Keyword Extraction
25 papers with code • 3 benchmarks • 5 datasets
Keyword extraction is tasked with the automatic identification of terms that best describe the subject of a document (Source: Wikipedia).
Datasets
Latest papers
SpeechCLIP+: Self-supervised multi-task representation learning for speech via CLIP and speech-image data
Second, we propose a new hybrid architecture that merges the cascaded and parallel architectures of SpeechCLIP into a multi-task learning framework.
Task Oriented Conversational Modelling With Subjective Knowledge
Existing conversational models are handled by a database(DB) and API based systems.
Graph-based Semantical Extractive Text Analysis
The two important tasks to do this are keyword extraction and text summarization.
AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERT
Downstream training for keyword extractors is a lengthy process and requires a significant amount of data.
Large-Scale Bidirectional Training for Zero-Shot Image Captioning
However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning.
SiDi KWS: A Large-Scale Multilingual Dataset for Keyword Spotting
Keyword spotting (KWS) has become a hot topic in speech processing due to the rise of commercial applications based on voice command detection, such as voice assistants.
Study of keyword extraction techniques for Electric Double Layer Capacitor domain using text similarity indexes: An experimental analysis
This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert provided keywords from the Electric Double Layer Capacitor (EDLC) domain.
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document.
DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature
In this work, we present to the NLP community, and to the wider research community as a whole, an application for the diachronic analysis of research corpora.
Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction
Term weighting schemes are widely used in Natural Language Processing and Information Retrieval.