Domain-specific Modeling Research Articles

MetaMorph: formalization of domain-specific conceptual modeling methods—an evaluative case study, juxtaposition and empirical assessment

Models have evolved from mere pictures supporting human understanding and communication to sophisticated knowledge structures processable by machines and establish value through their processing capabilities. This entails an inevitable need for computer-understandable modeling languages and causes formalization to be a crucial part in the lifecycle of engineering a modeling method. An appropriate formalism must be a means for providing a structural definition to enable a theoretical investigation of conceptual modeling languages and a unique, unambiguous way of specifying the syntax and semantics of an arbitrary modeling language. For this purpose, it must be generic and open to capturing any domain and any functionality. This paper provides a pervasive description of the formalism MetaMorph based on logic and model theory—an approach fulfilling the requirements above for modeling method engineering. The evaluation of the formalism is presented following three streams of work: First, two evaluative case studies illustrate the applicability of MetaMorph formalism concept by concept on the modeling language ProVis from the domain of stochastic education and the well-known Entity-Relationship language. ProVis as well as ER comprise only a few objects and relation types but with high interconnection and expressive power and are therefore considered interesting specimens for formalization. Second, a comprehensive juxtaposition of MetaMorph to three other formalization approaches based on different foundational theories is outlined concept by concept to underpin the formalism design. Third, an empirical evaluation has been performed, assessing the usability and adequacy of the formalism within a classroom assessment. The results allow for conclusions on the completeness, intuitiveness, and complexity as well as on interdependencies with engineers’ skills.

Critical assessment of transformer-based AI models for German clinical notes

Healthcare data such as clinical notes are primarily recorded in an unstructured manner. If adequately translated into structured data, they can be utilized for health economics and set the groundwork for better individualized patient care. To structure clinical notes, deep-learning methods, particularly transformer-based models like Bidirectional Encoder Representations from Transformers (BERT), have recently received much attention. Currently, biomedical applications are primarily focused on the English language. While general-purpose German-language models such as GermanBERT and GottBERT have been published, adaptations for biomedical data are unavailable. This study evaluated the suitability of existing and novel transformer-based models for the German biomedical and clinical domain. We used 8 transformer-based models and pre-trained 3 new models on a newly generated biomedical corpus, and systematically compared them with each other. We annotated a new dataset of clinical notes and used it with 4 other corpora (BRONCO150, CLEF eHealth 2019 Task 1, GGPONC, and JSynCC) to perform named entity recognition (NER) and document classification tasks. General-purpose language models can be used effectively for biomedical and clinical natural language processing (NLP) tasks, still, our newly trained BioGottBERT model outperformed GottBERT on both clinical NER tasks. However, training new biomedical models from scratch proved ineffective. The domain-adaptation strategy's potential is currently limited due to a lack of pre-training data. Since general-purpose language models are only marginally inferior to domain-specific models, both options are suitable for developing German-language biomedical applications. General-purpose language models perform remarkably well on biomedical and clinical NLP tasks. If larger corpora become available in the future, domain-adapting these models may improve performances.

Extracting interrelated information from road-related social media data

The social media data (SMD) have been viewed as a potential and promising information source of road conditions. However, most existing SMD-based sensing approaches (SMDSAs) either ignore interrelations among information items (e.g., name, direction, and status of the road) or rely on rigid grammar rules to establish entities’ interrelations. Additionally, current SMDSAs in the transportation domain are unable to link the extracted text-formatted information with domain-specific models (e.g., virtual road model, VRM). In order to fill such gaps, this work proposes an improved SMDSA of road conditions, which involves a three-stage (i.e., SMD classification, relation inference, and entity pair recognition) interrelated information extraction model, as well as a semantic converter to feed the SMD-provided text-formatted information into VRMs. The proposed SMDSA is demonstrated by the newly annotated datasets of tweets in Lexington, USA. The three-stage interrelated information extraction model outperforms conventional rule-based methods and deep-learning algorithms (e.g., Text CNN, Bi-LSTM, Piecewise CNN, and Capsule Net). The SMD-enabled VRM also preliminarily shows its capacity to optimize signal timings during incidents that change the road network topology. This work contributes to circumventing the reliance on human-made rules during SMDSAs’ development, bridging user-generated SMD with operable VRMs for potential real-world road management, and providing a standard tweet dataset annotated with interrelation triplets to help promote SMDSA studies.

Production processes modelling within digital product manufacturing in the context of Industry 4.0

Industry 4.0 aims to establish highly flexible production, enabling effective and efficient mass customisation of products. Modelling techniques and simulation of production processes are among the core techniques of the manufacturing industry that facilitate flexibility and automation of a shop floor in the era of Industry 4.0. In this paper, we present an approach to support production process modelling and process model management. The approach is based on Model-Driven (MD) principles and comprises a Domain-Specific Modelling Language (DSML) named Multi-Level Production Process Modelling Language (MultiProLan). MultiProLan uses a set of concepts to specify production process models suitable for automatic instruction generation and execution of the instructions in a simulation or on a shop floor. By using MultiProLan, process designers may create process models independent of the specific production system. Such process models can either be automatically enriched by matching and scheduling algorithms or manually enriched by a process designer via MultiProLan’s modelling tool. In this paper, we also present an application of our approach in the assembly industry to showcase its dynamic resource management, generation of production documentation, error handling and process monitoring.

HealMA: a model-driven framework for automatic generation of IoT-based Android health monitoring applications.

The development of IoT-based Android health monitoring mobile applications (apps) using traditional software development methods is a challenging task. Developers need to be familiar with various programming languages to manage the heterogeneity of hardware and software systems and to support different communication technologies. To address these problems, in this paper, we first analyze the domain of health monitoring mobile applications and then propose a framework based on model-driven engineering that accelerates the development of such systems. The proposed framework, called HealMA, includes a domain-specific modeling language, a graphical modeling editor, several validation rules, and a set of model-to-code transformations, all packed as an Eclipse plugin. We evaluated the framework to assess its applicability in generating various mobile health applications, as well as its impact on software productivity. To this end, four different health monitoring applications have been automatically generated. Then, we evaluated the productivity of software developers by comparing the time and effort it takes to use HealMA compared to a code-centric process. As part of the evaluation, we also evaluated the usability of HealMA-generated apps by conducting a user study. The results show that HealMA is both applicable and beneficial for automatic generation of usable IoT-based Android health monitoring apps.

BioGPT: generative pre-trained transformer for biomedical text generation and mining.

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.

Formalization of Ontology Conceptualizations Using Model Transformation

Conceptual models are built with concepts and relationships between them to reach a unified view of domain problems. There are many kinds of conceptual models developed in different modeling languages, such as class diagrams and entity-relationship models. In this paper, the authors have developed a specific meta-model following the Ecore standard to define conceptual models. These domain-specific conceptual models can be automatically formalized as domain ontologies using model transformation with the technique of triple graph grammars into ontology formal descriptions in accordance with the defined Ecore meta-model of the language OWL (web ontology language). For ontology deployment, its OWL code may be generated from OWL models using model-to-code transformation guided by Xpand templates. A performance evaluation is realized using a benchmark from the university domain with very large conceptual models. Through the experiments, they validate the performance and we prove the exactness and the scalability of the automatic transformation process of conceptual models.

Model-Driven Engineering of Process-Aware Information Systems

Enterprise information systems created with model-driven software engineering methods need to handle not only data but also business processes in an automated way. This paper shows how to engineer process-aware information systems following the model-driven and generative software engineering paradigms. Existing approaches realize either the generation of automated or manual activities but do not employ model-driven engineering of all system aspects through systematic language composition. A generative approach that additionally uses process modeling languages allows developers to evolve generated data-centric information systems into process-aware information systems. To be usable within our generation process, we have developed a textual BPMN version and a corresponding language tooling to check the soundness of the models. We have included these process models into the generation process of an information system together with other domain-specific modeling languages, e.g., for data structures, and generate an extendable, process-aware information system that is open for continuous regeneration and hand-written additions. This approach allows us to lift a generated data-centric information system to a process-aware information system. Agile development enabled through the opportunity to validate assumptions automatically and adapt changes efficiently, enhances the engineering process as well as the generated systems themselves.

A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora

BackgroundClinical trial protocols are the foundation for advancing medical sciences, however, the extraction of accurate and meaningful information from the original clinical trials is very challenging due to the complex and unstructured texts of such documents. Named entity recognition (NER) is a fundamental and necessary step to process and standardize the unstructured text in clinical trials using Natural Language Processing (NLP) techniques.MethodsIn this study we fine-tuned pre-trained language models to support the NER task on clinical trial eligibility criteria. We systematically investigated four pre-trained contextual embedding models for the biomedical domain (i.e., BioBERT, BlueBERT, PubMedBERT, and SciBERT) and two models for the open domains (BERT and SpanBERT), for NER tasks using three existing clinical trial eligibility criteria corpora. In addition, we also investigated the feasibility of data augmentation approaches and evaluated their performance.ResultsOur evaluation results using tenfold cross-validation show that domain-specific transformer models achieved better performance than the general transformer models, with the best performance obtained by the PubMedBERT model (F1-scores of 0.715, 0.836, and 0.622 for the three corpora respectively). The data augmentation results show that it is feasible to leverage additional corpora to improve NER performance.ConclusionsFindings from this study not only demonstrate the importance of contextual embeddings trained from domain-specific corpora, but also shed lights on the benefits of leveraging multiple data sources for the challenging NER task in clinical trial eligibility criteria text.

Do syntactic trees enhance Bidirectional Encoder Representations from Transformers (BERT) models for chemical-drug relation extraction?

Collecting relations between chemicals and drugs is crucial in biomedical research. The pre-trained transformer model, e.g. Bidirectional Encoder Representations from Transformers (BERT), is shown to have limitations on biomedical texts; more specifically, the lack of annotated data makes relation extraction (RE) from biomedical texts very challenging. In this paper, we hypothesize that enriching a pre-trained transformer model with syntactic information may help improve its performance on chemical–drug RE tasks. For this purpose, we propose three syntax-enhanced models based on the domain-specific BioBERT model: Chunking-Enhanced-BioBERT and Constituency-Tree-BioBERT in which constituency information is integrated and a Multi-Task-Learning framework Multi-Task-Syntactic (MTS)-BioBERT in which syntactic information is injected implicitly by adding syntax-related tasks as training objectives. Besides, we test an existing model Late-Fusion which is enhanced by syntactic dependency information and build ensemble systems combining syntax-enhanced models and non-syntax-enhanced models. Experiments are conducted on the BioCreative VII DrugProt corpus, a manually annotated corpus for the development and evaluation of RE systems. Our results reveal that syntax-enhanced models in general degrade the performance of BioBERT in the scenario of biomedical RE but improve the performance when the subject–object distance of candidate semantic relation is long. We also explore the impact of quality of dependency parses. [Our code is available at: https://github.com/Maple177/syntax-enhanced-RE/tree/drugprot (for only MTS-BioBERT); https://github.com/Maple177/drugprot-relation-extraction (for the rest of experiments)] Database URL https://github.com/Maple177/drugprot-relation-extraction

Enhancement of the virtual design platform for modeling a functional system architecture of complex cabin systems

Model-based systems engineering (MBSE) is a fundamental approach for the end-to-end use of digital models in the development of complex systems. The aviation industry in particular, where system complexity is constantly increasing, needs new concepts and methods to overcome ecological and socio-economic challenges. Therefore, domain-specific models are needed for the design and evaluation of systems to support the various system investigations, such as requirements management, installation space optimization, or failure analyses. An end-to-end coupling and linking of these mostly heterogeneous systems offer many advantages (e.g. shorter development times) over working with isolated digital sub-models, natural language documents, and purely physical prototypes. In addition, digitalization allows global and interdisciplinary collaboration of multiple teams of experts on the same virtual product. Since this approach is particularly promising for the configuration of aircraft cabins, a virtual development platform is developed at the German Aerospace Center (DLR) for the conceptual design of the aircraft cabin and its systems. As a result, virtual prototypes of cabin configurations are quickly generated to allow new concepts to be visualized and investigated at an early design stage. Extending the conceptual cabin system design process with a functional system architecture and executable system architecture models promotes information traceability, early failure detection, and requirements verification. The methodology used for this purpose is presented in this paper. The systems modeling language (SysML) is used to build a model for the functional depiction of cabin systems and to link it to existing models of the conceptual cabin design process. The modeling is performed exemplarily for the passenger service functions. Subsequently, the results are automatically transferred to the virtual development platform to experience the generated cabin concept.

Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design

National educational standards stress the importance of science and mathematics learning for today’s students. However, across disciplines, students frequently struggle to meet learning goals about core concepts like energy. Digital learning environments enhanced with artificial intelligence hold the promise to address this issue by providing individualized instruction and support for students at scale. Scaffolding and feedback, for example, are both most effective when tailored to students’ needs. Providing individualized instruction requires continuous assessment of students’ individual knowledge, abilities, and skills in a way that is meaningful for providing tailored support and planning further instruction. While continuously assessing individual students’ science and mathematics learning is challenging, intelligent tutoring systems show that it is feasible in principle. However, the learning environments in intelligent tutoring systems are typically not compatible with the vision of how effective K-12 science and mathematics learning looks like. This leads to the challenge of designing digital learning environments that allow for both – meaningful science and mathematics learning and the reliable and valid assessment of individual students’ learning. Today, digital devices such as tablets, laptops, or digital measurement systems increasingly enter science and mathematics classrooms. In consequence, students’ learning increasingly produces rich product and process data. Learning Analytics techniques can help to automatically analyze this data in order to obtain insights about individual students’ learning, drawing on general theories of learning and relative to established domain specific models of learning, i.e., learning progressions. We call this approach Learning Progression Analytics (LPA). In this manuscript, building of evidence-centered design (ECD), we develop a framework to guide the development of learning environments that provide meaningful learning activities and data for the automated analysis of individual students’ learning – the basis for LPA and scaling individualized instruction with artificial intelligence.

RiFeGAN2: Rich Feature Generation for Text-to-Image Synthesis From Constrained Prior Knowledge

Text-to-image synthesis is a challenging task that generates realistic images from a textual description. The description contains limited information compared with the corresponding image and is ambiguous and abstract, which will complicate the generation and lead to low-quality images. To address this problem, we propose a novel generation text-to-image synthesis method, called RiFeGAN2, to enrich the given description. To improve the enrichment quality while accelerating the enrichment process, RiFeGAN2 exploits a domain-specific constrained model to limit the search scope and then uses an attention-based caption matching model to refine the compatible candidate captions based on constrained prior knowledge. To improve the semantic consistency between the given description and the synthesized results, RiFeGAN2 employs improved SAEMs, SAEM2s, to compact better features of the retrieved captions and effectively emphasize the descriptions via incorporating centre-attention layers. Finally, multi-caption attentional GANs are exploited to synthesize images from those features. Experiments performed on widely-used datasets show that the models can generate vivid images from enriched captions and effectually improve the semantic consistency.

Threat Modeling and Risk Analysis for Miniaturized Wireless Biomedical Devices

The landscape of miniaturized wireless biomedical devices (MWBDs) is rapidly expanding as proactive mobile healthcare proliferates. MWBDs are diverse and include various injectable, ingestible, implantable, and wearable devices. While the growth of MWBDs increases the flexibility of medical services, the adoption of these technologies brings privacy and security risks for their users. MWBDs can operate with sensitive, private information and affect patients through the use of stimulation and drug delivery. Therefore, these devices require trust and need to be secure. Embedding protective mechanisms into MWBDs is challenging because they are restricted in size, power budget, as well as processing and storage capabilities. Nevertheless, MWBDs need to be at least minimally securable in the face of evolving threats. The main intent of this work is to make the primary stakeholders of MWBDs aware of associated risks and to help the architects and the manufacturers of MWBDs protect their emerging designs in a repeatable and structured manner. Making MWBDs securable begins with performing threat modeling. This paper introduces a domain-specific qualitative-quantitative threat model dedicated to MWBDs. The proposed model is then applied to representative case studies from each category of MWBDs.

Neural-ILT 2.0: Migrating ILT to Domain-Specific and Multitask-Enabled Neural Network

Optical proximity correction (OPC) in modern design closures has become extremely expensive and challenging. Conventional model-based OPC encounters performance degradation and large process variation, while aggressive approach, such as inverse lithography technology (ILT), suffers from large computational overhead for both mask optimization and mask writing processes. In this article, we developed Neural-ILT, an end-to-end learning-based OPC framework, which literally conducts mask prediction and ILT correction for a given layout in a single neural network, with the objectives of: 1) mask printability enhancement; 2) mask complexity optimization; and 3) flow acceleration. A domain-specific model pretraining recipe, which introduces the domain knowledge of lithography system, is proposed to help Neural-ILT achieving faster and better convergence. Quantitative results show that compared to the state-of-the-art (SOTA) learning-based OPC solutions and conventional OPC flows, Neural-ILT can achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$30\times $ </tex-math></inline-formula> turnaround time (TAT) speedup and the best mask printability with relatively lower mask complexity. Based on the developed infrastructure, we further investigated the feasibility of handling multiple mask optimization tasks for different datasets within a common Neural-ILT platform. We believe this work could bridge well-developed deep learning toolkits to GPU-based high-performance lithographic computations to achieve groundbreaking performance boosting on various computational lithography-related tasks.

Structured Development of Digital Twins—A Cross-Domain Analysis towards a Unified Approach

Digital Twins (DT) as digital representations are increasingly becoming operational design tools in a variety of contexts. Although a common understanding of the concept and the underlying development procedure would facilitate DT applications, only limited information has been published on the essential stages of development and fundamental development activities. This paper examines the extent to which an abstract, and thus generally applicable model for the development of Digital Twins can be identified. In order to come up with such a reference procedure, a structured analysis of published development experiences has been performed. Three major application domains, namely product lifecycle management, manufacturing, and predictive maintenance, could be detailed and cross-checked. For each of these domains, a contextual development model could be derived from empirically valid design and engineering practices. The data also allowed for the determination of which way each model corresponds to existing Digital Twin concepts. The use of a standard modeling notation enabled the integration of the domain-specific models into a single Digital Twin development model. As a result, developers are guided by domain-independent and -dependent development steps. Due to its generic structure, the model can serve for further domain explorations.

Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models.

While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task. Data were collected from a cohort of patients undergoing diabetic retinopathy screening between March 2020 and March 2021. Two models were created for each of 12 systemic health features based on the DenseNet201 architecture: one utilizing transfer learning with images from ImageNet and another from 35,126 fundus images. Here, 1277 fundus images were used to train the AI models. Area under the receiver operating characteristics curve (AUROC) scores were used to compare the model performance. Models utilizing the ImageNet transfer learning data were superior to those using retinal images for transfer learning (mean AUROC 0.78 vs. 0.65, p-value < 0.001). Models using ImageNet pretraining were able to predict systemic features including ethnicity (AUROC 0.93), age > 70 (AUROC 0.90), gender (AUROC 0.85), ACE inhibitor (AUROC 0.82), and ARB medication use (AUROC 0.78). We conclude that fundus images contain valuable information about the systemic characteristics of a patient. To optimize DL model performance, we recommend that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy.

Named-entity recognition in Turkish legal texts

AbstractNatural language processing (NLP) technologies and applications in legal text processing are gaining momentum. Being one of the most prominent tasks in NLP, named-entity recognition (NER) can substantiate a great convenience for NLP in law due to the variety of named entities in the legal domain and their accentuated importance in legal documents. However, domain-specific NER models in the legal domain are not well studied. We present a NER model for Turkish legal texts with a custom-made corpus as well as several NER architectures based on conditional random fields and bidirectional long-short-term memories (BiLSTMs) to address the task. We also study several combinations of different word embeddings consisting of GloVe, Morph2Vec, and neural network-based character feature extraction techniques either with BiLSTM or convolutional neural networks. We report 92.27% F1 score with a hybrid word representation of GloVe and Morph2Vec with character-level features extracted with BiLSTM. Being an agglutinative language, the morphological structure of Turkish is also considered. To the best of our knowledge, our work is the first legal domain-specific NER study in Turkish and also the first study for an agglutinative language in the legal domain. Thus, our work can also have implications beyond the Turkish language.

Performance of Multiple Pretrained BERT Models to Automate and Accelerate Data Annotation for Large Datasets.

To develop and evaluate domain-specific and pretrained bidirectional encoder representations from transformers (BERT) models in a transfer learning task on varying training dataset sizes to annotate a larger overall dataset. The authors retrospectively reviewed 69 095 anonymized adult chest radiograph reports (reports dated April 2020-March 2021). From the overall cohort, 1004 reports were randomly selected and labeled for the presence or absence of each of the following devices: endotracheal tube (ETT), enterogastric tube (NGT, or Dobhoff tube), central venous catheter (CVC), and Swan-Ganz catheter (SGC). Pretrained transformer models (BERT, PubMedBERT, DistilBERT, RoBERTa, and DeBERTa) were trained, validated, and tested on 60%, 20%, and 20%, respectively, of these reports through fivefold cross-validation. Additional training involved varying dataset sizes with 5%, 10%, 15%, 20%, and 40% of the 1004 reports. The best-performing epochs were used to assess area under the receiver operating characteristic curve (AUC) and determine run time on the overall dataset. The highest average AUCs from fivefold cross-validation were 0.996 for ETT (RoBERTa), 0.994 for NGT (RoBERTa), 0.991 for CVC (PubMedBERT), and 0.98 for SGC (PubMedBERT). DeBERTa demonstrated the highest AUC for each support device trained on 5% of the training set. PubMedBERT showed a higher AUC with a decreasing training set size compared with BERT. Training and validation time was shortest for DistilBERT at 3 minutes 39 seconds on the annotated cohort. Pretrained and domain-specific transformer models required small training datasets and short training times to create a highly accurate final model that expedites autonomous annotation of large datasets.Keywords: Informatics, Named Entity Recognition, Transfer Learning Supplemental material is available for this article. ©RSNA, 2022See also the commentary by Zech in this issue.

Context Sensitive Verb Similarity Dataset for Legal Information Extraction

Existing literature demonstrates that verbs are pivotal in legal information extraction tasks due to their semantic and argumentative properties. However, granting computers the ability to interpret the meaning of a verb and its semantic properties in relation to a given context can be considered as a challenging task, mainly due to the polysemic and domain specific behaviours of verbs. Therefore, developing mechanisms to identify behaviors of verbs and evaluate how artificial models detect the domain specific and polysemic behaviours of verbs can be considered as tasks with significant importance. In this regard, a comprehensive dataset that can be used as an evaluation resource, as well as a training data set, can be considered as a major requirement. In this paper, we introduce LeCoVe, which is a verb similarity dataset intended towards facilitating the process of identifying verbs with similar meanings in a legal domain specific context. Using the dataset, we evaluated both domain specific and domain generic embedding models, which were developed using state-of-the-art word representation and language modelling techniques. As a part of the experiments carried out using the announced dataset, Sense2Vec and BERT models were trained using a corpus of legal opinion texts in order to capture domain specific behaviours. In addition to LeCoVe, we demonstrate that a neural network model, which was developed by combining semantic, syntactic, and contextual features that can be obtained from the outputs of embedding models, can perform comparatively well, even in a low resource scenario.