1-4 of 4 Results

  • Keywords: computational model x
Clear all


New Computational Methods and the Study of the Romance Languages  

Basilio Calderone and Vito Pirrelli

Nowadays, computer models of human language are instrumental to millions of people, who use them every day with little if any awareness of their existence and role. Their exponential development has had a huge impact on daily life through practical applications like machine translation or automated dialogue systems. It has also deeply affected the way we think about language as an object of scientific inquiry. Computer modeling of Romance languages has helped scholars develop new theoretical frameworks and new ways of looking at traditional approaches. In particular, computer modeling of lexical phenomena has had a profound influence on some fundamental issues in human language processing, such as the purported dichotomy between rules and exceptions, or grammar and lexicon, the inherently probabilistic nature of speakers’ perception of analogy and word internal structure, and their ability to generalize to novel items from attested evidence. Although it is probably premature to anticipate and assess the prospects of these models, their current impact on language research can hardly be overestimated. In a few years, data-driven assessment of theoretical models is expected to play an irreplaceable role in pacing progress in all branches of language sciences, from typological and pragmatic approaches to cognitive and formal ones.


Models of Human Sentence Comprehension in Computational Psycholinguistics  

John Hale

Computational models of human sentence comprehension help researchers reason about how grammar might actually be used in the understanding process. Taking a cognitivist approach, this article relates computational psycholinguistics to neighboring fields (such as linguistics), surveys important precedents, and catalogs open problems.


Computational Phonology  

Jane Chandlee and Jeffrey Heinz

Computational phonology studies the nature of the computations necessary and sufficient for characterizing phonological knowledge. As a field it is informed by the theories of computation and phonology. The computational nature of phonological knowledge is important because at a fundamental level it is about the psychological nature of memory as it pertains to phonological knowledge. Different types of phonological knowledge can be characterized as computational problems, and the solutions to these problems reveal their computational nature. In contrast to syntactic knowledge, there is clear evidence that phonological knowledge is computationally bounded to the so-called regular classes of sets and relations. These classes have multiple mathematical characterizations in terms of logic, automata, and algebra with significant implications for the nature of memory. In fact, there is evidence that phonological knowledge is bounded by particular subregular classes, with more restrictive logical, automata-theoretic, and algebraic characterizations, and thus by weaker models of memory.


Discriminative Learning and the Lexicon: NDL and LDL  

Yu-Ying Chuang and R. Harald Baayen

Naive discriminative learning (NDL) and linear discriminative learning (LDL) are simple computational algorithms for lexical learning and lexical processing. Both NDL and LDL assume that learning is discriminative, driven by prediction error, and that it is this error that calibrates the association strength between input and output representations. Both words’ forms and their meanings are represented by numeric vectors, and mappings between forms and meanings are set up. For comprehension, form vectors predict meaning vectors. For production, meaning vectors map onto form vectors. These mappings can be learned incrementally, approximating how children learn the words of their language. Alternatively, optimal mappings representing the end state of learning can be estimated. The NDL and LDL algorithms are incorporated in a computational theory of the mental lexicon, the ‘discriminative lexicon’. The model shows good performance both with respect to production and comprehension accuracy, and for predicting aspects of lexical processing, including morphological processing, across a wide range of experiments. Since, mathematically, NDL and LDL implement multivariate multiple regression, the ‘discriminative lexicon’ provides a cognitively motivated statistical modeling approach to lexical processing.