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PoKED: A Semi-Supervised System for Word Sense Disambiguation
Our proposed unsupervised language model is trained over unlabelled corpus, and then the pre-trained language model is capable of abstracting the surrounding context of polyseme instances in labeled corpus into context embeddings.
Semi-Supervised Clustering with Contrastive Learning for Discovering New Intents
To make DCSC fully utilize the limited known intents, we propose a two-stage training procedure for DCSC, in which DCSC will be trained on both labeled samples and unlabeled samples, and achieve better text representation and clustering performance.
Twitter Bot Detection Using Bidirectional Long Short-term Memory Neural Networks and Word Embeddings
Twitter is a web application playing dual roles of online social networking and micro-blogging.
Dual-FOFE-net Neural Models for Entity Linking with PageRank
Our neural linking models consist of three parts: a PageRank based candidate generation module, a dual-FOFE-net neural ranking model and a simple NIL entity clustering system.
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