Word Sense Disambiguation
141 papers with code • 15 benchmarks • 15 datasets
The task of Word Sense Disambiguation (WSD) consists of associating words in context with their most suitable entry in a pre-defined sense inventory. The de-facto sense inventory for English in WSD is WordNet. For example, given the word “mouse” and the following sentence:
“A mouse consists of an object held in one's hand, with one or more buttons.”
we would assign “mouse” with its electronic device sense (the 4th sense in the WordNet sense inventory).
Libraries
Use these libraries to find Word Sense Disambiguation models and implementationsDatasets
Most implemented papers
Word-Class Embeddings for Multiclass Text Classification
Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambiguation, machine translation, and sentiment analysis, to name a few.
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation
Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples.
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE.
Can a Fruit Fly Learn Word Embeddings?
In this work we study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language processing (NLP) task.
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms
The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work.
Incorporating Word Sense Disambiguation in Neural Language Models
We present two supervised (pre-)training methods to incorporate gloss definitions from lexical resources into neural language models (LMs).
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories
We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
ST-MoE: Designing Stable and Transferable Sparse Expert Models
But advancing the state-of-the-art across a broad set of natural language tasks has been hindered by training instabilities and uncertain quality during fine-tuning.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.