All languages have something analogous to words in this way, although it can be hard to know where to draw the boundaries sometimes.
Technically the smallest indivisible unit that bears meaning is the morpheme, not the word. For example the word “cats” in English consists of two morphemes, cat+s. The first morpheme can stand on its own as a word, but the second can’t.
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All languages have something analogous to words in this way, although it can be hard to know where to draw the boundaries sometimes.
Technically the smallest indivisible unit that bears meaning is the morpheme, not the word. For example the word “cats” in English consists of two morphemes, cat+s. The first morpheme can stand on its own as a word, but the second can’t.
I agree, but I think the trickier part is that the semantics of words are even blurrier/more ambiguous than the syntax.
Yeah, hence the turn away from dictionary definitions and things like WordNet towards continuous distributional vector representations in NLP.
I don’t think you could really give an uncontroversial symbolic definition for any natural word.