GraphAware Blog - NLP

Find out what's new in the Neo4j world

Caring about sentiment: how to get the most from people feelings

17 Sep 2018 by Dr. Vlasta Kůs, Dr. Alessandro Negro Sentiment Analysis doc2vec NLP Knowledge Graph

It is often useful to relate a piece of text with the sentiment expressed in it. Extracting and processing sentiments from text provides not only a new emotional access pattern to your corpus but also new knowledge which can reveal new insights. Suppose you want to build a recommendation engine which leverages reviews to spot detailed strengths and weaknesses of different hotels, such as good location but bad staff. Or, it certainly makes a difference whether an article talks about your organization in a positive or negative manner.

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Deep text understanding combining Graph Models, Named Entity Recognition and Word2Vec

10 Sep 2018 by Dr. Vlasta Kůs, Dr. Alessandro Negro word2vec NLP NER Knowledge Graph

One of the key components of Information Extraction (IE) and Knowledge Discovery (KD) is Named Entity Recognition, which is a machine learning technique that provides us with generalization capabilities based on lexical and contextual information. Named Entities are specific language elements that belong to certain predefined categories, such as persons names, locations, organizations, chemical elements or names of space missions. They are not easy to find and subsequently classify (for example, organizations and space missions share similar formatting and sometimes even context), but having them is of significant help for various tasks:

  • improving search capabilities
  • relating documents among themselves or with external information (such as connecting people in a financial document with information from a business registry)
  • relating causes (e.g weather conditions, accidents, regulatory changes) with effects (e.g. flight or tram delay, stock price changes)

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Advanced Document Representation

03 Sep 2018 by Dr. Vlasta Kůs, Dr. Alessandro Negro doc2vec NLP Knowledge Graph

Representation is one of the most complex and compelling tasks in machine learning. The way in which we represent facts, events, objects, labels, etc. affects how an autonomous learning agent can analyze them and extract insights, make predictions and deliver knowledge.

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Efficient unsupervised keywords extraction using graphs

03 Oct 2017 by Alessandro Negro, Vlasta Kůs, Miro Marchi, Christophe Willemsen Neo4j NLP Knowledge Platform Cognitive Computing

Companies of any size have to manage and access huge amounts of data providing advanced services for their end-users or to handle their internal processes. The greater part of this data is usually stored in the form of text. Processing and analyzing this huge source of knowledge represents a competitive advantage, but often, even providing simple and effective access to it is a complex task, due to the unstructured nature of the textual data. This blog post will focus on a specific use case: provide effective access to a huge set of documents - later referred as a corpus - identifying main concepts , organizing indexes and providing an adequate visualization.

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Using NLP + Neo4j for a Social Media Recommendation Engine

04 Oct 2016 by Alessandro Negro Neo4j NLP

In recent years, the rapid growth of social media communities has created a vast amount of digital documents on the web. Recommending relevant documents to users is a strategic goal for the effectiveness of customer engagement but at the same time is not a trivial problem.

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