Search Results for author:
Found 18 papers, 5 papers with code
Word Emdeddings through Hellinger PCA
Word embeddings resulting from neural language models have been shown to be successful for a large variety of NLP tasks.
Rehabilitation of Count-based Models for Word Vector Representations
We present a systematic study of the use of the Hellinger distance to extract semantic representations from the word co-occurence statistics of large text corpora.
N-gram-Based Low-Dimensional Representation for Document Classification
The number of features is therefore dramatically reduced and documents can be represented as bag of semantic concepts.
Phrase-based Image Captioning
Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing.
"The Sum of Its Parts": Joint Learning of Word and Phrase Representations with Autoencoders
We evaluate the quality of the word representations on several classical word evaluation tasks, and we introduce a novel task to evaluate the quality of the phrase representations.
Taxonomy Induction using Hypernym Subsequences
We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms.
280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification
We propose a simple, yet effective, approach towards inducing multilingual taxonomies from Wikipedia.
Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning
Companies, users, researchers, and regulators still lack usable and scalable tools to cope with the breadth and depth of privacy policies.
Weakly Supervised Active Learning with Cluster Annotation
In this work, we introduce a novel framework that employs cluster annotation to boost active learning by reducing the number of human interactions required to train deep neural networks.
Upgrading the Newsroom: An Automated Image Selection System for News Articles
The system fuses multiple textual sources extracted from news articles and accepts multilingual inputs.
Spoken dialect identification in Twitter using a multi-filter architecture
This paper presents our approach for SwissText & KONVENS 2020 shared task 2, which is a multi-stage neural model for Swiss German (GSW) identification on Twitter.
Direction is what you need: Improving Word Embedding Compression in Large Language Models
The adoption of Transformer-based models in natural language processing (NLP) has led to great success using a massive number of parameters.
Legal Transformer Models May Not Always Help
This work investigates the value of domain adaptive pre-training and language adapters in legal NLP tasks.
AdaGrid: Adaptive Grid Search for Link Prediction Training Objective
To mitigate these limitations, we propose Adaptive Grid Search (AdaGrid), which dynamically adjusts the edge message ratio during training.
An Efficient Active Learning Pipeline for Legal Text Classification
However, most AL strategies require a set of labeled samples to start with, which is expensive to acquire.
Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models
Recent transformer language models achieve outstanding results in many natural language processing (NLP) tasks.
Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages
Our evaluation across three distinct tasks (image-text retrieval, visual entailment, and natural language visual reasoning) demonstrates that this approach outperforms the state-of-the-art multilingual vision-language models without requiring large parallel corpora.
Multilingual Text Summarization on Financial Documents
The language models were fine-tuned on a financial document collection of three languages (English, Spanish, and Greek) and aim to identify the beginning of the summary narrative part of the document.
