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:

We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser.
[["linguistic generality", "parser", "FEATURE-OF"], ["approach", "statistical machine translation", "USED-FOR"], ["phrasal translation", "approach", "PART-OF"], ["target language word segmentation", "approach", "USED-FOR"], ["target language word segmentation", "unsupervised word alignment component", "CONJUNCTION"], ["unsupervised word alignment component", "approach", "USED-FOR"], ["phrasal SMT", "linguistic generality", "CONJUNCTION"], ["source-language dependency parser", "approach", "USED-FOR"], ["phrasal SMT", "parser", "USED-FOR"], ["tree-based ordering model", "SMT models", "CONJUNCTION"], ["SMT models", "approach", "USED-FOR"], ["source-language dependency parser", "target language word segmentation", "CONJUNCTION"], ["tree-based ordering model", "approach", "USED-FOR"], ["syntactic information", "approach", "PART-OF"], ["syntactic information", "phrasal translation", "CONJUNCTION"]]