Concept

N-gram

Related publications (10)

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

EPFL2024

Comparison of Subword Segmentation Methods for Open-vocabulary ASR using a Difficulty Metric

Philip Neil Garner, Claudiu-Cristian Musat

We experiment with subword segmentation approaches that are widely used to address the open vocabulary problem in the context of end-to-end automatic speech recognition (ASR). For morphologically rich languages such as German which has many rare words main ...

2020

COMPARISON OF SUBWORD SEGMENTATION METHODS FOR OPEN-VOCABULARYEND-TO-END SPEECH RECOGNITION

Philip Neil Garner, Claudiu-Cristian Musat

To address the open vocabulary problem in the context of end-to-end automatic speech recognition (ASR), we experiment with subword segmentation approaches, specifically byte-pair encoding and unigram language model. Such approaches are attractive in genera ...

Idiap2020

Cross-lingual Transfer for News Article Labeling: Benchmarking Statistical and Neural Models

Andrei Popescu-Belis, Nikolaos Pappas, Khalil Mrini

Cross-lingual transfer has been shown to increase the performance of a text classification model thanks to the use of Multilingual Hierarchical Attention Networks (MHAN), on which this work is based. Firstly, we compared the performance of monolingual and ...

Idiap2017

N-gram-Based Low-Dimensional Representation for Document Classification

Rémi Philippe Lebret, Ronan Collobert

The bag-of-words (BOW) model is the common approach for classifying documents, where words are used as feature for training a classifier. This generally involves a huge number of features. Some techniques, such as Latent Semantic Analysis (LSA) or Latent D ...

2015

Discovering health-related knowledge in social media using ensembles of heterogeneous features

Marcel Salathé

Social media is emerging as a powerful source of communication, information dissemination and mining. Being colloquial and ubiquitous in nature makes it easier for users to express their opinions and preferences in a seamless, dynamic manner. Epidemic surv ...

ACM2013

Semantic Vector Machines

Vincent Etter

We first present our work in machine translation, during which we used aligned sentences to train a neural network to embed n-grams of different languages into an d-dimensional space, such that n-grams that are the translation of each other are close with ...

2009

Noisy Text Categorization

Alessandro Vinciarelli

This work presents categorization experiments performed over noisy texts. By noisy it is meant any text obtained through an extraction process (affected by errors) from media other than digital texts (e.g. transcriptions of speech recordings extracted with ...

2005

Noisy Text Categorization

Alessandro Vinciarelli

IDIAP2004

Language modeling based on neural clustering of words

This document describes a neural method for clustering words and its use in language modeling for speech recognizers. The method is based on clustering the words which appear on similar local context and estimating the parameters needed for language modeli ...

IDIAP2000

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.