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:

Dividing sentences in chunks of words is a useful preprocessing step for parsing, information extraction and information retrieval. (Ramshaw and Marcus, 1995) have introduced a 'convenient' data representation for chunking by converting it to a tagging task. In this paper we will examine seven different data representations for the problem of recognizing noun phrase chunks. We will show that the data representation choice has a minor influence on chunking performance. However, equipped with the most suitable data representation, our memory-based learning chunker was able to improve the best published chunking results for a standard data set.
[["data representations", "recognizing noun phrase chunks", "USED-FOR"], ["data set", "memory-based learning chunker", "EVALUATE-FOR"], ["information extraction", "information retrieval", "CONJUNCTION"], ["Dividing sentences in chunks of words", "information retrieval", "USED-FOR"], ["data representation", "Dividing sentences in chunks of words", "USED-FOR"], ["Dividing sentences in chunks of words", "information extraction", "USED-FOR"], ["parsing", "information extraction", "CONJUNCTION"], ["data representation", "memory-based learning chunker", "USED-FOR"], ["Dividing sentences in chunks of words", "parsing", "USED-FOR"]]