Term Extraction

The dataset contains 45 documents containing narrative description of business process and their annotations. Annotated with activities, gateways, actors, and flow information. Each document is composed of three files: Doc_name.txt (Process description in CONLL format) Doc_name.process-elements.IOB2.txt (Process elements annotated with IOB2 Schema in CONLL format) Doc_name.relations.tsv (Process relations between process elements. Each line is a triplette (source, relation tag, target). Source and target are in the form: n_sent_x words range.)

The peer-reviewed paper of AWARE dataset is published in ASEW 2021, and can be accessed through: http://doi.org/10.1109/ASEW52652.2021.00049. Kindly cite this paper when using AWARE dataset. Aspect-Based Sentiment Analysis (ABSA) aims to identify the opinion (sentiment) with respect to a specific aspect. Since there is a lack of smartphone apps reviews dataset that is annotated to support the ABSA task, we present AWARE: ABSA Warehouse of Apps REviews. AWARE contains apps reviews from three different domains (Productivity, Social Networking, and Games), as each domain has its distinct functionalities and audience. Each sentence is annotated with three labels, as follows: Aspect Term: a term that exists in the sentence and describes an aspect of the app that is expressed by the sentiment. A term value of “N/A” means that the term is not explicitly mentioned in the sentence. Aspect Category: one of the pre-defined set of domain-specific categories that represent an aspect of the app (e.g., security, usability, etc.). Sentiment: positive or negative. Note: games domain does not contain aspect terms. We provide a comprehensive dataset of 11323 sentences from the three domains, where each sentence is additionally annotated with a Boolean value indicating whether the sentence expresses a positive/negative opinion. In addition, we provide three separate datasets, one for each domain, containing only sentences that express opinions. The file named “AWARE_metadata.csv” contains a description of the dataset’s columns. How AWARE can be used? We designed AWARE such that it can be used to serve various tasks. The tasks can be, but are not limited to: Sentiment Analysis. Aspect Term Extraction. Aspect Category Classification. Aspect Sentiment Analysis. Explicit/Implicit Aspect Term Classification. Opinion/Not-Opinion Classification. Furthermore, researchers can experiment with and investigate the effects of different domains on users' feedback. Source: AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation

The dataset contains 45 documents containing narrative description of business process and their annotations. Annotated with activities, gateways, actors, and flow information. Each document is composed of three files: Doc_name.txt (Process description in CONLL format) Doc_name.process-elements.IOB2.txt (Process elements annotated with IOB2 Schema in CONLL format) Doc_name.relations.tsv (Process relations between process elements. Each line is a triplette (source, relation tag, target). Source and target are in the form: n_sent_x words range.)

IEE is a financial-domain dataset of the Insurance-entity extraction task. Its goal is to locate named entities mentioned in the input sentence.

CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems. First track is devoted to Nested term extraction (in sequence labeling format) (also called Nested Term Identification).

CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems. Second track is devoted to Nested term extraction (in sequence labeling format) and classification (labels are specific, common, nomen).

CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems. Third track is devoted to Nested term extraction (in sequence labeling format) and classification (labels are specific, common, nomen) in cross-domain task.