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 present the first application of the head-driven statistical parsing model of Collins (1999) as a simultaneous language model and parser for large-vocabulary speech recognition. The model is adapted to an online left to right chart-parser for word lattices, integrating acoustic, n-gram, and parser probabilities. The parser uses structural and lexical dependencies not considered by n-gram models, conditioning recognition on more linguistically-grounded relationships. Experiments on the Wall Street Journal treebank and lattice corpora show word error rates competitive with the standard n-gram language model while extracting additional structural information useful for speech understanding.
[["online left to right chart-parser", "large-vocabulary speech recognition", "USED-FOR"], ["simultaneous language model", "large-vocabulary speech recognition", "USED-FOR"], ["Wall Street Journal treebank", "n-gram language model", "EVALUATE-FOR"], ["word error rates", "n-gram language model", "EVALUATE-FOR"], ["structural information", "speech understanding", "USED-FOR"], ["head-driven statistical parsing model", "simultaneous language model", "USED-FOR"], ["head-driven statistical parsing model", "online left to right chart-parser", "USED-FOR"], ["lattice corpora", "n-gram language model", "EVALUATE-FOR"], ["online left to right chart-parser", "word lattices", "USED-FOR"], ["Wall Street Journal treebank", "lattice corpora", "CONJUNCTION"], ["simultaneous language model", "online left to right chart-parser", "CONJUNCTION"], ["acoustic, n-gram, and parser probabilities", "online left to right chart-parser", "PART-OF"], ["structural and lexical dependencies", "online left to right chart-parser", "USED-FOR"]]