Classifying Non-Sentential Utterances in Dialogue: A Machine Learning Approach

In this article we use well-known machine learning methods to tackle a novel task, namely the classification of non-sentential utterances (NSUs) in dialogue. We introduce a fine-grained taxonomy of NSU classes based on corpus work, and then report on the results of several machine learning experiments. First, we present a pilot study focused on one of the NSU classes in the taxonomy—bare wh-phrases or “sluices”—and explore the task of disambiguating between the different readings that sluices can convey. We then extend the approach to classify the full range of NSU classes, obtaining results of around an 87% weighted F-score. Thus our experiments show that, for the taxonomy adopted, the task of identifying the right NSU class can be successfully learned, and hence provide a very encouraging basis for the more general enterprise of fully processing NSUs.

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How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2016.09.012 ◽

2016 ◽

Vol 64 ◽

pp. 20-24 ◽

Cited By ~ 26

Author(s):

Daisuke Ichikawa ◽

Toki Saito ◽

Waka Ujita ◽

Hiroshi Oyama

Keyword(s):

Machine Learning ◽

Virtual Screening ◽

Learning Approach ◽

Learning Methods ◽

Machine Learning Methods ◽

Machine Learning Approach

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To switch or not to switch – a machine learning approach for ferroelectricity

Nanoscale Advances ◽

10.1039/c9na00731h ◽

2020 ◽

Vol 2 (5) ◽

pp. 2063-2072 ◽

Cited By ~ 1

Author(s):

Sabine M. Neumayer ◽

Stephen Jesse ◽

Gabriel Velarde ◽

Andrei L. Kholkin ◽

Ivan Kravchenko ◽

...

Keyword(s):

Machine Learning ◽

Measured Signal ◽

Learning Approach ◽

Two Dimensional ◽

Dimensional Representation ◽

Machine Learning Methods ◽

Machine Learning Approach ◽

Signal Dependence ◽

Two Parameter

The introduced two-dimensional representation of two-parameter signal dependence allows for clear interpretation and classification of the measured signal upon using machine learning methods.

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Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studies

10.1101/2020.06.16.20132670 ◽

2020 ◽

Author(s):

Toni Lange ◽

Guido Schwarzer ◽

Thomas Datzmann ◽

Harald Binder

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Diagnostic Test ◽

Systematic Reviews ◽

Data Preprocessing ◽

Learning Approach ◽

Learning Methods ◽

Final Performance ◽

Machine Learning Methods ◽

Machine Learning Approach

AbstractBackgroundUpdating systematic reviews is often a time-consuming process involving a lot of human effort and is therefore not carried out as often as it should be. Our aim was therefore to explore the potential of machine learning methods to reduce the human workload, and to particularly also gauge the performance of deep learning methods as compared to more established machine learning methods.MethodsWe used three available reviews of diagnostic test studies as data basis. In order to identify relevant publications we used typical text pre-processing methods. The reference standard for the evaluation was the human-consensus based binary classification (inclusion, exclusion). For the evaluation of models various scenarios were generated using a grid of combinations of data preprocessing steps. Furthermore, we evaluated each machine learning approach with an approach-specific predefined grid of tuning parameters using the Brier score metric.ResultsThe best performance was obtained with an ensemble method for two of the reviews, and by a deep learning approach for the other review. Yet, the final performance of approaches is seen to strongly depend on data preparation. Overall, machine learning methods provided reasonable classification.ConclusionIt seems possible to reduce the human workload in updating systematic reviews by using machine learning methods. Yet, as the influence of data preprocessing on the final performance seems to be at least as important as choosing the specific machine learning approach, users should not blindly expect good performance just by using approaches from a popular class, such as deep learning.

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Novel Machine learning approach for Self-Aware prediction based on the Contextual reasoning

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2021.4.4345 ◽

2021 ◽

Vol 16 (4) ◽

Author(s):

Andrius Daranda ◽

Gintautas Dzemyda

Keyword(s):

Machine Learning ◽

Threat Assessment ◽

Support Vector ◽

Learning Approach ◽

Context Aware ◽

Learning Methods ◽

Vector Method ◽

Machine Learning Methods ◽

Contextual Reasoning ◽

Machine Learning Approach

Machine learning is compelling in solving various applied problems. Nevertheless, machine learning methods lack the contextual reasoning capabilities and cannot be fitted to utilize additional information about circumstances, environments, backgrounds, etc. Such information provides essential knowledge about possible reasons for particular actions. This knowledge could not be processed directly by either machine learning methods. This paper presents the context-aware machine learning approach for actor behavior contextual reasoning analysis and context-based prediction for threat assessment. Moreover, the proposed approach uses context-aware prediction to tackle the interaction between actors. An idea of the technique lies in the cooperative use of two classification methods when one way predicts an actor’s behavior. The second method discloses such predicted action (behavior) that is non-typical or unusual. Such integration of two-method allows the actor to make the self-awareness threat assessment based on relations between different actors where some multidimensional numerical data define the connections. This approach predicts the possible further situation and makes its threat assessment without any waiting for future actions. The suggested approach is based on the Decision Tree and Support Vector Method algorithm. Due to the complexity of context, marine traffic data was chosen to demonstrate the proposed approach capability. This technique could deal with the end-to-end approach for safe vessel navigation in maritime traffic with considerable ship congestion.

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Enhanced Classification of Incomplete Pattern Using Fuzzy Systems

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst196133 ◽

2019 ◽

pp. 204-210

Author(s):

Yogita Deshmukh ◽

Pallavi Khawshi ◽

Priyanka Shinde ◽

Ruchita Charpe ◽

Rupali Bopche ◽

...

Keyword(s):

Machine Learning ◽

Hierarchical Clustering ◽

Fuzzy Systems ◽

Learning Approach ◽

Specific Class ◽

Course Of Action ◽

Data Segment ◽

Machine Learning Approach ◽

The Right

More often than not values are absent in database, which ought to be managed. Missing qualities are occurred in light of the way that, the data segment individual did not know the right regard or frustration of sensors or leave the space cleanse. The course of action of missing regarded lacking case is a trying errand in machine learning approach. Divided data is not proper for classification handle. Exactly when insufficient cases are masterminded using prototype values, the last class for comparable illustrations may have distinctive results that are variable yields. We can't describe specific class for specific cases. The structure makes a wrong result which also realizes contrasting effects. So, to oversee such kind of lacking data, system executes prototype-based credal classification (PCC) strategy. The PCC procedure is intertwined with Hierarchical clustering and evidential reasoning methodology to give correct, time and memory profitable outcomes. This procedure readies the examples and perceives the class prototype. This will be useful for recognizing the missing qualities. By then in the wake of getting each and every missing worth, credal procedure is use for classification. The trial occurs exhibit that the enhanced type of PCC performs better similar to time and memory viability.

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Enhanced Classification of Incomplete Pattern Using Hierarchical Clustering

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset19632 ◽

2019 ◽

pp. 594-599

Author(s):

Gaminee Sharnagat ◽

Pragati Patil

Keyword(s):

Machine Learning ◽

Hierarchical Clustering ◽

Data System ◽

Learning Approach ◽

Specific Class ◽

Course Of Action ◽

Data Segment ◽

Machine Learning Approach ◽

The Right

More often than not values are absent in database, which ought to be managed. Missing qualities are occurred in light of the way that, the data segment individual did not know the right regard or frustration of sensors or leave the space cleanse. The course of action of missing regarded lacking case is a trying errand in machine learning approach. Divided data is not proper for classification handle. Exactly when insufficient cases are masterminded using prototype values, the last class for comparable illustrations may have distinctive results that are variable yields. We can't describe specific class for specific cases. The structure makes a wrong result which also realizes contrasting effects. So to oversee such kind of lacking data, system executes prototype-based credal classification (PCC) strategy. The PCC procedure is intertwined with Hierarchical clustering and evidential reasoning methodology to give correct, time and memory profitable outcomes. This procedure readies the examples and perceives the class prototype. This will be useful for recognizing the missing qualities. By then in the wake of getting each and every missing worth, credal procedure is use for classification. The trial occurs exhibit that the enhanced type of PCC performs better similar to time and memory viability.

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A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

Microorganisms ◽

10.3390/microorganisms9051034 ◽

2021 ◽

Vol 9 (5) ◽

pp. 1034

Author(s):

Carlos Sabater ◽

Lorena Ruiz ◽

Abelardo Margolles

Keyword(s):

Machine Learning ◽

Supervised Classification ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Human Milk Oligosaccharides ◽

Future Studies ◽

High Fiber ◽

Machine Learning Approach ◽

Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

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