome revealed that two attributes: Creatinine and Bicarbonate, have the highest influence on the CKD prediction.

FUNDAMENTALS OF COMMUNICATION UNITS - PHRASEOLOGICAL COMBINATION

The article discusses communicative units, which are the main indicators of linguistic communication, and also describes the functions, features, importance and place of communicative units in communication. Communicative units of the language are words, sentences, text and phraseological units, each of which requires individual study. Accordingly, the article examines the relationship of phraseological units with the communicative, emotional and expressive functions of the language. Focusing on the types of phrases and idioms of the phraseology of the Kazakh language, their similarities and differences were identified. The opinions of scientists about the semantic meaning, cognitive, emotional, expressive shades of phraseological expressions are analyzed, examples are given. The features, use, methods of creating phraseological units with the quality of personalization are analyzed. The communicative activity of phraseological units, the positive cognitive function, the pragmatic purpose of the sentence were studied. Phraseological phrases are grouped by communicative function.

A Novel Drift Detection Algorithm Based on Features’ Importance Analysis in a Data Streams Environment

The training set consists of many features that influence the classifier in different degrees. Choosing the most important features and rejecting those that do not carry relevant information is of great importance to the operating of the learned model. In the case of data streams, the importance of the features may additionally change over time. Such changes affect the performance of the classifier but can also be an important indicator of occurring concept-drift. In this work, we propose a new algorithm for data streams classification, called Random Forest with Features Importance (RFFI), which uses the measure of features importance as a drift detector. The RFFT algorithm implements solutions inspired by the Random Forest algorithm to the data stream scenarios. The proposed algorithm combines the ability of ensemble methods for handling slow changes in a data stream with a new method for detecting concept drift occurrence. The work contains an experimental analysis of the proposed algorithm, carried out on synthetic and real data.