Topic Classification

Symbolic and statistical approaches have traditionally been kept separate and applied to very different problems. Symbolic techniques apply best where we have a priori knowledge of the language or the …...

In this paper we present some lessons learned from building viz s la, the keyword search and topic classification system used on the largest Hungarian portal, [ origo .hu]. Based …...

AbstractThe NOMAD project (Policy Formulation and Validation through non Moderated Crowd-sourcing) is a project that supports policy making, by providing rich, actionable information related to how citizens perceive different policies. …...

When applying machine learning to prob- lems in NLP, there are many choices to make about how to represent input texts. They can have a big effect on perfor- mance, …...

This paper proposes a variability normaliza- tion algorithm to reduce the variability be- tween intra-topic documents for topic classi- fication. Firstly, an optimization problem is constructed based on linear variability …...

Large amount of user-generated data are posted online in social media platforms, in- cluding user preferences, dining and leisure activities, events, news and personal blogs. This resulted in varying efforts …...

AbstractDropout is used to avoid overfitting by randomly dropping units from the neural networks during training. Inspired by dropout, this paper presents GI-Dropout, a novel dropout method integrating with global …...

AbstractWe present a method for applying a neural network trained on one (resource-rich) language for a given task to other (resource-poor) languages. We accomplish this by inducing a mapping from …...

AbstractNews recommendation can help users find interested news and alleviate information overload. The topic information of news is critical for learning accurate news and user representations for news recommendation. However, …...

AbstractSimplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have insufficient performance because they do not take into account that …...

AbstractWe study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources. This problem is challenging because rule-induced weak …...

AbstractWord embeddings and pre-trained language models allow to build rich representations of text and have enabled improvements across most NLP tasks. Unfortunately they are very expensive to train, and many …...

AbstractMultilingual transformer models like mBERT and XLM-RoBERTa have obtained great improvements for many NLP tasks on a variety of languages. However, recent works also showed that results from high-resource languages …...

AbstractNeural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models. One added benefit when using representations from multilingual models is …...

AbstractActive learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification …...

AbstractGenre identification is a kind of non-topic text classification. The main difference between this task and topic classification is that genre, unlike topic, usually cannot be expressed just by some …...

AbstractThis paper aims for a potential architectural improvement for multilingual learning and asks: Can different tasks from different languages be modeled in a monolithic framework, i.e. without any task/language-specific module? …...

AbstractPrompt-based tuning for pre-trained language models (PLMs) has shown its effectiveness in few-shot learning. Typically, prompt-based tuning wraps the input text into a cloze question. To make predictions, the model …...

AbstractUser language data can contain highly sensitive personal content. As such, it is imperative to offer users a strong and interpretable privacy guarantee when learning from their data. In this …...