From a machine point of view, human text and human utterances from language and speech are open to multiple interpretations because words may have more than one meaning which is also called lexical ambiguity. Semantic Modelling has gone through several peaks and valleys in the last 50 years. With the recent advancements of real-time human curation interlinked with supervised self-learning this technique has finally grown up into a core technology for the majority of today\u2019s NLP\/NLU systems. So, the next time you utter a sentence to Siri or Alexa \u2014 somewhere deep down in backend systems there is a Semantic Model working on the answer. Linguistic AmbiguityEven though the linguistic signatures of both sentences are practically the same, the semantic meaning is completely different.<\/p>\n
\n#[Project] Google ArXiv Papers with NLP semantic-search! Link to Github in the comments!! \ud83d\udcca #DataScience<\/a> \ud83e\uddee #DataVisualization<\/a> #DataAnalytics<\/a> #DataFam<\/a> https:\/\/t.co\/nVeGxHR01u<\/a><\/p>\n
— Rahul (@Rahul_B) February 20, 2023<\/a><\/p><\/blockquote>\n