Below is an abstract from a computer science research paper. Given this abstract, your task is to extract all unique relationships between entities of the following types:

- "USED-FOR": For instance, "Our method models user proficiency" -> ["method", "user proficiency", "USED-FOR"].
- "EVALUATE-FOR": For instance, "We evaluate our method using F1-measure" -> ["F1-measure", "method", "EVALUATE-FOR"].
- "FEATURE-OF": For instance, "prior knowledge of the model" -> ["prior knowledge", "model", "FEATURE-OF"].
- "HYPONYM-OF": For instance, "TUIT is a software library" -> ["TUIT", "software library", "HYPONYM-OF"].
- "PART-OF": For instance, "We incorporate NLU module into the system." -> ["NLU module", "system", "PART-OF"].
- "COMPARE": For instance, "Unlike the quantitative prior, the qualitative prior is often ignored" -> ["quantitative prior", "qualitative prior", "COMPARE"].
- "CONJUNCTION": For instance: "obtained from human expert or knowledge base" -> ["human expert", "knowledge base", "CONJUNCTION"].

Format your output as a json array. Each entry in the array should express a single relation, formatted as ["<Entity_A>", "<Entity_B>", "<RELATION_A_B>"]. An example output might look like:

[["neural networks", "classification", "USED-FOR"], ["neuron", "neural network", "PART-OF"]].

If no relations are found, please return an empty array [].

Abstract:

Along with the increasing requirements, the hash-tag recommendation task for microblogs has been receiving considerable attention in recent years. Various researchers have studied the problem from different aspects. However, most of these methods usually need handcrafted features. Motivated by the successful use of convolutional neural networks (CNNs) for many natural language processing tasks, in this paper, we adopt CNNs to perform the hashtag recommendation problem. To incorporate the trigger words whose effectiveness have been experimentally evaluated in several previous works, we propose a novel architecture with an attention mechanism. The results of experiments on the data collected from a real world microblogging service demonstrated that the proposed model outperforms state-of-the-art methods. By incorporating trigger words into the consideration, the relative improvement of the proposed method over the state-of-the-art method is around 9.4% in the F1-score.
[["convolutional neural networks (CNNs)", "natural language processing tasks", "USED-FOR"], ["hash-tag recommendation task", "microblogs", "USED-FOR"], ["F1-score", "convolutional neural networks (CNNs)", "EVALUATE-FOR"], ["architecture", "convolutional neural networks (CNNs)", "COMPARE"], ["architecture", "trigger words", "USED-FOR"], ["attention mechanism", "architecture", "FEATURE-OF"], ["architecture", "state-of-the-art methods", "COMPARE"], ["data", "architecture", "EVALUATE-FOR"]]