Domain-specific Terms Research Articles

Research on a Joint Extraction Method of Track Circuit Entities and Relations Integrating Global Pointer and Tensor Learning

To address the issue of efficiently reusing the massive amount of unstructured knowledge generated during the handling of track circuit equipment faults and to automate the construction of knowledge graphs in the railway maintenance domain, it is crucial to leverage knowledge extraction techniques to efficiently extract relational triplets from fault maintenance text data. Given the current lag in joint extraction technology within the railway domain and the inefficiency in resource utilization, this paper proposes a joint extraction model for track circuit entities and relations, integrating Global Pointer and tensor learning. Taking into account the associative characteristics of semantic relations, the nesting of domain-specific terms in the railway sector, and semantic diversity, this research views the relation extraction task as a tensor learning process and the entity recognition task as a span-based Global Pointer search process. First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). Finally, comparative experiments with existing mainstream joint extraction models were conducted, and the proposed model’s excellent performance was validated on the English public datasets NYT and WebNLG, the Chinese public dataset DuIE, and a private track circuit dataset. The F1 scores on the NYT, WebNLG, and DuIE public datasets reached 92.1%, 92.7%, and 78.2%, respectively.

Hierarchical attention for aspect extraction using LSTM in fine-grained sentiment analysis and evaluation

In the current digital world, aspect-based sentiment analysis (ABSA) from a review sentence is a big challenge. In recent works, the aspect term detection from the reviews of the discourse is performed independently, even for contextually interdependent reviews. Further, the baseline techniques could not correlate valid multi-word aspect extraction from different review sentences, which further degrades the aspect extraction performance. Therefore, this paper aims to improve the performance of the aspect term detection subtask using a supervised hierarchical attention-based model. A sentence alignment step is added as preprocessing before ABSA on multi-sentence reviews. This step removes deficient knowledge, captures aspect alignment from different review sentences, and properly relates the aspect to the context domain. Next, the context information is used to extract contextual aspects using word-vector representation. The input sentences and labeled data using sequence tagging are supplied to the proposed supervised hierarchical attention network. The proposed model can capture the semantics of words for different domains, enhancing the extraction of domain-specific relevant aspect terms. The experimental results shown in the SemEval-16 reviews present the effectiveness of the proposed attention-based approach for aspect term detection. The F-score is improved by approximately 3 % as compared to the recent hybrid unsupervised approach for both the laptop and restaurant domains when coreference resolution is not performed. On the other side, F-score is improved by approximately 4 % and 3 % as compared to the recent hybrid unsupervised approach for the laptop and restaurant domains, respectively, when coreference resolution is not performed. When the results of the proposed approach are compared with those of recent supervised approaches, the F-score of the proposed model is improved by 5 % and 9 % for laptops and restaurants, respectively, when coreference resolution is not performed. Whereas the F-score of the proposed model is improved by 9 % and 12 % for laptops and restaurants, respectively, when coreference resolution is performed before the aspect extraction subtask.

A representation learning-based approach to enhancing manufacturing quality for low-voltage electrical products

In low-voltage electrical product manufacturing, resolving quality issues is heavily reliant on engineering experience, and can be time-consuming and error-prone. Through quality management systems, a large number of historical defect cases can be consolidated for analysis along with relevant causes. However, these defect descriptions are often casually described with a mix of Chinese and English language, containing domain-specific terms. Additionally, defect product features have varieties and complex relationships. Therefore, historical defect cases have not been effectively utilized to support manufacturing quality issues. To address this challenge, this study proposes a representation learning-based approach to enhance manufacturing quality. Key research contributions include: (1) A two-stage word embedding technique based on the pre-trained language model. First, TSDAE is utilized for unsupervised pre-training on a large amount of unlabeled data. Then, Sentence-BERT is utilized for fine-tuning on a small set of labeled similar sentence pairs. This process yields a pre-trained language model specific to low-voltage electrical product defects. (2) NSHPSAGE graph embedding model based on the constructed product feature knowledge graph. We select more valuable neighboring nodes during sampling and explore different aggregation functions to enhance graph embedding performance. This model effectively aggregates product feature information into “Defect_Case” nodes, yielding graph embedding vectors. The model exhibits good Weighted-Precision and Weighted-Recall with a short training duration, and it can handle new nodes, addressing the issue of heterogeneous graph embedding. (3) A defect case recommendation technique that fuses word embedding and graph embedding. We use Multi-Head Attention Fusion in the late-fusion to obtain defect case vectors. This approach comprehensively considers defect description semantic knowledge and complex product feature relationships, enabling accurate defect case recommendation with the prototype system.

Reflection of Explicitation in Scientific Translation: Neural Machine Translation vs. Human Post-Editing

The present paper reports on the findings of an empirical comparative study on the extent to which explicitation is employed in the translation of a scientific text as conducted by Google Neural Machine Translation (GNMT) vs its post-edited (PE) version. A recent report released in English by the World Meteorological Organization in September 2023 was selected as the source text for the present study. The purpose of the study is to reveal how domain-specific acronyms and technical terms are lexically expanded (explicitated) in a GNMT output compared to its post-edited (PE) version as performed by a team of professional translators at a translation service provider in Amman-Jordan. Explicitation in translation can be obligatory or optional. The type of explicitation investigated in the present study is optional, pragmatic explicitation. The results show that GNMT has its limitations in dealing with scientific terms and acronyms in translating scientific texts from English into Arabic. In contrast, human post-editing explicitated domain-specific terms and acronyms producing a text with a higher level of readability and naturalness for domain expert readers and non-expert readers.

An ontology-based approach for harmonizing metrics in bike network evaluations

The urgency to decarbonize the transportation sector has accelerated the adoption of micro-mobility solutions, with cycling network development witnessing remarkable growth. Robust and quantitative evaluation frameworks are needed to evaluate the quality of such developments. While a plethora of bike network evaluation approaches exist, their diversity creates issues of interpretability and comparability due to varying metrics and domain-specific terms. We present three contributions to address these challenges. First, we construct a formal ontology, VeloNEMO, that captures key attributes of evaluation metrics for harmonizing bike network evaluation metrics. Second, we generate a machine-readable knowledge base containing these metrics, enabling meta-analyses and resolving some of the existing terminological discrepancies. Third, we propose recommendations for transparent and comparable metric descriptions across various evaluation approaches, illustrated by exploratory metric selection scenarios for a forthcoming bike network evaluation tool. In summary, our research addresses the need for a structured and shared vocabulary for bike network evaluations. This ontology-based approach aims to improve the coherence of evaluation methods as the field of bike network planning continues to evolve, ultimately supporting decision-making for sustainable transportation planning.

An Unsupervised Domain-Adaptive Framework for Chinese Spelling Checking

Chinese Spelling Check (CSC) is a meaningful task in the area of Natural Language Processing (NLP), which aims at detecting spelling errors in Chinese texts and then correcting these errors. Current typical Chinese Spelling Check models have shown impressive performance in general datasets with the help of pretrained language models such as BERT, but suffer great perform loss in downstream tasks with domain-specific terms because they are primarily trained on general corpora. To verify the cross-domain adaptation ability of these models, we build three new datasets with abundant domain-specific terms on financial, medical, and legal domains and conduct empirical investigations on them in the corresponding domain-specific test datasets to verify the cross-domain adaptation ability. In response to the poor performance of the existing models, we propose a framework named uChecker which utilizes unsupervised method in spelling error detection and correction. Experiment results prove that uChecker can perform well in domain-specific test datasets while not losing its performance in the general domain.

LLMs-based machine translation for E-commerce

Large language models(LLMs) have shown promising performance for various downstream tasks, especially machine translation. However, LLMs and Specialized Translation Models (STMs) are designed to handle general translation needs, they are not well-suited for domains with specialized terms and writing styles, such as e-commerce, legal, and medicine. In the e-commerce domain, the text often contains many domain-specific terms and keyword-stacked structures, leading to poor translation quality with existing NMT methods. To tackle these problems, we have collected two resources specifically for the e-commerce domain, including aligned Chinese-English bilingual terms and parallel corpus from real e-commerce scenarios for model fine-tuning. We propose an LLMs-based E-commerce machine translation approach(LEMT) which includes LLMs utilization, e-commerce resources collection, and tokenizer optimization. We conduct two-stage fine-tuning and self-contrastive enhancement based on general LLMs to enable the model to learn translation features in the e-commerce domain. Through comprehensive evaluations on real e-commerce titles, our LEMT methodology demonstrates superior translation quality and robustness, outperforming leading NMT models such as NLLB, LLaMA, and even GPT-4.

Ukrainian historical terminology designating social and military status in Old Rus': Examining their counterparts in English

The article researches translators' challenges in rendering domain-specific terms which include culture-specific items. The study has been carried out using Old Rus' historical terminology employed to designate social and military status. It was selected en masse from academic texts, using monographs and academic articles in both languages. 82 language units representing culture-specific historical terms related to social, political and military organisation of the society of that period were chosen and described in terms of appropriate translation. Our hypothesis has been tested as to the unacceptability of approximation (analogous translation and generalisation) in rendering culture-related terms that designate highly specific concepts of the defined period in Ukrainian history. The research establishes that approximate translation fails to meet the requirements of strict definition and accuracy demanded by professional terminology. Our study underlines the importance of secondary term formation, descriptive translation, and the method called combined renomination (transcription and description combined). For a translation to be accurate, the shift in translation should follow the shift in concept. The specific term may have wider, more general semantics, and in each particular context it should be specified. The use of the description or combined renomination helps to make the meaning of the concept clearer. The study demonstrates that the most accurate and effective secondary term formation usually follows the pattern of term formation in the original language.

Research on Term Recognition in the Field of English Linguistics Based on Deep Learning Algorithms

Linguistic recognition, a fundamental aspect of natural language processing, has seen significant advancements with the integration of deep learning algorithms. his paper presents a novel approach leveraging deep learning algorithms, specifically an optimized Long Short-Term Memory (LSTM) model, for term recognition tasks in English linguistics. This paper presents a novel approach leveraging deep learning algorithms, particularly Weighted Genetic Optimization Deep Learning (WGODL), for term recognition tasks in English linguistics. his hybrid approach not only enhances model accuracy but also improves robustness and generalization, enabling accurate term recognition even in noisy or ambiguous linguistic contexts. The weighted genetic optimization mechanism strategically prioritizes relevant features during the learning process, further enhancing the model's ability to discern domain-specific terms. Additionally, qualitative analysis highlights the interpretability of learned representations, shedding light on the underlying linguistic structures captured by the model. By harnessing the sequential nature of language data, the proposed model integrates LSTM units with optimization techniques to effectively capture contextual dependencies and semantic relationships within text corpora. Through extensive experimentation on diverse linguistic datasets, our optimized LSTM model achieves superior term recognition accuracy, surpassing traditional methods by 8.3%.

Combining permuted language model and adversarial training for Chinese machine reading comprehension

Due to the polysemy and complexity of the Chinese language, Chinese machine reading comprehension has always been a challenging task. To improve the semantic understanding and robustness of Chinese machine reading comprehension models, we propose a model that utilizes adversarial training algorithms and Permuted Language Model (PERT). Firstly, we employ the PERT pre-training model to embed paragraphs and questions into vector space to obtain corresponding sequential representations. Secondly, we use a multi-head self-attention mechanism to extract key textual information from the sequence and employ a Bi-GRU network to semantically fuse the output feature vectors, aiming to learn deep semantic representations in the text. Finally, we introduce perturbations into the model training process. We achieve this by utilizing adversarial training algorithms such as Fast Gradient Method (FGM) and Projected Gradient Descent (PGD). These algorithms generate adversarial samples to enhance the model’s robustness and stability when facing diverse inputs. We conducted comparative experiments on the publicly available Chinese reading comprehension datasets CMRC2018 and DRCD. The experimental results show that our proposed model has achieved significant improvements in both EM and F1-Score compared to the baseline model. To validate the model’s generalization and robustness, we utilized ChatGPT to construct a scientific dataset that includes a large number of domain-specific terms, sentences with mixed Chinese and English, and complex comprehension tasks. Our model also performed remarkably well on the self-built dataset. In conclusion, the proposed model not only effectively enhances the understanding of semantic information in Chinese text but also demonstrates a certain level of generalization capability.

A Dual-Attention Learning Network With Word and Sentence Embedding for Medical Visual Question Answering.

Research in medical visual question answering (MVQA) can contribute to the development of computer-aided diagnosis. MVQA is a task that aims to predict accurate and convincing answers based on given medical images and associated natural language questions. This task requires extracting medical knowledge-rich feature content and making fine-grained understandings of them. Therefore, constructing an effective feature extraction and understanding scheme are keys to modeling. Existing MVQA question extraction schemes mainly focus on word information, ignoring medical information in the text, such as medical concepts and domain-specific terms. Meanwhile, some visual and textual feature understanding schemes cannot effectively capture the correlation between regions and keywords for reasonable visual reasoning. In this study, a dual-attention learning network with word and sentence embedding (DALNet-WSE) is proposed. We design a module, transformer with sentence embedding (TSE), to extract a double embedding representation of questions containing keywords and medical information. A dual-attention learning (DAL) module consisting of self-attention and guided attention is proposed to model intensive intramodal and intermodal interactions. With multiple DAL modules (DALs), learning visual and textual co-attention can increase the granularity of understanding and improve visual reasoning. Experimental results on the ImageCLEF 2019 VQA-MED (VQA-MED 2019) and VQA-RAD datasets demonstrate that our proposed method outperforms previous state-of-the-art methods. According to the ablation studies and Grad-CAM maps, DALNet-WSE can extract rich textual information and has strong visual reasoning ability.

LKAN: LLM-Based Knowledge-Aware Attention Network for Clinical Staging of Liver Cancer.

Clinical staging of liver cancer (CSoLC), an important indicator for evaluating the degree of deterioration of primary liver cancer cells (PLCCs), is key in the diagnosis, treatment, and rehabilitation of liver cancer. In China, the current CSoLC adopts the China liver cancer (CNLC) staging, which is usually evaluated by clinicians based on the patient's radiology reports. Therefore, inferring clinical information from unstructured radiology reports can provide auxiliary decision support for clinicians. The key to solving the challenging task is to guide the model to pay attention to the staging-related words or sentences, and the following issues may occur: 1) Imbalanced categories: The symptoms of liver cancer in the early- or mid-stage are not obvious, resulting in more data in the end-stage. 2) Domain sensitivity of liver cancer data: The liver cancer dataset contains a large amount of domain knowledge, and the conventional methods can exacerbate out-of-vocabulary, which greatly affects the accuracy of classification. 3) Free-text and lengthy report: The radiology report of liver cancer sparsely describes various lesions with domain-specific terms, which poses difficulties in mining key information related to staging. To tackle these challenges, this article proposes a large language model (LLM)-based Knowledge-aware Attention Network (LKAN) for CSoLC. First, for maintaining semantic consistency, LLM and a rule-based algorithm are integrated to generate more diverse and reasonable data. Second, unlabeled radiology corpus of liver cancer are pre-trained to introduce domain knowledge for subsequent representation learning. Third, attention is improved by incorporating both global and local features, which can provide professional guidance for the classifier to focus on the important information. Compared with the baseline models, the classification accuracy of LKAN has achieved the best results with 90.3% Accuracy, 90.0% Macro_F1 score, and 90.0% Macro_Recall. The code is available at https://github.com/xczhh/Supplemental-Material.

The prototype taxonomised: Towards the capture, curation, and integration of physical models in new product development

The management of data related to prototypes created during new product development is seen as a beneficial yet challenging activity. While attempts have been made to understand prototypes and their context in a range of use-cases, there is a gap in the understanding of the data that captures a prototype’s context and physical form. This paper highlights this gap, and addresses it through the development of a new taxonomy. Using existing literature, a body of domain-specific terms, and the combined experience of the nine authors, a robust and systematic taxonomy development process was followed. A comparison of the developed and pre-existing taxonomies, and an illustrative example, is used for evaluation. The taxonomy is fully presented along with a description of each of the 53 dimensions, and it is intended to be the foundation upon which methods and processes can be developed to improve the capture, curation and integration of physical prototypes in new product development.

Research on named entity recognition of Traditional Chinese Medicine chest discomfort cases incorporating domain vocabulary features

Objective:To promote research on knowledge extraction and knowledge graph construction of chest discomfort medical cases in Traditional Chinese Medicine (TCM), this paper focuses on their named entity recognition (NER). The recognition task includes six entity types: “syndrome”, “symptom”, “etiology and pathogenesis”, “treatment method”, “medication”, and “prescription”. Methods:We annotated data in a BIO (B-begin, I-inside, O-outside) manner. For the characteristics of medical case texts, we proposed a custom dictionary method that can be dynamically updated for word segmentation. To compare the effect of the method on the experimental results, we applied the method in the BiLSTM-CRF model and IDCNN-CRF model, respectively. Results:The models using custom dictionaries (BiLSTM-CRF-Loaded and IDCNN-CRF-Loaded) outperformed the models without custom dictionaries (BiLSTM-CRF and IDCNN-CRF) in accuracy, precision, recall, and F1 score. The BiLSTM-CRF-Loaded model yielded F1 scores of 92.59% and 93.23% on the test set and validation set, respectively, surpassing the BERT-BiLSTM-CRF model by 3.59% and 4.87%. Furthermore, when analyzing the results for the six entity categories separately, we found that the use of custom dictionaries has a marked impact, with the categories of “etiology and pathogenesis” and “syndrome” demonstrating the most noticeable improvements. By comparing the F1 scores, we observed that the entity category “medication” yielded the highest performance, reaching F1 scores of 96.04% and 96.48% on the test set and validation set, respectively. Conclusion:We propose a word segmentation method based on a dynamically updated custom dictionary. The method is combined with the BILSTM-CRF and the IDCNN-CRF models, which enhances the model to recognize domain-specific terms and new entities. It can be widely applied in dealing with complex text structures and texts containing domain-specific terms.

Formal Specification and Testing for Reinforcement Learning

The development process for reinforcement learning applications is still exploratory rather than systematic. This exploratory nature reduces reuse of specifications between applications and increases the chances of introducing programming errors. This paper takes a step towards systematizing the development of reinforcement learning applications. We introduce a formal specification of reinforcement learning problems and algorithms, with a particular focus on temporal difference methods and their definitions in backup diagrams. We further develop a test harness for a large class of reinforcement learning applications based on temporal difference learning, including SARSA and Q-learning. The entire development is rooted in functional programming methods; starting with pure specifications and denotational semantics, ending with property-based testing and using compositional interpreters for a domain-specific term language as a test oracle for concrete implementations. We demonstrate the usefulness of this testing method on a number of examples, and evaluate with mutation testing. We show that our test suite is effective in killing mutants (90% mutants killed for 75% of subject agents). More importantly, almost half of all mutants are killed by generic write-once-use-everywhere tests that apply to any reinforcement learning problem modeled using our library, without any additional effort from the programmer.

Exploring Practical Metrics to Support Automatic Speech Recognition Evaluations.

Recent studies into the evaluation of automatic speech recognition for its quality of output in the form of text have shown that using word error rate to see how many mistakes exist in English does not necessarily help the developer of automatic transcriptions or captions. Confidence levels as to the type of errors being made remain low because mistranslations from speech to text are not always captured with a note that details the reason for the error. There have been situations in higher education where students requiring captions and transcriptions have found that some academic lecture results are littered with word errors which means that comprehension levels drop and those with cognitive, physical and sensory disabilities are particularly affected. Despite the incredible improvements in general understanding of conversational automatic speech recognition, academic situations tend to include numerous domain specific terms and the lecturers may be non-native speakers, coping with recording technology in noisy situations. This paper aims to discuss the way additional metrics are used to capture issues and feedback into the machine learning process to enable enhanced quality of output and more inclusive practices for those using virtual conferencing systems. The process goes beyond what is expressed and examines paralinguistic aspects such as timing, intonation, voice quality and speech understanding.

Topic sentiment analysis based on deep neural network using document embedding technique.

Sentiment Analysis (SA) is a domain- or topic-dependent task since polarity terms convey different sentiments in various domains. Hence, machine learning models trained on a specific domain cannot be employed in other domains, and existing domain-independent lexicons cannot correctly recognize the polarity of domain-specific polarity terms. Conventional approaches of Topic Sentiment Analysis perform Topic Modeling (TM) and SA sequentially, utilizing the previously trained models on irrelevant datasets for classifying sentiments that cannot provide acceptable accuracy. However, some researchers perform TM and SA simultaneously using topic-sentiment joint models, which require a list of seeds and their sentiments from widely used domain-independent lexicons. As a result, these methods cannot find the polarity of domain-specific terms correctly. This paper proposes a novel supervised hybrid TSA approach, called Embedding Topic Sentiment Analysis using Deep Neural Networks (ETSANet), that extracts the semantic relationships between the hidden topics and the training dataset using Semantically Topic-Related Documents Finder (STRDF). STRDF discovers those training documents in the same context as the topic based on the semantic relationships between the Semantic Topic Vector, a newly introduced concept that encompasses the semantic aspects of a topic, and the training dataset. Then, a hybrid CNN-GRU model is trained by these semantically topic-related documents. Moreover, a hybrid metaheuristic method utilizing Grey Wolf Optimization and Whale Optimization Algorithm is employed to fine-tune the hyperparameters of the CNN-GRU network. The evaluation results demonstrate that ETSANet increases the accuracy of the state-of-the-art methods by 1.92%.

Interpretable by Design: Learning Predictors by Composing Interpretable Queries.

There is a growing concern about typically opaque decision-making with high-performance machine learning algorithms. Providing an explanation of the reasoning process in domain-specific terms can be crucial for adoption in risk-sensitive domains such as healthcare. We argue that machine learning algorithms should be interpretable by design and that the language in which these interpretations are expressed should be domain- and task-dependent. Consequently, we base our model's prediction on a family of user-defined and task-specific binary functions of the data, each having a clear interpretation to the end-user. We then minimize the expected number of queries needed for accurate prediction on any given input. As the solution is generally intractable, following prior work, we choose the queries sequentially based on information gain. However, in contrast to previous work, we need not assume the queries are conditionally independent. Instead, we leverage a stochastic generative model (VAE) and an MCMC algorithm (Unadjusted Langevin) to select the most informative query about the input based on previous query-answers. This enables the online determination of a query chain of whatever depth is required to resolve prediction ambiguities. Finally, experiments on vision and NLP tasks demonstrate the efficacy of our approach and its superiority over post-hoc explanations.

Expert-adapted language models improve the fit to reading times

The concept of surprisal refers to the predictability of a word based on its context. Surprisal is known to be predictive of human processing difficulty and is usually estimated by language models. However, because humans differ in their linguistic experience, they also differ in the actual processing difficulty they experience with a given word or sentence. We investigate whether models that are similar to the linguistic experience and background knowledge of a specific group of humans are better at predicting their reading times than a generic language model. We analyze reading times from the PoTeC corpus [15,27] of eye movements from biology and physics experts reading biology and physics texts. We find experts read in-domain texts faster than novices, especially domain-specific terms. Next, we train language models adapted to the biology and physics domains and show that surprisal obtained from these specialized models improves the fit to expert reading times above and beyond a generic language model.

A Hybrid Semantic Networks Construction Framework for Engineering Design

Abstract This paper proposes a novel framework for building semantic networks from a seed design statement using Recursive Object Modeling (ROM), Word2Vec language modeling, and vector semantic-based method. Semantic Scholar API was used to retrieve abstracts of scientific papers to build ROM-based Semantic Networks to address the design problem implied in the seed design statement, following Environment Analysis from Environment-Based Design (EBD) methodology. The proposed framework was applied to construct the semantic network for a project to design aircraft braking systems, which demonstrates the framework's efficiency. The presented research makes two major contributions: a ROM-based phrase extractor and a domain-specific language model, which is trained on the automatically collected literature abstracts. Using a manually created and assessed truth set containing 100 pairs of abstract-key phrases, the phrase extractor was evaluated by benchmarking it with two existing off-the-shelf key phrase extraction algorithms: TextRank and Rake. The ROM-based phrase extractor extracted most key phrases from target domains and showed higher precision, recall, and F-1 scores than other methods. Meanwhile, the trained project-specific language model was evaluated using the NASA thesaurus. We randomly sampled 457 pairs of connected domain-specific terms related to aircraft braking and landing knowledge. Our Skip-gram model was compared with Google's pre-trained word2vec model and a baseline word2vec model. The results demonstrated that our language model could detect the most pairs of concepts from the NASA thesaurus. The generated semantic network can be applied to design information retrieval, computer-aided design idea generation, cross-domain communication support system, and designer training tool.