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"Sentiment": models, code, and papers

Using Recurrent Neural Network for Learning Expressive Ontologies

Jul 14, 2016
Giulio Petrucci, Chiara Ghidini, Marco Rospocher

Recently, Neural Networks have been proven extremely effective in many natural language processing tasks such as sentiment analysis, question answering, or machine translation. Aiming to exploit such advantages in the Ontology Learning process, in this technical report we present a detailed description of a Recurrent Neural Network based system to be used to pursue such goal.

* Technical Report 

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Neural Networks as Explicit Word-Based Rules

Jul 10, 2019
Jind┼Öich Libovick├Ż

Filters of convolutional networks used in computer vision are often visualized as image patches that maximize the response of the filter. We use the same approach to interpret weight matrices in simple architectures for natural language processing tasks. We interpret a convolutional network for sentiment classification as word-based rules. Using the rule, we recover the performance of the original model.

* 3 pages; extended abstract at BlackboxNLP 2019 

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Comparing Writing Styles using Word Embedding and Dynamic Time Warping

Nov 05, 2015
Abhinav Tushar, Abhinav Dahiya

The development of plot or story in novels is reflected in the content and the words used. The flow of sentiments, which is one aspect of writing style, can be quantified by analyzing the flow of words. This study explores literary works as signals in word embedding space and tries to compare writing styles of popular classic novels using dynamic time warping.


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Stance detection in online discussions

Jan 02, 2017
Peter Krejzl, Barbora Hourová, Josef Steinberger

This paper describes our system created to detect stance in online discussions. The goal is to identify whether the author of a comment is in favor of the given target or against. Our approach is based on a maximum entropy classifier, which uses surface-level, sentiment and domain-specific features. The system was originally developed to detect stance in English tweets. We adapted it to process Czech news commentaries.


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Faces: AI Blitz XIII Solutions

Apr 03, 2022
Andrew Melnik, Eren Akbulut, Jannik Sheikh, Kira Loos, Michael Buettner, Tobias Lenze

AI Blitz XIII Faces challenge hosted on www.aicrowd.com platform consisted of five problems: Sentiment Classification, Age Prediction, Mask Prediction, Face Recognition, and Face De-Blurring. Our team GLaDOS took second place. Here we present our solutions and results. Code implementation: https://github.com/ndrwmlnk/ai-blitz-xiii


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The Dawn of Quantum Natural Language Processing

Oct 13, 2021
Riccardo Di Sipio, Jia-Hong Huang, Samuel Yen-Chi Chen, Stefano Mangini, Marcel Worring

In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing. We successfully train a quantum-enhanced Long Short-Term Memory network to perform the parts-of-speech tagging task via numerical simulations. Moreover, a quantum-enhanced Transformer is proposed to perform the sentiment analysis based on the existing dataset.


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Crowd-Labeling Fashion Reviews with Quality Control

Apr 05, 2018
Iurii Chernushenko, Felix A. Gers, Alexander L├Âser, Alessandro Checco

We present a new methodology for high-quality labeling in the fashion domain with crowd workers instead of experts. We focus on the Aspect-Based Sentiment Analysis task. Our methods filter out inaccurate input from crowd workers but we preserve different worker labeling to capture the inherent high variability of the opinions. We demonstrate the quality of labeled data based on Facebook's FastText framework as a baseline.


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SlovakBERT: Slovak Masked Language Model

Sep 30, 2021
Mat├║┼í Pikuliak, ┼átefan Grivalsk├Ż, Martin Kon├┤pka, Miroslav Bl┼ít├ík, Martin Tamajka, Viktor Bachrat├Ż, Mari├ín ┼áimko, Pavol Bal├í┼żik, Michal Trnka, Filip Uhl├írik

We introduce a new Slovak masked language model called SlovakBERT in this paper. It is the first Slovak-only transformers-based model trained on a sizeable corpus. We evaluate the model on several NLP tasks and achieve state-of-the-art results. We publish the masked language model, as well as the subsequently fine-tuned models for part-of-speech tagging, sentiment analysis and semantic textual similarity.

* 22 pages, 2 figures 

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Concept-Based Embeddings for Natural Language Processing

Jul 15, 2018
Yukun Ma, Erik Cambria

In this work, we focus on effectively leveraging and integrating information from concept-level as well as word-level via projecting concepts and words into a lower dimensional space while retaining most critical semantics. In a broad context of opinion understanding system, we investigate the use of the fused embedding for several core NLP tasks: named entity detection and classification, automatic speech recognition reranking, and targeted sentiment analysis.


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Embedding Learning Through Multilingual Concept Induction

Jun 27, 2018
Philipp Dufter, Mengjie Zhao, Martin Schmitt, Alexander Fraser, Hinrich Sch├╝tze

We present a new method for estimating vector space representations of words: embedding learning by concept induction. We test this method on a highly parallel corpus and learn semantic representations of words in 1259 different languages in a single common space. An extensive experimental evaluation on crosslingual word similarity and sentiment analysis indicates that concept-based multilingual embedding learning performs better than previous approaches.

* ACL 2018 

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