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Found 19 papers, 5 papers with code
CSRCZ: A Dataset About Corporate Social Responsibility in Czech Republic
As stakeholders' pressure on corporates for disclosing their corporate social responsibility operations grows, it is crucial to understand how efficient corporate disclosure systems are in bridging the gap between corporate social responsibility reports and their actual practice.
Batch Layer Normalization, A new normalization layer for CNNs and RNN
As a combined version of batch and layer normalization, BLN adaptively puts appropriate weight on mini-batch and feature normalization based on the inverse size of mini-batches to normalize the input to a layer during the learning process.
hmBERT: Historical Multilingual Language Models for Named Entity Recognition
We circumvent the need for large amounts of labeled data by using unlabeled data for pretraining a language model.
Topic Segmentation of Research Article Collections
In this work, we perform topic segmentation of a paper data collection that we crawled and produce a multitopic dataset of roughly seven million paper data records.
Focused Contrastive Training for Test-based Constituency Analysis
We propose a scheme for self-training of grammaticality models for constituency analysis based on linguistic tests.
How Many Pages? Paper Length Prediction from the Metadata
Being able to predict the length of a scientific paper may be helpful in numerous situations.
A Data-driven Neural Network Architecture for Sentiment Analysis
Our results indicate that parallel convolutions of filter lengths up to three are usually enough for capturing relevant text features.
Mood-based On-Car Music Recommendations
Driving and music listening are two inseparable everyday activities for millions of people today in the world.
Automating Text Naturalness Evaluation of NLG Systems
Instead of relying on human participants for scoring or labeling the text samples, we propose to automate the process by using a human likeliness metric we define and a discrimination procedure based on large pretrained language models with their probability distributions.
Human or Machine: Automating Human Likeliness Evaluation of NLG Texts
Automatic evaluation of various text quality criteria produced by data-driven intelligent methods is very common and useful because it is cheap, fast, and usually yields repeatable results.
Quality of Word Embeddings on Sentiment Analysis Tasks
Quality of word embeddings and performance of their applications depends on several factors like training method, corpus size and relevance etc.
Two Huge Title and Keyword Generation Corpora of Research Articles
Recent developments in sequence-to-sequence learning with neural networks have considerably improved the quality of automatically generated text summaries and document keywords, stipulating the need for even bigger training corpora.
Keyphrase Generation: A Multi-Aspect Survey
In this survey, we examine various aspects of the extractive keyphrase generation methods and focus mostly on the more recent abstractive methods that are based on neural networks.
Efficiency Metrics for Data-Driven Models: A Text Summarization Case Study
Using data-driven models for solving text summarization or similar tasks has become very common in the last years.
Keyphrase Generation: A Text Summarization Struggle
Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text.
Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey
This work investigates the role of factors like training method, training corpus size and thematic relevance of texts in the performance of word embedding features on sentiment analysis of tweets, song lyrics, movie reviews and item reviews.
Hybrid Recommender Systems: A Systematic Literature Review
Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors.
Sentiment Analysis of Czech Texts: An Algorithmic Survey
In the area of online communication, commerce and transactions, analyzing sentiment polarity of texts written in various natural languages has become crucial.
Text-based Sentiment Analysis and Music Emotion Recognition
Second, there are various uncertainties regarding the use of word embedding vectors: should they be generated from the same data set that is used to train the model or it is better to source them from big and popular collections?
