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).
Benchmarks
These leaderboards are used to track progress in Keyword Extraction
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Datasets
MPQA Opinion Corpus
SemEval-2017 Task-10
Latest papers with no code
Keyword Extraction in Scientific Documents
The scientific publication output grows exponentially.
Unsupervised Learning Algorithms for Keyword Extraction in an Undergraduate Thesis
The amount of data managed in many academic institutions has increased in recent years, particularly in all the research work done by undergraduate students, who simply use empirical techniques for keyword selection, forgetting existing technical methods to assist their students in this process.
Born for Auto-Tagging: Faster and better with new objective functions
The strength of BAT converges faster and better than other SOTA models, as its 4-layer structure achieves the best F scores at 50 epochs.
Using virtual edges to extract keywords from texts modeled as complex networks
Detecting keywords in texts is important for many text mining applications.
Cross-view Brain Decoding
Also, the decoded representations are sufficiently detailed to enable high accuracy for cross-view-translation tasks with following pairwise accuracy: IC (78. 0), IT (83. 0), KE (83. 7) and SF (74. 5).
Semantic Similarity Computing for Scientific Academic Conferences fused with domain features
Aiming at the problem that the current general-purpose semantic text similarity calculation methods are difficult to use the semantic information of scientific academic conference data, a semantic similarity calculation algorithm for scientific academic conferences by fusion with domain features is proposed.
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?
We find that the pretrained models fine-tuned on a multilingual corpus covering languages that do not appear in the test set (i. e. in a zero-shot setting), consistently outscore unsupervised models in all six languages.
Open Domain Response Generation Guided by Retrieved Conversations
Open domain response generation is the task of creating a response givena user query in any topics/domain.
Coherence-Based Document Clustering
While these algorithms differ in their modeling approach, they have in common that hyperparameter optimization is difficult and is mainly achieved by maximizing the extracted topic coherence scores via a grid search.
Explainable Point-Based Document Visualizations
Popular techniques to construct data maps are t-SNE and UMAP.
