Understanding Semantic Analysis NLP

In finance, NLP can be paired with machine learning to generate financial reports based on invoices, statements and other documents. Financial analysts can also employ natural language processing to predict stock market trends by analyzing news articles, social media posts and other online sources for market sentiments. A system for semantic analysis determines the meaning of words in text. Semantics gives a deeper understanding of the text in sources such as a blog post, comments in a forum, documents, group chat applications, chatbots, etc. With lexical semantics, the study of word meanings, semantic analysis provides a deeper understanding of unstructured text. The first part of semantic analysis, studying the meaning of individual words is called lexical semantics.

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Autoregressive (AR) models are statistical and time series models used to analyze and forecast data points based on their previous… Natural language processing (NLP) for Arabic text involves tokenization, stemming, lemmatization, part-of-speech tagging, and named entity recognition, among others…. Neri Van Otten is a machine learning and software engineer with over 12 years of Natural Language Processing (NLP) experience. Spacy Transformers is an extension of spaCy that integrates transformer-based models, such as BERT and RoBERTa, into the spaCy framework, enabling seamless use of these models for semantic analysis. Gensim is a library for topic modelling and document similarity analysis. It is beneficial for techniques like Word2Vec, Doc2Vec, and Latent Semantic Analysis (LSA), which are integral to semantic analysis.

What Is Semantic Analysis?

It is also essential for automated processing and question-answer systems like chatbots. However, many organizations struggle to capitalize on it because of their inability to analyze unstructured data. This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes. Automated semantic analysis works with the help of machine learning algorithms. With the help of semantic analysis, machine learning tools can recognize a ticket either as a  Payment issue  or a Shipping problem. Now, we can understand that meaning representation shows how to put together the building blocks of semantic systems.

Insurance companies can assess claims with natural language processing since this technology can handle both structured and unstructured data. NLP can also be trained to pick out unusual information, allowing teams to spot fraudulent claims. This degree of language understanding can help companies automate even the most complex language-intensive processes and, in doing so, transform the way they do business. So the question is, why settle for an educated guess when you can rely on actual knowledge Expert.ai’s rule-based technology starts by reading all of the words within a piece of content to capture its real meaning. It then identifies the textual elements and assigns them to their logical and grammatical roles.

Do the syntax analysis and semantic analysis give the same output?

It involves words, sub-words, affixes (sub-units), compound words, and phrases also. All the words, sub-words, etc. are collectively known as lexical items. The semantic analysis creates a representation of the meaning of a sentence. But before deep dive into the concept and approaches related to meaning representation, firstly we have to understand the building blocks of the semantic system. Therefore, in semantic analysis with machine learning, computers use Word Sense Disambiguation to determine which meaning is correct in the given context. It is the first part of the semantic analysis in which the study of the meaning of individual words is performed.

It specializes in deep learning for NLP and provides a wide range of pre-trained models and tools for tasks like semantic role labelling and coreference resolution. These future trends in semantic analysis hold the promise of not only making NLP systems more versatile and intelligent but also more ethical and responsible. As semantic analysis advances, it will profoundly impact various industries, from healthcare and finance to education and customer service. The synergy between humans and machines in the semantic analysis will develop further. Humans will be crucial in fine-tuning models, annotating data, and enhancing system performance. Real-time semantic analysis will become essential in applications like live chat, voice assistants, and interactive systems.

Advantages of Syntactic Analysis

In English, there are a lot of words that appear very frequently like “is”, “and”, “the”, and “a”. Stop words might be filtered out before doing any statistical analysis. Word Tokenizer is used to break the sentence into separate words or tokens. Case Grammar was developed by Linguist Charles J. Fillmore in the year 1968. Case Grammar uses languages such as English to express the relationship between nouns and verbs by using the preposition. Augmented Transition Networks is a finite state machine that is capable of recognizing regular languages.

If an account with this email id exists, you will receive instructions to reset your password. We will calculate the Chi square scores for all the features and visualize the top 20, here terms or words or N-grams are features, and positive and negative are two classes. Given a feature X, we can use Chi square test to evaluate its importance to distinguish the class. In reference to the above sentence, we can check out tf-idf scores for a few words within this sentence. In this context, this will be the hypernym while other related words that follow, such as  leaves ,  roots , and  flowers  are referred to as their hyponyms. What’s difficult is making sense of every word and comprehending what the text says.

The main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related. For example, if we talk about the same word  Bank, we can write the meaning a financial institution or a river bank. In that case it would be the example of homonym because the meanings are unrelated to each other. In the second part, the individual words will be combined to provide meaning in sentences.

While semantic analysis is more modern and sophisticated, it is also expensive to implement. You see, the word on its own matters less, and the words surrounding it matter more for the interpretation. A semantic analysis algorithm needs to be trained with a larger corpus of data to perform better. Syntactic analysis involves analyzing the grammatical syntax of a sentence to understand its meaning. The idea of entity extraction is to identify named entities in text, such as names of people, companies, places, etc. For Example, Tagging Twitter mentions by sentiment to get a sense of how customers feel about your product and can identify unhappy customers in real-time.

How Does Semantic Analysis In NLP Work?

These categories can range from the names of persons, organizations and locations to monetary values and percentages. These two sentences mean the exact same thing and the use of the word is identical. With structure I mean that we have the verb ( robbed ), which is marked with a  V above it and a VP above that, which is linked with a S to the subject (the thief), which has a NP above it. This is like a template for a subject-verb relationship and there are many others for other types of relationships. It is a method for processing any text and sorting them according to different known predefined categories on the basis of its content.

The analysis can segregate tickets based on their content, such as map data-related issues, and deliver them to the respective The platform allows Uber to streamline and optimize the map data triggering the ticket. Cdiscount, an online retailer of goods and services, uses semantic analysis to analyze and understand online customer reviews.

Semantic analysis helps in processing customer queries and understanding their meaning, thereby allowing an organization to understand the customer’s inclination. Moreover, analyzing customer reviews, feedback, or satisfaction surveys helps understand the overall customer experience by factoring in language tone, emotions, and even sentiments. With sentiment analysis we want to determine the attitude (i.e. the sentiment) of a speaker or writer with respect to a document, interaction or event. Therefore it is a natural language processing problem where text needs to be understood in order to predict the underlying intent. The sentiment is mostly categorized into positive, negative and neutral categories.

Read more about https://www.metadialog.com/ here.

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Do you want to maintain a competitive advantage on the market in 2020 Your company must take advantage of the Business Intelligence tools.

Today, there are numerous tools that allow businesses to identify and monitor external and internal data trends.

Before analyzing the trends of this year, however, it is necessary to specify that all companies must trace a series of data which, through a long process, are eventually processed in spreadsheets.

This process has always been done by hand. But how much time wasted!

Here, surprisingly, Business Intelligence tools avoid this nuisance.

Do you find it difficult to choose the right platform for your company  Don’t worry, with our indications it will become much simpler.

Here are the best Business Intelligence tools for 2020.

1. Microsoft Power BI

Microsoft provides users with a Business Intelligence tool signed directly by the company: Power BI.

What sets it apart from other tools  Its completely different and non-web based approach. Power BI uses software  that must necessarily be downloaded for the desktop version.

You will discover how easy it is to connect hundreds of data sources together, including Microsoft, Facebook, Sybase and Oracle applications. Data analysis is so fast that you can get the first reports in a matter of minutes.

Here are the main advantages:

• Connect all data from any source
• You can access both from the web and from mobile
• Provides solutions for businesses of all sizes
• You can view reports in real time on all devices

2. Zoho Reports

Do you love the cloud  Zoho Reports  Business Intelligence platform is the solution for you.

It’s a great solution because it integrates data from a variety of files, such as Microsoft Office documents, URL feeds and databases, and like MySQL, including data from other clouds such as Dropbox and Google Drive, as well as other applications such as Salesforce, Quickbooks and Google Analytics.

The data collected by the analysis are then combined through a series of mathematical formulas and integrated statistics, such as marketing costs, which are then included in an Excel file.

What about the cloud  It is the next step. The sales data is entered into a cloud database to create an information report.

Does this seem like a long process  Not at all. Today, all this is accomplished through a simple online interface and a few clicks.

After that, you can decide to do what you want with your final report. You can print it  or  send it by e-mail.

Let’s summarize some advantages:

• Provides a cloud-based platform
• Collects data from all sources
• It provides a very simple interface in use and sharing
• It allows you to create complete reports

3. Dundas

Are you looking for a complete but simple to use tool  Dundas  is the right solution for you.
The company that created this Business Intelligence tool has 25 years of experience  behind it and is designed to transform the analysis of corporate data into visual data through careful control of the visual design elements.

Here are the benefits:

• You can use it on various devices, including mobile ones
• It has drag and drop tools
• Provides analysis of data from multiple sources in real time
• It is suitable for businesses of any size

4. Sisense

Do you want to collect data faster and smoothly  Fortune-500 has created the right product. It’s called  Sisense  and it collects data  very quickly, also exploiting artificial intelligence.

The artificial intelligence  simplifies the process of data analysis to enable more immediate decision-making.

Among its customers there arebig names such as GE, Philips, Fujitsu, NBC and Airbus which in turn have guaranteed the remarkable performance of the product.

Let’s see what differentiates it:

1. You can take advantage of the cloud
2. It is a scalable product
3. Simplify your workflows
4. It is based on end-to-end technology

5. Infor Birst

Let’s go back to citing BI tools that take advantage of the cloud. Infor Birst  is a cloud-based business intelligence  platform.

Take advantage of the latest digital technologies such as automation and  machine learning, to allow the team to work on big data through a secure and patented network.

Infor Birst also allows you to collect data from different sources and then manage and analyze it by means of a clear and intuitive interface.

Finally, an always very important aspect in the field of business analytics is the ability to collect reports with complete and high definition graphs.

4 reasons to choose it:

1. It is a BI software on the net
2. The user experience is very fluid
3. Offers cloud architecture
4. It provides solutions for businesses of all sizes and sectors

6. Board

A single tool, many activities you can do.

Board  represents a Business Intelligence, Performance Management platform and analysis software.

Board offers many solutions for all types of users, for large and small companies operating in any sector.

Given the large amount of products available, we advise you to consult the site to find out all the features of each single platform, of which you can also  try the demo to test its operation.

The advantages to choose it:

1. It offers a BI platform, with performance management and integrated analysis
2. You can take advantage of cloud technology
3. Access from all the devices you want
4. Create interactive dashboards with drag and drop

7. Looker

Are you an SQL or  data warehouse expert  Then you should choose Looker. The platform has precise BI tools, with metrics that allow you to save time, accessible at all times and processable, that is, integrated with third-party tools to make them even more performing.

If you don’t feel so expert, you can rely on the drag and drop dashboard. But if you are an expert, you can use the tools that the platform makes available for creating customized queries.

The data comes from different sources to ultimately provide you with a complete overview of  the metrics that represent your company.

It helps you to discover many aspects of your customers, to create tailor-made messages with which to make contact and, in general, to increase the understanding of each of your customers.

Here are 4 advantages:

1. It is a web platform and there is no software to download
2. It connects to the SQL database or data warehouse
3. Create charts that tell the story of your business
4. It has a high capacity for user management and collaboration

8. Qlik Sense

What is the  Qlik Sense platform made of An analysis engine, sophisticated artificial intelligence and a scalable multi cloud architecture.

Through the multi cloud architecture it  allows to implement SaaS and private clouds.

It allows you to combine and load data faster, create intelligent visualizationsand draw rich analyzes thanks to drag and drop and artificial intelligence.

Finally, you can also take advantage ofinteractive mobile tools to control and manage your data anytime and wherever you are.

It can also be used by those who do not have particular skills in the field, while the more experienced can resort to Python to be able to work on the most complex cases.

The benefits of the platform:

• Offers a BI and analysis solution
• Suitable for experts, for advanced and non-advanced analyzes
• Collects data from different sources
• It provides a simplified interface for data management and representation

9. ClicData

Do you want to best present all the data you have collected  ClicData  offers beautiful and interactive dashboards, but which hide much more underneath.

You can connect data from  more than 250 different sources, warehouse data and from the cloud.

ClicData has an ETL platform that allows you to transform data, merge it, manage it, view the history, etc.

ClicData allows you to share the dashboards you have created with your whole team and you can also use them to take stock of the activities you are carrying out for your customers.

Also, to make sure your metrics are always up to date, you can add automatic alerts.

Finally, ClicData has an iOS and Android application that allows you to access the dashboard anywhere and anytime.

Here’s what sets it apart:

• Cloud-based BI and data analysis platform
• Drag and drop tools for creating dashboards
• You can share your reports via email, via images or in PDF format
• Authorize different user levels for data visualization

10. Domo

Domo is a native cloud platform. Perhaps more than the others, it favors collaboration between users.

Thanks to Domo you can connect data from more than 1,000 sources, create KPI shared by the team, allow everyone to access data and solve problems faster.

4 advantages to consider:

• Merges data from a multitude of sources
• It allows you to create dashboards with drag and drop
• Create charts in real time
• It allows you to export data and collaborate with other users

Conclusions

Each service tries to solve most business problems, but only one is perfect for achieving the professional goals you have set yourself.

You can start comparing the characteristics of the platforms, start some free trial and, if you need expert advice, we at Exato Software are at your disposal.

If you want to be sure not to be wrong, fill out the form and  request a quote. Business Intelligence represents the future.

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The concept of�Business Intelligence�evokes solutions for advanced reporting, exploitation of data marts, and sophisticated algorithms for the synthesis of large quantities of unstructured data to support management decisions.�The result is a sort of crystal ball, with a little more scientific rigor, capable of helping the company to keep the right course to achieve specific objectives.�In reality, it was born long before there were computers and�software when at the end of the nineteenth century the essential role of the collection of precise data was already understood as a prerequisite for the success of any business.

Business Intelligence was born in the late nineteenth century when the role of collecting precise data for the success of any business was already understood.�the first BI tool that every manager had to deal with was a more or less personalized Excel sheet, with the flood of information that companies, sometimes unwittingly, have available in our day, to apply good Business Intelligence practices it becomes indispensable to react promptly to sudden changes in the market.

For this reason, it is no wonder that Gartner has indicated investments in Business Intelligence as the primary objective of CIOs at least until 2022, with an estimated market for Advanced and Predictive Analytics in 2 billion dollars worldwide for 2020.

In India, the situation is well summarized by the Nextvalue research published in the Assintel 2019 report, according to which 51% of medium-large companies and 45% of small and medium-sized ones have included 2019 or 2020 projects related to Business Intelligence in the portfolio, which in both cases is the choice most often indicated among all types of investment in software and services.

The pyramid of Business Intelligence

Currently, there are solutions of all kinds for BI, in�the cloud�or on-premises, suitable for large and small organizations, but we must not confuse the concept with the Business analytics tools which, as far as the cutting edge of the procedure is concerned, is only a component of the wider design that a company should consider to enable a future-proof Business Intelligence system.

The system can be conceived as a pyramid, in which more distilled data are obtained at each level, up to the extreme synthesis: a dashboard that constitutes the real decision support.�At the base of the pyramid, there are multiple sources of information, such as files, documents, and�databases, from which often fragmented and unstructured data are extracted.�The next level takes advantage of Data Warehouses and Data Marts to achieve better organization and important skimming.�The next phase involves the application of statistical analysis techniques and OLAP (On-Line Analytical Processing) that allow you to process large quantities of information very quickly, obtaining only the significant elements.�The next step is the so-called data mining.�It is here that the most sophisticated algorithms intervene in search of what can represent an anomaly, a risk, or an important suggestion to predict the progress of the business.

This important information must then be made available to decision-makers in the most immediate and understandable way possible, making use of experts in visualization techniques and reporting. Everything must work as quickly as possible so that management can respond immediately to critical issues and opportunities.�In this process, Business Analytics is the explanatory and predictive element of data analysis, thus representing the most delicate phase of the Data mining process.

How to get valid analysis with BI

If the base of the pyramid is constituted by a large amount of data available, the quality and homogeneity of the latter become the cornerstone of the whole procedure and the real discriminant for obtaining reliable analyzes and forecasts.�If the number of information today is almost never a problem, its completeness and reliability is very often a factor of uncertainty, which no one, however rigorous, implementation of a BI system can remedy.

For each level of the pyramid, it is therefore appropriate to act on the data to solve specific problems. With the basic sources it is necessary to make the data uniform and comparable, while in the transition to the Data warehouse, it is necessary to clean the archive from inaccurate data or correct them, eliminating the empty fields.

Before entering the Data Mining phase, it is also important to eliminate the duplicates and make sure that the set of preparatory information for the analysis is complete.�The visualization and reporting phase is also not immune to the risk of errors.�In this case, the elements to be verified are the uniqueness of the indicators and the correctness of the formulas used to represent the results.

BI in the Cloud for Small Business

Given the complexity of the process and the involvement of the entire technological infrastructure of an organization, one could think of BI as suitable only for medium and large companies.

In reality, the possibility of subscribing to Cloud-based BI services in SaaS mode has opened the doors of predictive data analysis even to small businesses, without having to have a specialized staff and avoiding the complexity and costs of an on-premises solution.

From IT manager to business analyst

Gartner predicted that most of the managers and analysts in the company will have tools available to prepare and evaluate data in a completely autonomous way, without needing to be supported by the IT department.�The trend is that of decentralization of Business Intelligence towards business units, with the consequent risk of the proliferation of similar tools within the same organization, each with only partial access to data and without centralized control.

This, in addition to the apparent ease of use of the new tools, is also due to the actual difficulties of corporate IT in meeting the growing needs for speed and flexibility in reporting.�Furthermore, it is not certain that those with IT skills also have the business vision necessary to design and make analytics efficient.

This scenario paves the way for the emergence of new professional figures, with a view to retraining IT workers, who would straddle the two worlds becoming fundamental resources for the harmonious development of Business Intelligence in the company.

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