For the runtime performance aware developerPhoto by Marc Sendra Martorell on UnsplashTransformer networks have dramatically changed the landscape of Natural Language Processing (NLP) over the last couple of years. The release of BERT has transformed search for the better, leading to a host of new semantic-driven applications. Data can now be found by topic, concept or similarity vs being purely keyword driven.Innovative models are being released at a blistering pace, with different…
Used in unsupervised machine learning tasks, Topic Modeling is treated as a form of tagging and primarily used for information retrieval wherein it helps in query expansion. It is vastly used in mapping user preference in topics across search engineers. The main applications of Topic Modeling are classification, categorization, summarization of documents. AI methodologies associated with genetics, social media, and computer vision tasks are associated with Topic Modeling. It also powers analysis on social networks pertaining to the sentiments of users.
Topic Modeling Difference and Related Algorithms
Topic Modeling is performed on unsupervised information and has a clear distinction from text classification and clustering tasks.
By Angelica Lo Duca, Researcher in Semantic Web, Data Integration, and Data Science.
What is Hypothesis Testing
According to Jim Frost, Hypothesis Testing is a form of inferential statistics that allows us to draw conclusions about an entire population based on a representative sample [..] In most cases, it is simply impossible to observe the entire population to understand its properties. The only alternative is to collect a random sample and then use statistics to analyze it .
When performing Hypothesis Testing, firstly, a hypothesis must be formulated. An example of a hypothesis is “there is a correlation between height and gender in a population,” or “there is a difference between two groups of a population.”
Usually, the thesis to be demonstrated is called the Alternative Hypothesis (HA), and its opposite is the Null Hypothesis (H0).
I’m relatively old school, semantically speaking: my first encounters with RDF was in the early 2000s, not long after Tim Berners-Lee’s now-famous article in Scientific American introducing the Semantic Web to the world. I remember working through the complexities of RDFS and OWL, spending a long afternoon with one of the editors of the SPARQL specification in 2007, promoting SPARQL 1.1 and SHACL in the mid-2010s, and watching as the technology went from being an outlier to having its moment in the sun just before COVID-19 hit.
I like SPARQL, but increasingly I have to admit a hard reality: there’s a new kid on the block that I think may very well dethrone the language, and perhaps even RDF. I’m not talking about Neo4J’s Cypher (which in its open incarnation is intriguing), or GQL, TigerGraph’s SQL-like language intended to bring SQL syntax to graph querying.
Not too long ago, chatbots and assistants didn’t work well. The ones that did, like Amazon Alexa, were developed by companies with enormous R&D budgets and specialized ML and NLP teams.
But due to leaps in the performance of NLP systems made after the introduction of transformers in 2017, combined with the open source nature of many of these models, the landscape is quickly changing. Companies like Rasa have made it easy for organizations to build sophisticated agents that not only work better than their earlier counterparts, but cost a fraction of the time and money to develop, and don’t require experts to design.