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"Topic": models, code, and papers

[email protected]: Detecting Signs of Depression from Social Media Text

Apr 09, 2022
Manex Agirrezabal, Janek Amann

In this paper we present our approach for detecting signs of depression from social media text. Our model relies on word unigrams, part-of-speech tags, readabilitiy measures and the use of first, second or third person and the number of words. Our best model obtained a macro F1-score of 0.439 and ranked 25th, out of 31 teams. We further take advantage of the interpretability of the Logistic Regression model and we make an attempt to interpret the model coefficients with the hope that these will be useful for further research on the topic.


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Training and Generating Neural Networks in Compressed Weight Space

Dec 31, 2021
Kazuki Irie, Jürgen Schmidhuber

The inputs and/or outputs of some neural nets are weight matrices of other neural nets. Indirect encodings or end-to-end compression of weight matrices could help to scale such approaches. Our goal is to open a discussion on this topic, starting with recurrent neural networks for character-level language modelling whose weight matrices are encoded by the discrete cosine transform. Our fast weight version thereof uses a recurrent neural network to parameterise the compressed weights. We present experimental results on the enwik8 dataset.

* Presented at ICLR 2021 Workshop on Neural Compression, https://openreview.net/forum?id=qU1EUxdVd_D 

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The Flipped Classroom model for teaching Conditional Random Fields in an NLP course

May 04, 2021
Manex Agirrezabal

In this article, we show and discuss our experience in applying the flipped classroom method for teaching Conditional Random Fields in a Natural Language Processing course. We present the activities that we developed together with their relationship to a cognitive complexity model (Bloom's taxonomy). After this, we provide our own reflections and expectations of the model itself. Based on the evaluation got from students, it seems that students learn about the topic and also that the method is rewarding for some students. Additionally, we discuss some shortcomings and we propose possible solutions to them. We conclude the paper with some possible future work.

* Accepted to the 5th Workshop on Teaching NLP at NAACL-HLT 2021 

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Stochastic Probabilistic Programs

Jan 22, 2020
David Tolpin, Tomer Dobkin

We introduce the notion of a stochastic probabilistic program and present a reference implementation of a probabilistic programming facility supporting specification of stochastic probabilistic programs and inference in them. Stochastic probabilistic programs allow straightforward specification and efficient inference in models with nuisance parameters, noise, and nondeterminism. We give several examples of stochastic probabilistic programs, and compare the programs with corresponding deterministic probabilistic programs in terms of model specification and inference. We conclude with discussion of open research topics and related work.

* 7 pages main body, 4 pages appendix 

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Stochastic probabilistic programs

Jan 08, 2020
David Tolpin, Tomer Dobkin

We introduce the notion of a stochastic probabilistic program and present a reference implementation of a probabilistic programming facility supporting specification of stochastic probabilistic programs and inference in them. Stochastic probabilistic programs allow straightforward specification and efficient inference in models with nuisance parameters, noise, and nondeterminism. We give several examples of stochastic probabilistic programs, and compare the programs with corresponding deterministic probabilistic programs in terms of model specification and inference. We conclude with discussion of open research topics and related work.


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You Write Like You Eat: Stylistic variation as a predictor of social stratification

Jul 16, 2019
Angelo Basile, Albert Gatt, Malvina Nissim

Inspired by Labov's seminal work on stylistic variation as a function of social stratification, we develop and compare neural models that predict a person's presumed socio-economic status, obtained through distant supervision,from their writing style on social media. The focus of our work is on identifying the most important stylistic parameters to predict socio-economic group. In particular, we show the effectiveness of morpho-syntactic features as stylistic predictors of socio-economic group,in contrast to lexical features, which are good predictors of topic.

* 11 pages, 5 figures, ACL Conference 2019 

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Unmasking Bias in News

Jun 11, 2019
Javier Sánchez-Junquera, Paolo Rosso, Manuel Montes-y-Gómez, Simone Paolo Ponzetto

We present experiments on detecting hyperpartisanship in news using a 'masking' method that allows us to assess the role of style vs. content for the task at hand. Our results corroborate previous research on this task in that topic related features yield better results than stylistic ones. We additionally show that competitive results can be achieved by simply including higher-length n-grams, which suggests the need to develop more challenging datasets and tasks that address implicit and more subtle forms of bias.


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Semantic Characteristics of Schizophrenic Speech

Apr 16, 2019
Kfir Bar, Vered Zilberstein, Ido Ziv, Heli Baram, Nachum Dershowitz, Samuel Itzikowitz, Eiran Vadim Harel

Natural language processing tools are used to automatically detect disturbances in transcribed speech of schizophrenia inpatients who speak Hebrew. We measure topic mutation over time and show that controls maintain more cohesive speech than inpatients. We also examine differences in how inpatients and controls use adjectives and adverbs to describe content words and show that the ones used by controls are more common than the those of inpatients. We provide experimental results and show their potential for automatically detecting schizophrenia in patients by means only of their speech patterns.

* CLPsych at NAACL 2019 

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Brief survey of Mobility Analyses based on Mobile Phone Datasets

Dec 03, 2018
Carlos Sarraute, Martin Minnoni

This is a brief survey of the research performed by Grandata Labs in collaboration with numerous academic groups around the world on the topic of human mobility. A driving theme in these projects is to use and improve Data Science techniques to understand mobility, as it can be observed through the lens of mobile phone datasets. We describe applications of mobility analyses for urban planning, prediction of data traffic usage, building delay tolerant networks, generating epidemiologic risk maps and measuring the predictability of human mobility.

* Workshop on Urban Computing and Society. Petropolis, RJ, Brazil. Nov 28, 2018 

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