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Academic literature on the topic 'Annotated score'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Annotated score"

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Constraints, Generative. "Break Up Variations: An Annotated Score." Performance Philosophy 4, no. 2 (February 1, 2019): 591–600. http://dx.doi.org/10.21476/pp.2019.42227.

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Break Up Variations is an annotated score by means of which we consider the document as a break-up from — and with — the thinking of performance. We explore the formal categories of page-based and stage-based scores and documentations of performance, asserting the simultaneity of the document and its performance in their mutual departures, theorising the break-up as a form of relation, not as its absence. As a committee of interdisciplinary researchers and practitioners, we consider annotation in terms of affective and theoretical responses to each other’s subject positions.Break Up Variations relates to the problems particular to working in groups: the challenges of collaboration, the disagreements and community-led conflict resolutions, the difficulties with acting professionally, and the desires to keep working together, despite it all. We ask the following questions of each other and ourselves: What are the strategies that art, science, politics and theory might offer each other for navigating — possibly circumventing — the demise of relationships? If the working relationship breaks down, could the end of the group be considered a constitutive aspect of that group? We consider these questions to be about institutions as much as they are about interdependence on personal and planetary scales. Riffing on ideas about romantic break-ups, political dissolutions and ecological collapse, Break Up Variations considers the possibility that an end to a dream of symbiotic life is exactly what makes that dream possible and important.
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Hoffmann, Martin A., Louis-Félix Nothias, Marcus Ludwig, Markus Fleischauer, Emily C. Gentry, Michael Witting, Pieter C. Dorrestein, Kai Dührkop, and Sebastian Böcker. "High-confidence structural annotation of metabolites absent from spectral libraries." Nature Biotechnology 40, no. 3 (October 14, 2021): 411–21. http://dx.doi.org/10.1038/s41587-021-01045-9.

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AbstractUntargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries.
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Hentschel, Johannes, Markus Neuwirth, and Martin Rohrmeier. "The Annotated Mozart Sonatas: Score, Harmony, and Cadence." Transactions of the International Society for Music Information Retrieval 4, no. 1 (2021): 67–80. http://dx.doi.org/10.5334/tismir.63.

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Kors, Jan A., Simon Clematide, Saber A. Akhondi, Erik M. van Mulligen, and Dietrich Rebholz-Schuhmann. "A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC." Journal of the American Medical Informatics Association 22, no. 5 (May 5, 2015): 948–56. http://dx.doi.org/10.1093/jamia/ocv037.

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Abstract Objective To create a multilingual gold-standard corpus for biomedical concept recognition. Materials and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations. Results The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language. Discussion The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques. Conclusion To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotated.
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ElNahass, Yasser H., Hossam K. Mahmoud, Mervat M. Mattar, Omar A. Fahmy, Mohamed A. Samra, Raafat M. Abdelfattah, Fatma A. ElRefaey, et al. "MPN10 score and survival of molecularly annotated myeloproliferative neoplasm patients." Leukemia & Lymphoma 59, no. 4 (August 22, 2017): 844–54. http://dx.doi.org/10.1080/10428194.2017.1365852.

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Mohapatra, Nilamadhaba, Namrata Sarraf, and Swapna sarit Sahu. "Domain based Chunking." International Journal on Natural Language Computing 10, no. 04 (August 30, 2021): 1–14. http://dx.doi.org/10.5121/ijnlc.2021.10401.

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Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
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Jacobs, Gilles, and Véronique Hoste. "SENTiVENT: enabling supervised information extraction of company-specific events in economic and financial news." Language Resources and Evaluation 56, no. 1 (October 8, 2021): 225–57. http://dx.doi.org/10.1007/s10579-021-09562-4.

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AbstractWe present SENTiVENT, a corpus of fine-grained company-specific events in English economic news articles. The domain of event processing is highly productive and various general domain, fine-grained event extraction corpora are freely available but economically-focused resources are lacking. This work fills a large need for a manually annotated dataset for economic and financial text mining applications. A representative corpus of business news is crawled and an annotation scheme developed with an iteratively refined economic event typology. The annotations are compatible with benchmark datasets (ACE/ERE) so state-of-the-art event extraction systems can be readily applied. This results in a gold-standard dataset annotated with event triggers, participant arguments, event co-reference, and event attributes such as type, subtype, negation, and modality. An adjudicated reference test set is created for use in annotator and system evaluation. Agreement scores are substantial and annotator performance adequate, indicating that the annotation scheme produces consistent event annotations of high quality. In an event detection pilot study, satisfactory results were obtained with a macro-averaged $$F_1$$ F 1 -score of $$59\%$$ 59 % validating the dataset for machine learning purposes. This dataset thus provides a rich resource on events as training data for supervised machine learning for economic and financial applications. The dataset and related source code is made available at https://osf.io/8jec2/.
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Dari, Retno Wulan, Suvi Akhiriyah, Eva Rahmawati, Him'mawan Adi Nugroho, and Twin Dyah Martiana. "Students’ ability in writing annotated bibliography: Teaching critical writing." EnJourMe (English Journal of Merdeka) : Culture, Language, and Teaching of English 7, no. 2 (January 1, 2023): 264–74. http://dx.doi.org/10.26905/enjourme.v7i2.9034.

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Annotated bibliographies are used in various situations. Annotated bibliography is a short-annotated list of sources that summarizes, evaluates, and states source relevance. The ability to write annotated bibliography shows one’s ability to find the right information for writing research article. This paper aims to see how the ability of second year students majoring in English is in making annotated bibliography. This research is qualitative research using data obtained from 42 students who assigned on writing annotated bibliography. Data were explained descriptively from the score mean and percentage using Turnitin then scaled them using 4-scale Linkert Scale. Results showed that of five variables, quality of sources, annotations content, overall quality were good, while accuracy and annotations structure variables were adequate. For Turnitin check, 69.05% showed good and very good quality, while only 9.52% them showed poor quality. Finally, it isindicated that most of the second-year subjects consider as having good standard abilities categorized in writing annotated bibliography. DOI: 10.26905/enjourme.v7i2.8966
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Sarker, Abeed, Maksim Belousov, Jasper Friedrichs, Kai Hakala, Svetlana Kiritchenko, Farrokh Mehryary, Sifei Han, et al. "Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task." Journal of the American Medical Informatics Association 25, no. 10 (October 1, 2018): 1274–83. http://dx.doi.org/10.1093/jamia/ocy114.

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AbstractObjectiveWe executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data.Materials and MethodsWe organized 3 independent subtasks: automatic classification of self-reports of 1) adverse drug reactions (ADRs) and 2) medication consumption, from medication-mentioning tweets, and 3) normalization of ADR expressions. Training data consisted of 15 717 annotated tweets for (1), 10 260 for (2), and 6650 ADR phrases and identifiers for (3); and exhibited typical properties of social-media-based health-related texts. Systems were evaluated using 9961, 7513, and 2500 instances for the 3 subtasks, respectively. We evaluated performances of classes of methods and ensembles of system combinations following the shared tasks.ResultsAmong 55 system runs, the best system scores for the 3 subtasks were 0.435 (ADR class F1-score) for subtask-1, 0.693 (micro-averaged F1-score over two classes) for subtask-2, and 88.5% (accuracy) for subtask-3. Ensembles of system combinations obtained best scores of 0.476, 0.702, and 88.7%, outperforming individual systems.DiscussionAmong individual systems, support vector machines and convolutional neural networks showed high performance. Performance gains achieved by ensembles of system combinations suggest that such strategies may be suitable for operational systems relying on difficult text classification tasks (eg, subtask-1).ConclusionsData imbalance and lack of context remain challenges for natural language processing of social media text. Annotated data from the shared task have been made available as reference standards for future studies (http://dx.doi.org/10.17632/rxwfb3tysd.1).
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Mumtaz, Raabia, and Muhammad Abdul Qadir. "CustNER." International Journal on Semantic Web and Information Systems 16, no. 3 (July 2020): 110–27. http://dx.doi.org/10.4018/ijswis.2020070107.

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This article describes CustNER: a system for named-entity recognition (NER) of person, location, and organization. Realizing the incorrect annotations of existing NER, four categories of false negatives have been identified. The NEs not annotated contain nationalities, have corresponding resource in DBpedia, are acronyms of other NEs. A rule-based system, CustNER, has been proposed that utilizes existing NERs and DBpedia knowledge base. CustNER has been trained on the open knowledge extraction (OKE) challenge 2017 dataset and evaluated on OKE and CoNLL03 (Conference on Natural Language Learning) datasets. The OKE dataset has also been annotated with the three types. Evaluation results show that CustNER outperforms existing NERs with F score 12.4% better than Stanford NER and 3.1% better than Illinois NER. On another standard evaluation dataset for which the system is not trained, the CoNLL03 dataset, CustNER gives results comparable to existing systems with F score 3.9% better than Stanford NER, though Illinois NER F score is 1.3% better than CustNER.
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Dissertations / Theses on the topic "Annotated score"

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CESTINO, GIOVANNI. "'USED SCORES'. LINEE TEORICHE E OPERATIVE PER L¿INDAGINE DEL RAPPORTO TRA ESECUTORI E MATERIALI PERFORMATIVI." Doctoral thesis, Università degli Studi di Milano, 2019. http://hdl.handle.net/2434/697450.

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This study provides a theoretical and analytical framework to investigate the relationship between performers and their materials (scores, parts, etc.) in the Western art music context. Whilst the literature has focused on annotation practice – and thus on the informational content of scores – the approach here is extended to the practices of reading and the material alteration of the performative support. Textual artifacts are thus considered both for their role in the performative process, and for the cultural affordance of the content they convey. Combining recent anthropological perspectives on creative processes with textual criticism methods, this study delves into each of the three practices with a constant reference to numerous sources, belonging to relevant mid-XX century performers who had a unique relationship with their materials. In order to derive some analytical tools – potentially suitable for future case studies – the dissertation focuses on an example: the materials of Berg's Lyrische Suite employed by the LaSalle Quartet and its founder, Walter Levin. On one hand, this investigation reaffirms the relevance of such sources for different scholarly perspectives, including reception history and performance practice. On the other, this study claims for a reconsideration of text-performance dichotomy by relocating it in performers’ perspectives. Lastly, it suggests a multi-disciplinary approach to textual artifacts which aims to fill the gap between opposite approaches like musical philology and performance studies, towards an 'anthropology of the musical text'.
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DI, NUOVO ELISA. "VALICO-UD: annotating an Italian learner corpus." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1095350.

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Previous work on learner language has highlighted the importance of having annotated resources to describe the development of interlanguage. Despite this, few learner resources, mainly for English L2, feature error and syntactic annotation. This thesis describes the development of a novel parallel learner Italian treebank, VALICO-UD. Its name suggests two main points: where the data comes from—i.e. the corpus VALICO, a collection of non-native Italian texts elicited by comic strips—and what formalism is used for linguistic annotation—i.e. Universal Dependencies (UD) formalism. It is a parallel treebank because the resource provides for each learner sentence (LS) a target hypothesis (TH) (i.e., parallel corrected version written by an Italian native speaker) which is in turn annotated in UD. We developed this treebank to be exploitable for interlanguage research and comparable with the resources employed in Natural Language Processing tasks such as Native Language Identification or Grammatical Error Identification and Correction. VALICO-UD is composed of 237 texts written by English, French, German and Spanish native speakers, which correspond to 2,234 LSs, each associated with a single TH. While all LSs and THs were automatically annotated using UDPipe, only a portion of the treebank made of 398 LSs plus correspondent THs has been manually corrected and released in May 2021 in the UD repository. This core section features also an explicit XML-based annotation of the errors occurring in each sentence. Thus, the treebank is currently organized in two sections: the core gold standard—comprising 398 LSs and their correspondent THs—and the silver standard—consisting of 1,836 LSs and their correspondent THs. In order to contribute to the computational investigation about the peculiar type of texts included in VALICO-UD, this thesis describes the annotation schema of the resource, provides some preliminary tests about the performance of UDPipe models on this treebank, reports on inter-annotator agreement results for both error and linguistic annotation, and suggests some possible applications.
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Ballew, Cranford Braxton. "An annotated guide to electroacoustic solo double bass repertoire published scores to 2005 /." 2007. http://purl.galileo.usg.edu/uga%5Fetd/ballew%5Fcranford%5Fb%5F200705%5Fdma.

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Books on the topic "Annotated score"

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Guest, Ann Hutchinson. Nijinsky's Faune restored: A study of Vaslav Nijinsky's 1915 dance score : L'après-midi d'un faune and his dance notation system : revealed, translated into labanotation and annotated. Philadelphia: Gordon and Breach, 1991.

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Political corruption: Scope and resources : an annotated bibliography. New York: Garland Pub., 1990.

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Aguilar, Narciso M. Remedial law: Basic provisions annotated : scope, the rules of civil procedure, with amendments as of 2007. 2nd ed. Quezon City, Philippines: Central Book Supply, 2009.

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Aguilar, Narciso M. Remedial law: Basic provisions annotated : scope, the rules of civil procedure, with amendments as of 2007. 2nd ed. Quezon City, Philippines: Central Book Supply, 2009.

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Aguilar, Narciso M. Remedial law: Basic provisions annotated : scope, the rules of civil procedure, with amendments as of 2007. 2nd ed. Quezon City, Philippines: Central Book Supply, 2009.

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Aguilar, Narciso M. Remedial law: Basic provisions annotated : scope, the rules of civil procedure, with amendments as of 2007. 2nd ed. Quezon City, Philippines: Central Book Supply, 2009.

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Smithsonian Institution. The Smithsonian Institution African Mammal Project (1961-1972): An annotated gazetteer of collecting localities and summary of its taxonomic and geographic scope. Washington, D.C: Smithsonian Institution, 2008.

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Court, Philippines Supreme. Remedial law: Basic provisions annotated : scope, Ordinary civil actions, rules 1-56, Provisional remedies, rules 57-61, Special civil actions, rules 62-71. Sta. [i.e. Santa] Cruz, Manila: Central Book Supply, 2004.

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Lyres, Legends, and Lullabies: An Annotated Score Collection. Night Muse Press, 2020.

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UniAdmissions and Rohan Agarwal. The Ultimate BMAT Guide : 800 Practice Questions: Fully Worked Solutions, Time Saving Techniques, Score Boosting Strategies, 12 Annotated Essays, 2018 Edition UniAdmissions. RAR Medical Services, 2017.

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Book chapters on the topic "Annotated score"

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"ANNOTATED SCORE." In Inside Beethoven’s Quartets, 231–52. Harvard University Press, 2008. http://dx.doi.org/10.2307/j.ctv1dv0tkv.16.

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"ANNOTATED SCORE." In Inside Beethoven’s Quartets, 71–92. Harvard University Press, 2008. http://dx.doi.org/10.2307/j.ctv1dv0tkv.8.

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"ANNOTATED SCORE." In Inside Beethoven’s Quartets, 147–80. Harvard University Press, 2008. http://dx.doi.org/10.2307/j.ctv1dv0tkv.12.

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Barbu, Eduard, Kadri Muischnek, and Linda Freienthal. "A Study in Estonian Pronominal Coreference Resolution." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200595.

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The first study for Estonian pronominal coreference resolution using machine learning is presented. Appropriate machine learning algorithms and techniques for balancing the data are tested on a human-annotated corpus. The results are encouraging, showing an F-score comparable with the results obtained for English before the advent of deep neural networks.
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Nguyen, Nhung Thi-Hong, Phuong Phan-Dieu Ha, Luan Thanh Nguyen, Kiet Van Nguyen, and Ngan Luu-Thuy Nguyen. "Vietnamese Complaint Detection on E-Commerce Websites." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210058.

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Customer product reviews play a role in improving the quality of products and services for business organizations or their brands. Complaining is an attitude that expresses dissatisfaction with an event or a product not meeting customer expectations. In this paper, we build a Vietnamese Open-domain Complaint Detection dataset (UIT-ViOCD), including 5,485 human-annotated reviews on four categories about product reviews on e-commerce sites. After the data collection phase, we proceed to the annotation task and achieve the inter-annotator agreement (Am) of 87%. Then, we present an extensive methodology for the research purposes and achieve 92.16% by F1-score for identifying complaints. With the results, in future, we aim to build a system for open-domain complaint detection on E-commerce websites.
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Lin, Fengyang, Hao Liu, Paul Moon, and Chunhua Weng. "A Sample Size Extractor for RCT Reports." In MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220151.

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Sample size is an important indicator of the power of randomized controlled trials (RCTs). In this paper, we designed a total sample size extractor using a combination of syntactic and machine learning methods, and evaluated it on 300 Covid-19 abstracts (Covid-Set) and 100 generic RCT abstracts (General-Set). To improve the performance, we applied transfer learning from a large public corpus of annotated abstracts. We achieved an average F1 score of 0.73 on the Covid-Set testing set, and 0.60 on the General-Set using exact matches. The F1 scores for loose matches on both datasets were over 0.74. Compared with the state-of-the-art tool, our extractor reports total sample sizes directly and improved F1 scores by at least 4% without transfer learning. We demonstrated that transfer learning improved the sample size extraction accuracy and minimized human labor on annotations.
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Jagger, Jon, Nigel Perry, and Peter Sestoft. "Scope." In Annotated C# Standard, 2. Elsevier, 2007. http://dx.doi.org/10.1016/b978-012372511-0.50007-2.

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Oliveira, Hilário, Rinaldo Lima, João Gomes, Fred Freitas, Rafael Dueire Lins, Steven J. Simske, and Marcelo Riss. "A Web-Based Method for Ontology Population." In Mobile Computing and Wireless Networks, 565–86. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8751-6.ch025.

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The Semantic Web, proposed by Berners-Lee, aims to make explicit the meaning of the data available on the Internet, making it possible for Web data to be processed both by people and intelligent agents. The Semantic Web requires Web data to be semantically classified and annotated with some structured representation of knowledge, such as ontologies. This chapter proposes an unsupervised, domain-independent method for extracting instances of ontological classes from unstructured data sources available on the World Wide Web. Starting with an initial set of linguistic patterns, a confidence-weighted score measure is presented integrating distinct measures and heuristics to rank candidate instances extracted from the Web. The results of several experiments are discussed achieving very encouraging results, which demonstrate the feasibility of the proposed method for automatic ontology population.
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Oliveira, Hilário, Rinaldo Lima, João Gomes, Fred Freitas, Rafael Dueire Lins, Steven J. Simske, and Marcelo Riss. "A Web-Based Method for Ontology Population." In Advances in E-Business Research, 211–33. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7262-8.ch010.

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The Semantic Web, proposed by Berners-Lee, aims to make explicit the meaning of the data available on the Internet, making it possible for Web data to be processed both by people and intelligent agents. The Semantic Web requires Web data to be semantically classified and annotated with some structured representation of knowledge, such as ontologies. This chapter proposes an unsupervised, domain-independent method for extracting instances of ontological classes from unstructured data sources available on the World Wide Web. Starting with an initial set of linguistic patterns, a confidence-weighted score measure is presented integrating distinct measures and heuristics to rank candidate instances extracted from the Web. The results of several experiments are discussed achieving very encouraging results, which demonstrate the feasibility of the proposed method for automatic ontology population.
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Richards, Mark. "Estimating prognosis: biomarkers." In ESC CardioMed, 1791–97. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198784906.003.0412.

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Risk stratification informs the management of heart failure (HF). A range of clinical variables are associated with outcome in HF and have been incorporated into risk calculators which perform adequately at a population level, especially for those with more severe or recently decompensated HF. Their performance in predicting individual patient outcomes among the ambulant community-based populations with chronic HF is less assured. Risk score performance is enhanced by incorporation of selected circulating biomarkers. The cardiac natriuretic peptides, particularly B-type natriuretic peptide (BNP) and N-terminal proBNP (NT-proBNP), are independently predictive of mortality in both acute and chronic HF. Risk at a given level of BNP is similar regardless of left ventricular ejection fraction. The prognostic performance of NT-proBNP and BNP enriches therapeutic trials for clinical events. Trials of marker-guided therapy in HF consistently indicate that lowering plasma NT-proBNP (preferably to <1000 pg/mL) is associated with improved outcomes. The cardiac natriuretic peptides may be combined with other markers including mid-regional pro-adrenomedullin, ST2, growth/differentiation factor 15, and cardiac troponin to refine risk stratification. There is a need for an improved, independently validated, risk score calculator derived from a large well-annotated HF population incorporating both key clinical predictors and one or more circulating biomarkers.
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Conference papers on the topic "Annotated score"

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Nakahira, Katsuko T., Miki Akahane, and Yukiko Fukami. "Awareness promoting learning design of sing-along piano playing - the role of annotated musical score and multimedia contents." In 2011 3rd International Conference on Awareness Science and Technology (iCAST). IEEE, 2011. http://dx.doi.org/10.1109/icawst.2011.6163103.

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Gordeev, D. I., A. A. Davletov, A. I. Rey, G. R. Akzhigitova, and G. A. Geymbukh. "RELATION EXTRACTION DATASET FOR THE RUSSIAN." In International Conference on Computational Linguistics and Intellectual Technologies "Dialogue". Russian State University for the Humanities, 2020. http://dx.doi.org/10.28995/2075-7182-2020-19-348-360.

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There are few existing relation extraction datasets for the Russian language and they contain a rather small number of examples. Thus, we decided to create a new Ontonotes-based named entities and relation extraction sentence-level dataset called RURED. The dataset contains more than 500 annotated texts and more than 5,000 labelled relations. We also publish baseline models for relation extraction and named entity recognition trained on the dataset. Our models achieve 0.85 for named entity recognition and 0.78 for relation extraction in F1-score.
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Yu Min Kang, Kristin M. Gunnarsdottir, Matthew S. D. Kerr, Rachel M. E. Salas, Joshua Ewen, Richard Allen, Charlene Gamaldo, and Sridevi V. Sarma. "To Score or Not to Score? A look at the distinguishing power of micro EEG analysis on an annotated sample of PSG studies conducted in an HIV cohort." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7319912.

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Schimbinschi, Florin, Christian Walder, Sarah M. Erfani, and James Bailey. "SynthNet: Learning to Synthesize Music End-to-End." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/467.

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We consider the problem of learning a mapping directly from annotated music to waveforms, bypassing traditional single note synthesis. We propose a specific architecture based on WaveNet, a convolutional autoregressive generative model designed for text to speech. We investigate the representations learned by these models on music and concludethat mappings between musical notes and the instrument timbre can be learned directly from the raw audio coupled with the musical score, in binary piano roll format.Our model requires minimal training data (9 minutes), is substantially better in quality and converges 6 times faster in comparison to strong baselines in the form of powerful text to speech models.The quality of the generated waveforms (generation accuracy) is sufficiently high,that they are almost identical to the ground truth.Our evaluations are based on both the RMSE of the Constant-Q transform, and mean opinion scores from human subjects.We validate our work using 7 distinct synthetic instrument timbres, real cello music and also provide visualizations and links to all generated audio.
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Horbach, Andrea, Ronja Laarmann-Quante, Lucas Liebenow, Thorben Jansen, Stefan Keller, Jennifer Meyer, Torsten Zesch, and Johanna Fleckenstein. "Bringing Automatic Scoring into the Classroom – Measuring the Impact of Automated Analytic Feedback on Student Writing Performance." In 11th Workshop on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL 2022). Linköping University Electronic Press, 2022. http://dx.doi.org/10.3384/ecp190008.

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While many methods for automatically scoring student writings have been proposed, few studies have inquired whether such scores constitute effective feedback improving learners’ writing quality. In this paper, we use an EFL email dataset annotated according to five analytic assessment criteria to train a classifier for each criterion, reaching human-machine agreement values (kappa) between .35 and .87. We then perform an intervention study with 112 lower secondary students in which participants in the feedback condition received stepwise automatic feedback for each criterion while students in the control group received only a description of the respective scoring criterion. We manually and automatically score the resulting revisions to measure the effect of automated feedback and find that students in the feedback condition improved more than in the control group for 2 out of 5 criteria. Our results are encouraging as they show that even imperfect automated feedback can be successfully used in the classroom.
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Uzkent, Burak, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, and Stefano Ermon. "Learning to Interpret Satellite Images using Wikipedia." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/502.

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Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data. To overcome this limitation, we construct a novel dataset called WikiSatNet by pairing geo-referenced Wikipedia articles with satellite imagery of their corresponding locations. We then propose two strategies to learn representations of satellite images by predicting properties of the corresponding articles from the images. Leveraging this new multi-modal dataset, we can drastically reduce the quantity of human-annotated labels and time required for downstream tasks. On the recently released fMoW dataset, our pre-training strategies can boost the performance of a model pre-trained on ImageNet by up to 4.5% in F1 score.
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Kotelnikov, Evgeny, Natalia Loukachevitch, Irina Nikishina, and Alexander Panchenko. "RuArg-2022: Argument Mining Evaluation." In Dialogue. RSUH, 2022. http://dx.doi.org/10.28995/2075-7182-2022-21-333-348.

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Argumentation analysis is a field of computational linguistics that studies methods for extracting arguments from texts and the relationships between them, as well as building argumentation structure of texts. This paper is a report of the organizers on the first competition of argumentation analysis systems dealing with Russian language texts within the framework of the Dialogue conference. During the competition, the participants were offered two tasks: stance detection and argument classification. A corpus containing 9,550 sentences (comments on social media posts) on three topics related to the COVID-19 pandemic (vaccination, quarantine, and wearing masks) was prepared, annotated, and used for training and testing. The system that won the first place in both tasks used the NLI (Natural Language Inference) variant of the BERT architecture, automatic translation into English to apply a specialized BERT model, retrained on Twitter posts discussing COVID-19, as well as additional masking of target entities. This system showed the following results: for the stance detection task an F1-score of 0.6968, for the argument classification task an F1-score of 0.7404. We hope that the prepared dataset and baselines will help to foster further research on argument mining for the Russian language.
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Vedula, Nikhita, Nedim Lipka, Pranav Maneriker, and Srinivasan Parthasarathy. "Open Intent Extraction from Natural Language Interactions (Extended Abstract)." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/663.

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Accurately discovering user intents from their written or spoken language plays a critical role in natural language understanding and automated dialog response. Most existing research models this as a classification task with a single intent label per utterance. Going beyond this formulation, we define and investigate a new problem of open intent discovery. It involves discovering one or more generic intent types from text utterances, that may not have been encountered during training. We propose a novel, domain-agnostic approach, OPINE, which formulates the problem as a sequence tagging task in an open-world setting. It employs a CRF on top of a bidirectional LSTM to extract intents in a consistent format, subject to constraints among intent tag labels. We apply multi-headed self-attention and adversarial training to effectively learn dependencies between distant words, and robustly adapt our model across varying domains. We also curate and release an intent-annotated dataset of 25K real-life utterances spanning diverse domains. Extensive experiments show that OPINE outperforms state-of-art baselines by 5-15% F1 score.
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Feng, Xiaocheng, Xiachong Feng, Bing Qin, Zhangyin Feng, and Ting Liu. "Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/566.

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Neural networks have been widely used for high resource language (e.g. English) named entity recognition (NER) and have shown state-of-the-art results.However, for low resource languages, such as Dutch, Spanish, due to the limitation of resources and lack of annotated data, taggers tend to have lower performances.To narrow this gap, we propose three novel strategies to enrich the semantic representations of low resource languages: we first develop neural networks to improve low resource word representations by knowledge transfer from high resource language using bilingual lexicons. Further, a lexicon extension strategy is designed to address out-of lexicon problem by automatically learning semantic projections.Thirdly, we regard word-level entity type distribution features as an external language-independent knowledge and incorporate them into our neural architecture. Experiments on two low resource languages (including Dutch and Spanish) demonstrate the effectiveness of these additional semantic representations (average 4.8\% improvement). Moreover, on Chinese OntoNotes 4.0 dataset, our approach achieved an F-score of 83.07\% with 2.91\% absolute gain compared to the state-of-the-art results.
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Figueirêdo, Ilan Sousa, Tássio Farias Carvalho, Wenisten José Dantas Silva, Lílian Lefol Nani Guarieiro, and Erick Giovani Sperandio Nascimento. "Detecting Interesting and Anomalous Patterns In Multivariate Time-Series Data in an Offshore Platform Using Unsupervised Learning." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31297-ms.

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Abstract Detection of anomalous events in practical operation of oil and gas (O&G) wells and lines can help to avoid production losses, environmental disasters, and human fatalities, besides decreasing maintenance costs. Supervised machine learning algorithms have been successful to detect, diagnose, and forecast anomalous events in O&G industry. Nevertheless, these algorithms need a large quantity of annotated dataset and labelling data in real world scenarios is typically unfeasible because of exhaustive work of experts. Therefore, as unsupervised machine learning does not require an annotated dataset, this paper intends to perform a comparative evaluation performance of unsupervised learning algorithms to support experts for anomaly detection and pattern recognition in multivariate time-series data. So, the goal is to allow experts to analyze a small set of patterns and label them, instead of analyzing large datasets. This paper used the public 3W database of three offshore naturally flowing wells. The experiment used real data of production of O&G from underground reservoirs with the following anomalous events: (i) spurious closure of Downhole Safety Valve (DHSV) and (ii) quick restriction in Production Choke (PCK). Six unsupervised machine learning algorithms were assessed: Cluster-based Algorithm for Anomaly Detection in Time Series Using Mahalanobis Distance (C-AMDATS), Luminol Bitmap, SAX-REPEAT, k-NN, Bootstrap, and Robust Random Cut Forest (RRCF). The comparison evaluation of unsupervised learning algorithms was performed using a set of metrics: accuracy (ACC), precision (PR), recall (REC), specificity (SP), F1-Score (F1), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision-Recall Curve (AUC-PRC). The experiments only used the data labels for assessment purposes. The results revealed that unsupervised learning successfully detected the patterns of interest in multivariate data without prior annotation, with emphasis on the C-AMDATS algorithm. Thus, unsupervised learning can leverage supervised models through the support given to data annotation.
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Reports on the topic "Annotated score"

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Matthews, Lisa, Guanming Wu, Robin Haw, Timothy Brunson, Nasim Sanati, Solomon Shorser, Deidre Beavers, Patrick Conley, Lincoln Stein, and Peter D'Eustachio. Illuminating Dark Proteins using Reactome Pathways. Reactome, October 2022. http://dx.doi.org/10.3180/poster/20221027matthews.

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Diseases are often the consequence of proteins or protein complexes that are non-functional or that function improperly. An active area of research has focused on the identification of molecules that can interact with defective proteins and restore their function. While 22% percent of human proteins are estimated to be druggable, less than fifteen percent are targeted by FDA-approved drugs, and the vast majority of untargeted proteins are understudied or so-called "dark" proteins. Elucidation of the function of these dark proteins, particularly those in commonly drug-targeted protein families, may offer therapeutic opportunities for many diseases. Reactome is the most comprehensive, open-access pathway knowledgebase covering 2585 pathways and including 14246 reactions, 11088 proteins, 13984 complexes, and 1093 drugs. Placing dark proteins in the context of Reactome pathways provides a framework of reference for these proteins facilitating the generation of hypotheses for experimental biologists to develop targeted experiments, unravel the potential functions of these proteins, and then design drugs to manipulate them. To this end, we have trained a random forest with 106 protein/gene pairwise features collected from multiple resources to predict functional interactions between dark proteins and proteins annotated in Reactome and then developed three scores to measure the interactions between dark proteins and Reactome pathways based on enrichment analysis and fuzzy logic simulations. Literature evidence via manual checking and systematic NLP-based analysis support predicted interacting pathways for dark proteins. To visualize dark proteins in the context of Reactome pathways, we have also developed a new website, idg.reactome.org, by extending the Reactome web application with new features illustrating these proteins together with tissue-specific protein and gene expression levels and drug interactions.
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Megersa, Kelbesa. Financial Inclusion in a Refugee Response. Institute of Development Studies, August 2021. http://dx.doi.org/10.19088/k4d.2021.122.

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The growing scope, frequency, and complexity of forced displacement, both inside and outside of countries, has pushed donors and other development groups to rethink their approaches to humanitarian crises, particularly on refugee response. Financial inclusion is widely regarded as a particularly critical tool that development organisations can employ to mitigate the catastrophic impact of humanitarian crises on refugees. Financial inclusion would provide a wide range of financial products – such as savings, remittances, loans, and insurance – to both refugees and citizens of host countries, which are critical for disadvantaged populations seeking to mitigate shocks, acquire assets, and support local economic development. Changes in how humanitarian aid is distributed are opening the path for greater financial inclusion. Donors and humanitarian organisations are shifting away from emergency cash transfers and toward digital payments via electronic cards. This opens new opportunities to connect refugees and displaced people to a bigger pool of financial services. This rapid literature review summarises the available evidence on toolkits that assist the response by humanitarian and development agencies to financial inclusion of refugees. In addition to the documents defined explicitly as “toolkits”, it also includes reports and online articles which contain useful guidance, since there were few “toolkits” available. Generally, there is lack of resources that directly address the query, i.e., “financial inclusion” in a “refugee response” context. Although there is a growing literature and evidence on the financial inclusion theme, much of it does not directly relate to refugees. Furthermore, most guidance notes and toolkits prepared for refugee response by humanitarian/development agencies do not directly and explicitly deal with financial inclusion, but rather focus on operational and programming issues of wider relief responses. The review is presented as an annotated bibliography format and includes toolkits, guidance notes, technical reports, and online articles by humanitarian and international development agencies.
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