134 research outputs found
The entropy of words-learnability and expressivity across more than 1000 languages
The choice associated with words is a fundamental property of natural languages. It lies at the heart of quantitative linguistics, computational linguistics and language sciences more generally. Information theory gives us tools at hand to measure precisely the average amount of choice associated with words: the word entropy. Here, we use three parallel corpora, encompassing ca. 450 million words in 1916 texts and 1259 languages, to tackle some of the major conceptual and practical problems of word entropy estimation: dependence on text size, register, style and estimation method, as well as non-independence of words in co-text. We present two main findings: Firstly, word entropies display relatively narrow, unimodal distributions. There is no language in our sample with a unigram entropy of less than six bits/word. We argue that this is in line with information-theoretic models of communication. Languages are held in a narrow range by two fundamental pressures: word learnability and word expressivity, with a potential bias towards expressivity. Secondly, there is a strong linear relationship between unigram entropies and entropy rates. The entropy difference between words with and without co-textual information is narrowly distributed around ca. three bits/word. In other words, knowing the preceding text reduces the uncertainty of words by roughly the same amount across languages of the world.Peer ReviewedPostprint (published version
Empirical approaches for investigating the origins of structure in speech
© John Benjamins Publishing Company. In language evolution research, the use of computational and experimental methods to investigate the emergence of structure in language is exploding. In this review, we look exclusively at work exploring the emergence of structure in speech, on both a categorical level (what drives the emergence of an inventory of individual speech sounds), and a combinatorial level (how these individual speech sounds emerge and are reused as part of larger structures). We show that computational and experimental methods for investigating population-level processes can be effectively used to explore and measure the effects of learning, communication and transmission on the emergence of structure in speech. We also look at work on child language acquisition as a tool for generating and validating hypotheses for the emergence of speech categories. Further, we review the effects of noise, iconicity and production effects
Conceptual similarity and communicative need shape colexification:An experimental study
Colexification refers to the phenomenon of multiple meanings sharing one word
in a language. Cross-linguistic lexification patterns have been shown to be
largely predictable, as similar concepts are often colexified. We test a recent
claim that, beyond this general tendency, communicative needs play an important
role in shaping colexification patterns. We approach this question by means of
a series of human experiments, using an artificial language communication game
paradigm. Our results across four experiments match the previous
cross-linguistic findings: all other things being equal, speakers do prefer to
colexify similar concepts. However, we also find evidence supporting the
communicative need hypothesis: when faced with a frequent need to distinguish
similar pairs of meanings, speakers adjust their colexification preferences to
maintain communicative efficiency, and avoid colexifying those similar meanings
which need to be distinguished in communication. This research provides further
evidence to support the argument that languages are shaped by the needs and
preferences of their speakers
Optimal coding and the origins of Zipfian laws
The problem of compression in standard information theory consists of
assigning codes as short as possible to numbers. Here we consider the problem
of optimal coding -- under an arbitrary coding scheme -- and show that it
predicts Zipf's law of abbreviation, namely a tendency in natural languages for
more frequent words to be shorter. We apply this result to investigate optimal
coding also under so-called non-singular coding, a scheme where unique
segmentation is not warranted but codes stand for a distinct number. Optimal
non-singular coding predicts that the length of a word should grow
approximately as the logarithm of its frequency rank, which is again consistent
with Zipf's law of abbreviation. Optimal non-singular coding in combination
with the maximum entropy principle also predicts Zipf's rank-frequency
distribution. Furthermore, our findings on optimal non-singular coding
challenge common beliefs about random typing. It turns out that random typing
is in fact an optimal coding process, in stark contrast with the common
assumption that it is detached from cost cutting considerations. Finally, we
discuss the implications of optimal coding for the construction of a compact
theory of Zipfian laws and other linguistic laws.Comment: in press in the Journal of Quantitative Linguistics; definition of
concordant pair corrected, proofs polished, references update
The cross-linguistic performance of word segmentation models over time.
We select three word segmentation models with psycholinguistic foundations - transitional probabilities, the diphone-based segmenter, and PUDDLE - which track phoneme co-occurrence and positional frequencies in input strings, and in the case of PUDDLE build lexical and diphone inventories. The models are evaluated on caregiver utterances in 132 CHILDES corpora representing 28 languages and 11.9 m words. PUDDLE shows the best performance overall, albeit with wide cross-linguistic variation. We explore the reasons for this variation, fitting regression models to performance scores with linguistic properties which capture lexico-phonological characteristics of the input: word length, utterance length, diversity in the lexicon, the frequency of one-word utterances, the regularity of phoneme patterns at word boundaries, and the distribution of diphones in each language. These properties together explain four-tenths of the observed variation in segmentation performance, a strong outcome and a solid foundation for studying further variables which make the segmentation task difficult
Optimization models of natural communication
A family of information theoretic models of communication was introduced more than a decade ago to explain the origins of Zipf’s law for word frequencies. The family is a based on a combination of two information theoretic principles: maximization of mutual information between forms and meanings and minimization of form entropy. The family also sheds light on the origins of three other patterns: the principle of contrast; a related vocabulary learning bias; and the meaning-frequency law. Here two important components of the family, namely the information theoretic principles and the energy function that combines them linearly, are reviewed from the perspective of psycholinguistics, language learning, information theory and synergetic linguistics. The minimization of this linear function is linked to the problem of compression of standard information theory and might be tuned by self-organization.Peer ReviewedPostprint (author's final draft
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