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:

Information extraction techniques automatically create structured databases from unstructured data sources, such as the Web or newswire documents. Despite the successes of these systems, accuracy will always be imperfect. For many reasons, it is highly desirable to accurately estimate the confidence the system has in the correctness of each extracted field. The information extraction system we evaluate is based on a linear-chain conditional random field (CRF), a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlapping features of the input in a Markov model. We implement several techniques to estimate the confidence of both extracted fields and entire multi-field records, obtaining an average precision of 98% for retrieving correct fields and 87% for multi-field records.
[["newswire documents", "unstructured data sources", "HYPONYM-OF"], ["average precision", "techniques", "EVALUATE-FOR"], ["arbitrary, overlapping features", "Markov model", "PART-OF"], ["extracted fields", "multi-field records", "CONJUNCTION"], ["linear-chain conditional random field (CRF)", "Information extraction techniques", "USED-FOR"], ["unstructured data sources", "Information extraction techniques", "USED-FOR"], ["probabilistic model", "arbitrary, overlapping features", "USED-FOR"], ["linear-chain conditional random field (CRF)", "probabilistic model", "HYPONYM-OF"], ["probabilistic model", "information extraction tasks", "USED-FOR"], ["Web", "unstructured data sources", "HYPONYM-OF"], ["accuracy", "Information extraction techniques", "EVALUATE-FOR"], ["Information extraction techniques", "structured databases", "USED-FOR"], ["Web", "newswire documents", "CONJUNCTION"], ["arbitrary, overlapping features", "input", "FEATURE-OF"]]