#3rdi search

[all-software-updates]

Overcoming Search Challenges with NLP Powered Semantic Search

In the realm of information retrieval, traditional keyword-based search methods often face significant challenges. These include:

Lack of Context Understanding: Relevant results are frequently produced by keyword searches that are unable to understand the context of the queries.

Synonym and Polysemy Issues: Synonyms and polysemy are two terms that have different meanings but are similar enough to trick conventional search engines.

Ambiguity in Queries: Users may become frustrated by unclear inquiries as they may yield a wide range of irrelevant results

Benefits of Semantic Search with NLP

Semantic search, powered by Natural Language Processing (NLP), addresses these challenges effectively:

Contextual Relevance: Semantic search provides more precise and pertinent results by deciphering the intent and context of queries.

Synonym Recognition: NLP gives the search engine the ability to identify synonyms, guaranteeing that users can still locate what they're looking for even if they use alternative terms.

Handling Ambiguity: NLP provides more accurate answers by taking into account the context in which words are used in order to resolve ambiguities.

Choose 3RDi Search with NLP as your Search Partner

3RDi Search processes natural language queries using state-of-the-art NLP algorithms to deliver highly relevant search results. The platform is scalable to manage massive data volumes and adaptable to the unique requirements of different sectors. Real-time insights from data are provided by 3RDi Search, facilitating prompt and well-informed decision-making. Users can easily execute sophisticated searches without requiring substantial technical expertise because to the straightforward UI. In summary, semantic search combined with natural language processing is revolutionizing data interaction by improving search usability and relevancy of results. 3RDi Search stands out as a leading solution, leveraging the full potential of NLP to deliver exceptional search experiences.

[software-techonline]

Enterprise Search and Discovery: Challenges and a Comprehensive Solution

Understanding the paradigm shift in information retrieval, insights and data analytics In today’s competitive, data-intensive world, only a company that is able to use information faster and better can survive. The methods that were used to collect customer insights, the formats of the information, the multitude of platforms and applications and the nature of the content have all undergone a massive transformation. Needless to say, the information discovery tools have also changed. This article will cover the key challenges of Enterprise Search and Discovery and provide a comprehensive solution to tackle these challenges. Exponential growth of unstructured data Unstructured data is growing faster than ever. Given the complexity of the data and its sheer volume, expenses with regards to its analysis, storage, accessibility and security are also on the rise. Enterprises are finding it difficult to keep these expenses under control. Matching the search speeds with the speed of increase in data is also a challenge. Relevance More often than not, the results of an Enterprise Search do not match the expectations of users. This is what differentiates it from say a Google search. While the latter shows relevant results using backlinks and linking across webpages, the former needs a platform that understands the industry and context sensitivity and also has inbuilt controlled vocabularies, among other factors. Variety of data Data can be in the form of a webpage, an image, an audio/video file or a document, to name just a few formats. Another aspect of information is the language it’s in. It could be a local language or an international one. It is essential that the Search Platform has inbuilt Natural Language Processing (NLP) and other applications for Advanced Chunking and Sentiment Analysis. Shorter time cycle Unlike a generic Google search, a lot rides on Enterprise Search results. Critical decisions with regards to meeting customer expectations, medical diagnosis, legal cases and STP publishing, to name a few, depend on this search. In today’s dynamic environment, changes happen quickly and hence, the response to these changes also need to speedup. Information findability Irrelevant information, distracting advertisements and absence of good filters, all act as hindrances to the Search and Discovery process. Not only do these factors make the process time consuming, they also result in a loss of effort and money. Furthermore, once the information is discovered, determining its trustworthiness is also a lengthy process. An Ideal Solution So, taking into account the factors above, we can conclude that the ideal Enterprise Search platform is one that has all of these factors and is built to derive key insights from organizational data, a major chunk of which is in the unstructured form. To uncover the hidden insights from chunks of unstructured data, the new generation of platforms have an array of built-in text mining functionalities. Coveo, Commvault, 3RDi Search, and Algolia are all examples of such modern enterprise search platforms. These platforms are equipped with advanced security features, a distributed cloud environment, collaborative features, a set of vocabularies for different domains, and many more features.

from WordPress https://ift.tt/2TprOSd via IFTTT

[premayogan]

The Future of Enterprise Search

Enterprise search tools today have redefined the way organizations look at unstructured data and has opened new opportunities for tapping its potential. Search is witnessing interesting developments and the stupendous rate at which the field is developing only holds unlimited possibilities for the future. The rapidly growing and evolving technology can be exciting to someone who is new to this field. After all, it has come a long way from the days of yore when keywords and phrases were the only way to fetch results. Today, we have semantics – a revolutionary technology that still has a lot to offer. It has helped to not just make the process faster and easier, but also much more accurate and relevant. This article looks into the future to find out what’s in store for us in the field of enterprise search.

Enterprise Search at Present

The introduction of technologies like semantics, Natural Language Processing (NLP) and artificial intelligence has paved the way for enterprise search. Enterprise search tools help enterprises browse through and derive insights from volumes of organizational data, most of which is unstructured. Some of the tools are 3RDi Enterprise, Commvault, Copernic. These tools are built using enterprise search engines like Solr. Previously, it was next to impossible to decipher unstructured data. It was because of the limited scope of traditional keyword-based approach. However, all this has changed now and it would be exciting to peek into the future to know what’s in store.  

What the Future Holds

A study of the trends reveals that the future indeed has interesting things in store! The future of enterprise search will be all about making the experience more fruitful and relevant for the user. In other words, the future will be about fetching accurate results without efforts to type in keywords. It also means, opening doors to methods that we can only dream of right now.

Visual Search

The future is moving towards ‘queryless search’. It simply means fetching results without the need for the user to type in keywords. Some ecommerce apps have started adapting this technology. All you need to do is click a picture with the ‘click photo’ feature within the app and voila! You see an entire list of products in the store that are exactly similar to the subject of your photograph. Sounds great, isn’t it? Well, this is nothing but visual search at work and the future will witness its increased scope and application. After all, so many times it happens that we just can’t find words to describe what we want or are looking for, which can be a challenge. The future will see us overcoming this challenge.

Voice Search

Morgan Stanley estimates that by 2020 there will be more than 75 billion connected devices. This will be made possible by the growth of Internet of Things (IoT). Voice search has witnessed significant success in our times, having reached the 95% accuracy threshold. Google Home and Amazon Alexa are good examples of the advent of this new technology. However, it is long before enterprise search tools adopt this technology.

Vertical Search

Heard of the term ‘targeted search’, haven’t you? Well, vertical search will be all about giving it an all new dimension. It will be all about enhanced user experience that is more targeted and streamlined, say experts. Instead of people from all domains using the same tool, it will be about different tools built to cater to different niche domains. This can be seen to some extent with the domain specific vocabulary available with new age platforms like 3RDi.  Enterprise search will soon come with a built-in vocabulary set dedicated to different domains. In other words, it is all about offering a customized user experience.  

The Final Word

There are limitless possibilities that the future holds with enterprise search engines like Solr promising more advanced features in future. So it’s important that the enterprises learn about them and take steps to implement them. This will help them to create an enriched user experience and make them stay ahead of the game. Read the full article