Restricted thresholds for interval orders: A case of nonaxiomatizability by a universal sentence

In recent years, Deep Learning methods have become very popular in classification tasks for Natural Language Processing (NLP); this is mainly due to their ability to reach high performances by relying on very simple input representations, i.e., raw tokens. One of the drawbacks of deep architectures is the large amount of annotated data required for an effective training. Usually, in Machine Learning this problem is mitigated by the usage of semi-supervised methods or, more recently, by using Transfer Learning, in the context of deep architectures. One recent promising method to enable semi-supervised learning in deep architectures has been formalized within Semi-Supervised Generative Adversarial Networks (SS-GANs) in the context of Computer Vision. In this paper, we adopt the SS-GAN framework to enable semi-supervised learning in the context of NLP. We demonstrate how an SS-GAN can boost the performances of simple architectures when operating in expressive low-dimensional embeddings; these are derived by combining the unsupervised approximation of linguistic Reproducing Kernel Hilbert Spaces and the so-called Universal Sentence Encoders. We experimentally evaluate the proposed approach over a semantic classification task, i.e., Question Classification, by considering different sizes of training material and different numbers of target classes. By applying such adversarial schema to a simple Multi-Layer Perceptron, a classifier trained over a subset derived from 1% of the original training material achieves 92% of accuracy. Moreover, when considering a complex classification schema, e.g., involving 50 classes, the proposed method outperforms state-of-the-art alternatives such as BERT.

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An upper bound on the “dimension of interval orders”

Journal of Combinatorial Theory Series A ◽

10.1016/0097-3165(78)90032-8 ◽

1978 ◽

Vol 25 (1) ◽

pp. 68-71 ◽

Cited By ~ 16

Author(s):

I Rabinovitch

Keyword(s):

Upper Bound ◽

Interval Orders

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Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

10.31234/osf.io/uygzv ◽

2021 ◽

Author(s):

Xinxu Shen ◽

Troy Houser ◽

David Victor Smith ◽

Vishnu P. Murty

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Individual Difference ◽

Language Processing ◽

Large Scale ◽

High Reliability ◽

Difference Analysis ◽

Universal Sentence ◽

Natural Language Processing Tool

The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recall. We compared the reliability in scoring made between two independent raters (i.e., hand-scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique, video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand-scoring, and further that the results using USE outperformed another popular natural language processing tool, GloVe. In study two, we tested whether our automated approach remained valid when testing individual’s varying on clinically-relevant dimensions that influence episodic memory, age and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approaches implementing USE are a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.

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Numerical representation of interval orders on a topological space

Journal of Economic Theory ◽

10.1016/0022-0531(86)90093-1 ◽

1986 ◽

Vol 38 (1) ◽

pp. 160-166 ◽

Cited By ~ 21

Author(s):

Douglas S Bridges

Keyword(s):

Topological Space ◽

Numerical Representation ◽

Interval Orders

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Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study (Preprint)

10.2196/preprints.15371 ◽

2019 ◽

Author(s):

Derek Howard ◽

Marta M Maslej ◽

Justin Lee ◽

Jacob Ritchie ◽

Geoffrey Woollard ◽

...

Keyword(s):

Mental Health ◽

Machine Learning ◽

Social Media ◽

Transfer Learning ◽

Computational Linguistics ◽

Feature Representation ◽

Fine Tuning ◽

Language Models ◽

Universal Sentence ◽

Text Feature

BACKGROUND Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data that can be mined to predict mental health states using machine learning methods. OBJECTIVE This study aimed to benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools. We tested on datasets that contain posts labeled for perceived suicide risk or moderator attention in the context of self-harm. Specifically, we assessed the ability of the methods to prioritize posts that a moderator would identify for immediate response. METHODS We used 1588 labeled posts from the Computational Linguistics and Clinical Psychology (CLPsych) 2017 shared task collected from the Reachout.com forum. Posts were represented using lexicon-based tools, including Valence Aware Dictionary and sEntiment Reasoner, Empath, and Linguistic Inquiry and Word Count, and also using pretrained artificial neural network models, including DeepMoji, Universal Sentence Encoder, and Generative Pretrained Transformer-1 (GPT-1). We used Tree-based Optimization Tool and Auto-Sklearn as AutoML tools to generate classifiers to triage the posts. RESULTS The top-performing system used features derived from the GPT-1 model, which was fine-tuned on over 150,000 unlabeled posts from Reachout.com. Our top system had a macroaveraged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from metadata or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. In addition, we have presented visualizations that aid in the understanding of the learned classifiers. CONCLUSIONS In this study, we found that transfer learning is an effective strategy for predicting risk with relatively little labeled data and noted that fine-tuning of pretrained language models provides further gains when large amounts of unlabeled text are available.

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