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:

At MIT Lincoln Laboratory, we have been developing a Korean-to-English machine translation system CCLINC (Common Coalition Language System at Lincoln Laboratory). The CCLINC Korean-to-English translation system consists of two core modules, language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame. The key features of the system include: (i) Robust efficient parsing of Korean (a verb final language with overt case markers, relatively free word order, and frequent omissions of arguments). (ii) High quality translation via word sense disambiguation and accurate word order generation of the target language. (iii) Rapid system development and porting to new domains via knowledge-based automated acquisition of grammars. Having been trained on Korean newspaper articles on missiles and chemical biological warfare, the system produces the translation output sufficient for content understanding of the original document.
[["core modules", "CCLINC (Common Coalition Language System at Lincoln Laboratory)", "PART-OF"], ["word sense disambiguation", "word order generation", "CONJUNCTION"], ["Korean newspaper articles", "CCLINC (Common Coalition Language System at Lincoln Laboratory)", "USED-FOR"], ["overt case markers", "verb final language", "FEATURE-OF"], ["language neutral meaning representation", "core modules", "USED-FOR"], ["word order generation", "translation", "USED-FOR"], ["word sense disambiguation", "translation", "USED-FOR"], ["CCLINC (Common Coalition Language System at Lincoln Laboratory)", "Korean-to-English machine translation system", "HYPONYM-OF"], ["semantic frame", "language neutral meaning representation", "HYPONYM-OF"], ["Korean", "verb final language", "HYPONYM-OF"], ["missiles and chemical biological warfare", "Korean newspaper articles", "FEATURE-OF"]]