The meaning of “life” and other NLP storiesA Pythonic introduction to compositional semantics for language enthusiasts.
Jacopo TagliabueBlockedUnblockFollowFollowingJan 20The meaning of “meaning”“We are stuck with technology when what we really want is just stuff that works.
” — D.
AdamsEven with all the promises by tech giants in this A.
era and all the brand new gadgets rapidly filling up our homes, it is still fairly easy to stumble upon frustrating examples of complete misunderstanding of human language by machines.
After dozens of million of dollars, we got this:Some A.
epic fails — I’m sure you have your own top 10.
while we were expecting this (where’s my jetpack, by the way?):A.
writing a piece of music for you (awwww).
Why?The short answer is that understanding language is very hard, as solving the language riddle means navigating through a network of equally hard questions on the limits of our cognitive abilities, the power of logical representations and the quirks and biases of human societies: years after Plato’s thoughts on the matter, we are still really far from having a good theory of it.
Central to “natural language processing” (or “natural language understanding”, NLU, as the cool kids say these days) is obviously the concept of meaning: it could be argued that one of the reasons we are still behind in NLU is that we don’t have a good, unified perspective on what meaning is.
For technical and historical reasons, the literature is somewhat split between two perspectives:there’s a “statistical” view, as exemplified for example by word2vec and related works, carried out mostly within the machine learning community: the focus here is mostly on lexical items and semantic relations like synonymity; typically, these analyses become the backbone for downstream systems addressing challenges such as sentiment analysis and text classification.
In a slogan, meaning is a vector in a multi-dimensional semantic space;there’s a “functional” view on meaning, as exemplified for example by this and related works, carried out mostly by linguists, philosophers of language and logicians: the focus here is mostly on systematic rules of inference and semantic relations like entailment; typical tasks are automated reasoning and knowledge representation.
In a slogan, meaning is a function from pieces of language (e.
nouns, connectives, etc.
) to set-theoretic objects (e.
elements of a set, functions, etc.
While the distinction is obviously a bit simplistic (although, for example, very close to the background for.