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Start Over Creator Marek, Petr

2. Nelaserový zesilovač světla?

Creator:
Marek, Petr and Filip, Radim
Format:
print, bez média, and svazek
Type:
model:article and TEXT
Subject:
lasery, lasers, zesilovače, amplifiers, 6, and 535
Language:
Czech and English
Description:
We have proposed a probabilistic quantum amplifier, which is capable of amplifyzing weak light signals in such a way that phase of the light is not only preserved, but its resolution is improved as well! Surprisingly, this "noise-free" amplification is achieved by addition of incoherent thermal noise followed by a photon subtraction scheme. The amplifier, which has been already experimentally implemented, might in the future become a valuable tool for quantum metrology of quantum communication., Petr Marek, Radim Filip., and Obsahuje bibliografii
Rights:
http://creativecommons.org/licenses/by-nc-sa/4.0/ and policy:public

4. SumeCzech-NER

Creator:
Marek, Petr and Müller, Štěpán
Publisher:
Czech Technical University in Prague
Type:
text and corpus
Subject:
SumeCzech, named entity recognition, named entitity corpus, and summarization
Language:
Czech
Description:
SumeCzech-NER SumeCzech-NER contains named entity annotations of SumeCzech 1.0 (Straka et al. 2018, SumeCzech: Large Czech News-Based Summarization Dataset). Format The dataset is split into four files. Files are in jsonl format. There is one JSON object on each line of the file. The most important fields of JSON objects are: - dataset: train, dev, test, oodtest - ne_abstract: list of named entity annotations of article's abstract - ne_headline: list of named entity annotations of article's headline - ne_text: list of name entity annotations of article's text - url: article's URL that can be used to match article across SumeCzech and SumeCzech-NER Annotations We used SpaCy's NER model trained on CoNLL-based extended CNEC 2.0. The model achieved a 78.45 F-Score on the dataset's testing set. The annotations are in IOB2 format. The entity types are: Numbers in addresses, Geographical names, Institutions, Media names, Artifact names, Personal names, and Time expressions. Tokenization We used the following Python code for tokenization: from typing import List from nltk.tokenize import word_tokenize def tokenize(text: str) -> List[str]: for mark in ('.', ',', '?', '!', '-', '–', '/'): text = text.replace(mark, f' {mark} ') tokens = word_tokenize(text) return tokens
Rights:
Mozilla Public License 2.0, http://opensource.org/licenses/MPL-2.0, and PUB