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:

An entity-oriented approach to restricted-domain parsing is proposed. In this approach, the definitions of the structure and surface representation of domain entities are grouped together. Like semantic grammar, this allows easy exploitation of limited domain semantics. In addition, it facilitates fragmentary recognition and the use of multiple parsing strategies, and so is particularly useful for robust recognition of extra-grammatical input. Several advantages from the point of view of language definition are also noted. Representative samples from an entity-oriented language definition are presented, along with a control structure for an entity-oriented parser, some parsing strategies that use the control structure, and worked examples of parses. A parser incorporating the control structure and the parsing strategies is currently under implementation.
{"Generic": ["this"], "Material": [], "Method": ["entity-oriented parser", "parser", "parsing strategies", "entity-oriented approach", "multiple parsing strategies", "semantic grammar"], "Metric": [], "OtherScientificTerm": ["entity-oriented language definition", "limited domain semantics", "structure and surface representation of domain entities", "control structure", "recognition of extra-grammatical input"], "Task": ["restricted-domain parsing", "fragmentary recognition"]}