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:

One of the major problems one is faced with when decomposing words into their constituent parts is ambiguity: the generation of multiple analyses for one input word, many of which are implausible. In order to deal with ambiguity, the MORphological PArser MORPA is provided with a probabilistic context-free grammar (PCFG), i.e. it combines a 'conventional' context-free morphological grammar to filter out ungrammatical segmentations with a probability-based scoring function which determines the likelihood of each successful parse. Consequently, remaining analyses can be ordered along a scale of plausibility. Test performance data will show that a PCFG yields good results in morphological parsing. MORPA is a fully implemented parser developed for use in a text-to-speech conversion system.
[["probability-based scoring function", "parse", "USED-FOR"], ["\u0027conventional\u0027 context-free morphological grammar", "MORphological PArser MORPA", "USED-FOR"], ["\u0027conventional\u0027 context-free morphological grammar", "ungrammatical segmentations", "USED-FOR"], ["MORphological PArser MORPA", "parser", "HYPONYM-OF"], ["parser", "text-to-speech conversion system", "USED-FOR"], ["probability-based scoring function", "\u0027conventional\u0027 context-free morphological grammar", "CONJUNCTION"], ["probabilistic context-free grammar (PCFG)", "morphological parsing", "USED-FOR"], ["MORphological PArser MORPA", "ambiguity", "USED-FOR"], ["probability-based scoring function", "MORphological PArser MORPA", "USED-FOR"], ["MORphological PArser MORPA", "text-to-speech conversion system", "USED-FOR"], ["probabilistic context-free grammar (PCFG)", "MORphological PArser MORPA", "USED-FOR"]]