An Artificial-Intelligence-Based Semantic Assist Framework for Judicial Trials Asian Journal of Law and Society

It is in this context that such a technology is used to develop a customer-focused strategy. As we enter the era of ‘data explosion,’ it is vital for organizations to optimize this excess yet valuable data and derive valuable insights to drive their business goals. Semantic analysis allows organizations to interpret the meaning and extract critical information from unstructured data.

It is true that the types of words can influence the significance of a syntactic analysis, but the meaning is not determined by what they are used for. The AI thematic intelligence report identifies the key trends impacting the growth of the theme over the next 12 to 24 months. It also includes a comprehensive industry analysis, including market size and growth forecasts for AI hardware, AI platforms, AI consulting and support services, and specialized AI applications. Semantic AI combines symbolic AI and statistical AI to improve the system’s performance.

Lexical Semantics

As such, Cdiscount was able to implement actions aiming to reinforce the conditions around product returns and deliveries (two criteria mentioned often in customer feedback). Since then, the company enjoys more satisfied customers and less frustration. The automated process of identifying in which sense is a word used according to its context. To know the meaning of Orange in a sentence, we need to know the words around it. The idea of entity extraction is to identify named entities in text, such as names of people, companies, places, etc. For Example, you could analyze the keywords in a bunch of tweets that have been categorized as “negative” and detect which words or topics are mentioned most often.

Thus, as and when a new change is introduced on the Uber app, the semantic analysis algorithms start listening to social network feeds to understand whether users are happy about the update or if it needs further refinement. Apart from these vital elements, the semantic analysis also uses semiotics and collocations to understand and interpret language. Semiotics refers to what the word means and also the meaning it evokes or communicates. For example, ‘tea’ refers to a hot beverage, while it also evokes refreshment, alertness, and many other associations. On the other hand, collocations are two or more words that often go together.

Adversarial Search

When we started Jigsaw Academy more than a decade ago, we had just one goal – to transform the lives and career paths of new-age technology aspirants through airtight industry-relevant programs. Our delivery methodologies, operational procedures, and approach to disrupting learning earned us not just recognition but credibility and authority along the journey. We were topping the ‘Best Analytics Institutes in India’ lists several times, for several years. Other financial-oriented areas may include the processing of commercial property and casualty claims, syndicated loans, contingent convertible bonds, electronic enforcement, proxy voting, the redemption of properties, and the over-the-counter business. Finally, blockchain adoption by the financial sector would potentially lead to cost savings in areas such as central finance reporting, arbitration, centralized operations, and business operations.

According to collected statistics, the voice transcription can reach 250–300 words/minute, which is much higher than the speed of traditional manual input (about 120–150 words/minute). In the following section, we describe and analyze the programs that Chinese courts have deployed in the trial system. These case-studies reflect Chinese judges’ thoughts on AI and its assistance for trials.

The introduction of Artificial Intelligence is becoming a game changer for organizations and society. Though enterprises are willing to invest in AI is not easy to define a clear path on how to start. We believe that integrating Semantic AI into the organizational strategy is foremost the first step for AI governance.

• Chakraborty et al. in Chakraborty et al. (2019) Healthcare and Biomedical advancement have consistently been significantly concerned to be elevated in all possible ways with the technological progression that is seaming out all through the globe.
• Through this standards-based approach, also internal data and external data can be automatically linked and can be used as a rich data set for any machine learning task.
• Semantic analysis techniques and tools allow automated text classification or tickets, freeing the concerned staff from mundane and repetitive tasks.
• It is a collection of procedures which is called by parser as and when required by grammar.

However, LSA has been covered in detail with specific inputs from various sources. This study also highlights the future prospects of semantic analysis domain and finally the study is concluded with the result section where areas of improvement are highlighted and the recommendations are made for the future research. This study also highlights the weakness and the limitations of the study in the discussion (Sect. 4) and results (Sect. 5). The process of understanding natural language by analyzing unstructured data for meaning, context, emotions, and sentiments is known as semantics analysis. In this way, the information obtained from this data can be used to improve machine learning algorithms.

Of necessity, SMA is aligned with certain classical SCOM ideas from the organization’s point of view, such as the capital-dependent hypothesis or the hypothesis of acquisition costs. This is the theoretical foundation for potential scientific SCOM SMA studies. Almost all studies on TSM platforms such as Facebook and Twitter currently focus on SMA applications. While we argue that blockchain technology will offer multiple advantages, it is important to analyze whether there are challenges or negative impacts. Casino et al. in Casino et al. (2019) explains very well how blockchain technology is used in multiple domains with the integration of AI and highlights how specific characteristics of this disruptive technology can revolutionize. This study also shows a clear classification of blockchain across different areas like healthcare, banking, and finance, supply chain management, etc.

Machine learning, on the other hand, is a subset of AI that focuses on training algorithms to learn from data, without being explicitly programmed. In Semantic AI, machine learning is used to train algorithms to recognize patterns in text, images, and other data, and to use those patterns to make predictions about the meaning of the data. In the matching process, using the semantic similarity matching between legal facts and laws/regulations can better mimic a judge’s logical inference between legal facts and laws/regulations, thus enhancing the reasoning of judgments. In the current context, Suzhou Intermediate Court has actively explored AI technologies to set up an intelligent court,Footnote
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which effectively improves the quality and effectiveness of the trial. The most cogent—and suitable for these capabilities—involves almost any form of natural language technologies for deployments as varied as implementing workflows with Cognitive Processing Automation to applications of conversational AI. Not long ago, semantic technologies were considered a taboo, almost esoteric branch of data management that few people talked about or openly admitted to using.

This way, you will mitigate dependency on experts and technologies and gain an understanding of how things work. Define your actual business needs and be aware of the maturity level of AI technologies. Based on your execution capabilities embrace Semantic AI as an organizational strategy. Semantic AI offers you a future-proof framework to support AI with data integration, your first strategic step. Those few examples already spell out the complexity of agile data management.

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