You will be shown an abstract from a computer science research paper. Given this abstract, your task is to extract all unique entities with the following types:

- "Task": Applications, problems to solve, systems to construct. Examples include "information extraction", "machine reading system", "image segmentation".
- "Method": : Methods, models, systems to use, or tools, components of a system, frameworks. Examples include "language model", "CORENLP", "POS parser".
- "Metric": Metrics, measures, or entities that can express quality of a system / method. Examples include "F1", "BLEU", "Precision", "time complexity".
- "Material": Data, datasets, resources, Corpus, Knowledge base. Examples include "image data", "speech data", "stereo images", "CoNLL", "Wikipedia".
- "OtherScientificTerm": Phrases that are a scientific terms but do not fall into any of the above classes. Examples include "physical or geometric constraints", "qualitative prior knowledge", "tree kernel", "noise".
- "Generic": General terms or pronouns that may refer to a entity but are not themselves informative, often used as connection words. Examples include "model", "approach", "them".

Please return the output as a JSON object of the format: {"type1" : ["example_entity", ...], "type2" : ["example_entity", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. Entity types with no matching entities should be assigned an empty array [].

For instance, the output might look like: {"Task": ["speech recognition", ...], "Method": ["Conditional random field"], "Material": [], ...}.

Only output the JSON object and do not include any additional text.

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.
{"Generic": ["core modules"], "Material": ["verb final language", "missiles and chemical biological warfare", "Korean newspaper articles", "Korean"], "Method": ["CCLINC (Common Coalition Language System at Lincoln Laboratory)", "word sense disambiguation", "Korean-to-English machine translation system", "language neutral meaning representation", "knowledge-based automated acquisition of grammars"], "Metric": [], "OtherScientificTerm": ["overt case markers", "semantic frame"], "Task": ["word order generation", "parsing of Korean", "translation"]}