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:

Past work of generating referring expressions mainly utilized attributes of objects and binary relations between objects. However, such an approach does not work well when there is no distinctive attribute among objects. To overcome this limitation, this paper proposes a method utilizing the perceptual groups of objects and n-ary relations among them. The key is to identify groups of objects that are naturally recognized by humans. We conducted psychological experiments with 42 subjects to collect referring expressions in such situations, and built a generation algorithm based on the results. The evaluation using another 23 subjects showed that the proposed method could effectively generate proper referring expressions.
{"Generic": [], "Material": [], "Method": ["generation algorithm"], "Metric": [], "OtherScientificTerm": ["n-ary relations", "binary relations", "referring expressions"], "Task": []}