Natural Language Processing
With PoolParty you benefit from the new generation of NLP methods that combine statistical and linguistic methods with graph-based artificial intelligence.
“By 2024, companies using graphs and semantic approaches for natural language technology (NLT) projects will have 75% less AI technical debt than those that don’t.”
Why does Natural Language Processing (NLP) matter?
NLP merges several disciplines like computer linguistics and semantic technologies to help computers understand human communication (for example, to create really working chatbots).
Human communication generates a large amount of unstructured data mostly hidden in textual form. NLP helps to resolve the ambiguity of unstructured data and makes it processable by machines.
NLP delivers more precise extraction and analytics tasks turning natural language into useful data.
NLP will become a game-changing technology as it is developing towards capturing the intent of human communication in what is known as natural language understanding (NLU).
How to benefit from PoolParty's Natural Language Processing capabilities?
PoolParty supports entity extraction based on knowledge graphs and machine learning. With PoolParty Entity Extractor, you fetch and store additional facts about the extracted entities, terms, relations, and shadow concepts to drive in-depth text analytics.
NLP tasks such as word sense disambiguation or lemmatization based on controlled vocabularies resolve language ambiguities with higher precision because data is in the right context.
A semantic knowledge model is expressive enough that you can even extract hidden relations for accurate text classification and annotation. This makes PoolParty a great tool for Deep Text Analytics.
PoolParty’s NLP capabilities are widely used to create and maintain Semantic Knowledge Graphs on a large scale. You can semi-automatically extend your semantic knowledge graph with corpus learning.
Semantic classification with PoolParty Semantic Classifier can automate any classification task (e.g. for customer retention management or for news classification) or will enhance your recommendation services.
NLP based on PoolParty supports content authoring and information retrieval with semantic similarity and content recommendation.
You can include PoolParty semantic similarity services in any analytics or search application. Also, PoolParty supports the creation of recommender systems that are built upon matching rules.
PoolParty’s NLP capabilities are not language dependent but domain specific, turning it into a general purpose NLP tool.
PoolParty NLP-based Applications
Based on PoolParty GraphSearch we developed a graph-based HR analytics demo application. We bundled several internal and external HR data sources over a semantic layer to gain insights about employee skills compared with salaries and industry trends.
Based on PoolParty Entity Extractor we annotated employees’ CVs with concepts from our HR taxonomy. We transformed all data into RDF and stored it to an RDF triple store. This way we have all data sources integrated and ready for multifaceted knowledge discovery, analytics, and recommendation functionalities.
PoolParty underpins automation solutions based on knowledge graphs, machine learning and natural language processing (NLP) for customer support services.
Bots and virtual assistant systems benefit from expressive semantic knowledge models that support users with guidance and a more precise interpretation of natural language queries.
PoolParty can enhance different chatbot frameworks with smart and contextualized entity extraction and classification services.
Who is working with PoolParty?
As a data scientist, you already use a set of next generation technologies that address data integration, information analytics, and metadata management. You know that ‘more data’ not necessarily means ‘smart data’. Semantic technologies complement the tool-set of today’s data scientists.
PoolParty helps you deliver delightful customer experiences. Without coding skills, you can build, test, and adapt customer journeys with ease. With a semantic layer on top of your content assets, you can better match the target audience with your content. You can also better coordinate the efforts of all of your content creators.