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Found 11 papers, 2 papers with code
Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension
Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging tasks in natural language processing.
Tri-Attention: Explicit Context-Aware Attention Mechanism for Natural Language Processing
In natural language processing (NLP), the context of a word or sentence plays an essential role.
A Systematical Evaluation for Next-Basket Recommendation Algorithms
Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches.
Modeling Multi-interest News Sequence for News Recommendation
In MINS, a news encoder based on self-attention is devised on learn an informative embedding for each piece of news, and then a novel parallel interest network is devised to extract the potential multiple interests embedded in the news sequence in preparation for the subsequent next-news recommendations.
Modeling Complex Dependencies for Session-based Recommendations via Graph Neural Networks
Based on a strong assumption of adjacent dependency, any two adjacent items in a session are necessarily dependent in most GNN-based SBRs.
Aspect-driven User Preference and News Representation Learning for News Recommendation
News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news.
Intra-Correlation Encoding for Chinese Sentence Intention Matching
Sentence intention matching is vital for natural language understanding.
Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation
Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency with recent transactions that may also affect the next item.
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base
The techniques of our runs mainly make use of the word embeddings and the knowledge-based method.
QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings
This paper reports the details of our submissions in the task 1 of SemEval 2017.
