Word Embeddings
1106 papers with code • 0 benchmarks • 52 datasets
Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.
Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.
( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )
Benchmarks
These leaderboards are used to track progress in Word Embeddings
Datasets
ConceptNet
FrameNet
BookCorpus
WiC
BioASQ
WinoBias
PadChest
WikiMatrix
CELEX
Subtasks
Latest papers with no code
WordDecipher: Enhancing Digital Workspace Communication with Explainable AI for Non-native English Speakers
Non-native English speakers (NNES) face challenges in digital workspace communication (e. g., emails, Slack messages), often inadvertently translating expressions from their native languages, which can lead to awkward or incorrect usage.
Robust Concept Erasure Using Task Vectors
Finally, we show that Diverse Inversion enables us to apply a TV edit only to a subset of the model weights, enhancing the erasure capabilities while better maintaining the core functionality of the model.
PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian Tweets
Disambiguating the meaning of such terms might help the detection of misogyny.
The Shape of Word Embeddings: Recognizing Language Phylogenies through Topological Data Analysis
Word embeddings represent language vocabularies as clouds of $d$-dimensional points.
Natural Language, AI, and Quantum Computing in 2024: Research Ingredients and Directions in QNLP
Language processing is at the heart of current developments in artificial intelligence, and quantum computers are becoming available at the same time.
Fusion approaches for emotion recognition from speech using acoustic and text-based features
In this paper, we study different approaches for classifying emotions from speech using acoustic and text-based features.
A comparative analysis of embedding models for patent similarity
First, it compares the performance of different kinds of patent-specific pretrained embedding models, namely static word embeddings (such as word2vec and doc2vec models) and contextual word embeddings (such as transformers based models), on the task of patent similarity calculation.
An efficient domain-independent approach for supervised keyphrase extraction and ranking
We present a supervised learning approach for automatic extraction of keyphrases from single documents.
Empowering Segmentation Ability to Multi-modal Large Language Models
Multi-modal large language models (MLLMs) can understand image-language prompts and demonstrate impressive reasoning ability.
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction
To bridge this gap, we are the first to leverage linguistic features with a Seq2Seq PLM for OIE.
