Word forms - not just their lengths- are optimized for efficient communication
The inverse relationship between the length of a word and the frequency of its use, first identified by G.K. Zipf in 1935, is a classic empirical law that holds across a wide range of human languages. We demonstrate that length is one aspect of a much more general property of words: how distinctive they are with respect to other words in a language. Distinctiveness plays a critical role in recognizing words in fluent speech, in that it reflects the strength of potential competitors when selecting the best candidate for an ambiguous signal. Phonological information content, a measure of a word's string probability under a statistical model of a language's sound or character sequences, concisely captures distinctiveness. Examining large-scale corpora from 13 languages, we find that distinctiveness significantly outperforms word length as a predictor of frequency. This finding provides evidence that listeners' processing constraints shape fine-grained aspects of word forms across languages.
Code (1)
Similar Papers 제목 키워드 기반
The optimality of word lengths. Theoretical foundations and an empirical study
Zipf's law of abbreviation, namely the tendency of more frequent words to be shorter, has been viewed as a manifestation of compression, i.e. the minimization of the length of forms -- a universal principle of natural co…
Revisiting the Optimality of Word Lengths
Zipf (1935) posited that wordforms are optimized to minimize utterances' communicative costs. Under the assumption that cost is given by an utterance's length, he supported this claim by showing that words' lengths are i…
End-to-End Open Vocabulary Keyword Search
Recently, neural approaches to spoken content retrieval have become popular. However, they tend to be restricted in their vocabulary or in their ability to deal with imbalanced test settings. These restrictions limit the…
RetrievalCan LLMs Track Their Output Length? A Dynamic Feedback Mechanism for Precise Length Regulation
Precisely controlling the length of generated text is a common requirement in real-world applications. However, despite significant advancements in following human instructions, Large Language Models (LLMs) still struggl…
Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation
Emojis have become a very popular part of daily digital communication. Their appeal comes largely in part due to their ability to capture and elicit emotions in a more subtle and nuanced way than just plain text is able …
Image GenerationWord Embeddings