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:

Information extraction techniques automatically create structured databases from unstructured data sources, such as the Web or newswire documents. Despite the successes of these systems, accuracy will always be imperfect. For many reasons, it is highly desirable to accurately estimate the confidence the system has in the correctness of each extracted field. The information extraction system we evaluate is based on a linear-chain conditional random field (CRF), a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlapping features of the input in a Markov model. We implement several techniques to estimate the confidence of both extracted fields and entire multi-field records, obtaining an average precision of 98% for retrieving correct fields and 87% for multi-field records.
{"Generic": ["techniques", "input", "extracted fields"], "Material": ["Web", "multi-field records", "unstructured data sources", "newswire documents", "structured databases"], "Method": ["linear-chain conditional random field (CRF)", "Markov model", "Information extraction techniques", "probabilistic model"], "Metric": ["average precision", "accuracy"], "OtherScientificTerm": ["arbitrary , overlapping features"], "Task": ["information extraction tasks"]}