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:

The following describes recent work on the Lincoln CSR system. Some new variations in semiphone modeling have been tested. A very simple improved duration model has reduced the error rate by about 10% in both triphone and semiphone systems. A new training strategy has been tested which, by itself, did not provide useful improvements but suggests that improvements can be obtained by a related rapid adaptation technique. Finally, the recognizer has been modified to use bigram back-off language models. The system was then transferred from the RM task to the ATIS CSR task and a limited number of development tests performed. Evaluation test results are presented for both the RM and ATIS CSR tasks.
{"Generic": [], "Material": [], "Method": ["triphone and semiphone systems", "recognizer", "bigram back-off language models", "duration model", "training strategy", "semiphone modeling", "rapid adaptation technique", "Lincoln CSR system"], "Metric": ["error rate"], "OtherScientificTerm": [], "Task": ["ATIS CSR task", "RM and ATIS CSR tasks", "RM task"]}