Frequent frames as a cue for grammatical categories in child directed speech

This paper introduces the notion of frequent frames, distributional patterns based on co-occurrence patterns of words in sentences, then investigates the usefulness of this information in grammatical categorization. A frame is defined as two jointly occurring words with one word intervening. Qualitative and quantitative results from distributional analyses of six different corpora of child directed speech are presented in two experiments. In the analyses, words that were surrounded by the same frequent frame were categorized together. The results show that frequent frames yield very accurate categories. Furthermore, evidence from behavioral studies suggests that infants and adults are sensitive to frame-like units, and that adults use them to categorize words. This evidence, along with the success of frames in categorizing words, provides support for frames as a basis for the acquisition of grammatical categories.     

Global and Local Feature Distinctiveness Effects in Language Acquisition

Various aspects of semantic features drive early vocabulary development, but less is known about how the global and local structure of the overall semantic feature space influences language acquisition. A feature network of English words was constructed from a large database of adult feature production norms such that edges in the network represented feature distances between words (i.e., Manhattan distances of probability distributions of features elicited for each pair of words). A word's global feature distinctiveness is measured with respect to all other words in the network and a word's local feature distinctiveness is measured relative to words in sub-networks derived from clustering analyses. This paper investigates how feature distinctiveness of individual words at local and global scales of the network influences language acquisition. Regression analyses indicate that global feature distinctiveness was associated with earlier age of acquisition ratings, and was a stronger predictor of age of acquisition than local feature distinctiveness. These results suggest that the global structure of the semantic feature network could play an important role in language acquisition, whereby globally distinctive concepts help to structure vocabulary development over the lifespan.     

Words with Consistent Diachronic Usage Patterns are Learned Earlier: A Computational Analysis Using Temporally Aligned Word Embeddings

In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data-driven, scalable way, which is grounded in language use. We show a unique and reliable relation between measures of language change and age of acquisition (AoA) while controlling for frequency, contextual diversity, concreteness, length, dominant part of speech, orthographic neighborhood density, and diachronic frequency variation. We analyze measures of language change tackling both the change in lexical representations and the change in the relation between lexical representations and the words with the most similar usage patterns, showing that they capture different aspects of language change. Our results show a unique relation between language change and AoA, which is stronger when considering neighborhood-level measures of language change: Words with more coherent diachronic usage patterns tend to be acquired earlier. The results support theories positing a link between ontogenetic and ethnogenetic processes in language.     

The differential role of phonological and distributional cues in grammatical categorisation

Recognising the grammatical categories of words is a necessary skill for the acquisition of syntax and for on-line sentence processing. The syntactic and semantic context of the word contribute as cues for grammatical category assignment, but phonological cues, too, have been implicated as important sources of information. The value of phonological and distributional cues has not, with very few exceptions, been empirically assessed. This paper presents a series of analyses of phonological cues and distributional cues and their potential for distinguishing grammatical categories of words in corpus analyses. The corpus analyses indicated that phonological cues were more reliable for less frequent words, whereas distributional information was most valuable for high frequency words. We tested this prediction in an artificial language learning experiment, where the distributional and phonological cues of categories of nonsense words were varied. The results corroborated the corpus analyses. For high-frequency nonwords, distributional information was more useful, whereas for low-frequency words there was more reliance on phonological cues. The results indicate that phonological and distributional cues contribute differentially towards grammatical categorisation.     

iMinerva: A Mathematical Model of Distributional Statistical Learning

Statistical learning refers to the ability to identify structure in the input based on its statistical properties. For many linguistic structures, the relevant statistical features are distributional: They are related to the frequency and variability of exemplars in the input. These distributional regularities have been suggested to play a role in many different aspects of language learning, including phonetic categories, using phonemic distinctions in word learning, and discovering non‐adjacent relations. On the surface, these different aspects share few commonalities. Despite this, we demonstrate that the same computational framework can account for learning in all of these tasks. These results support two conclusions. The first is that much, and perhaps all, of distributional statistical learning can be explained by the same underlying set of processes. The second is that some aspects of language can be learned due to domain‐general characteristics of memory.     

Differential impacts of age of acquisition on letter and semantic fluency in Alzheimer's disease patients and healthy older adults

The degree to which the typical age of acquisition (AoA) of words and word frequency have separable influences on verbal production tasks has been strongly debated. To examine the overlap between these factors in verbal fluency tasks, the performance of Alzheimer's disease (AD) patients (N?=?34) and normal elderly controls (N?=?36) was compared on semantic (e.g., vegetables) and letter (e.g., words that begin with F) fluency tasks. These comparisons revealed that words generated for the semantic fluency task had an earlier AoA while words generated for the letter fluency task had a higher word frequency. Differences in AoA between AD patients and controls were larger for semantic than letter fluency. These results suggest that AoA has an effect on verbal production that is independent of word frequency and that AoA has a semantic locus.     

Numerical morphology supports early number word learning: Evidence from a comparison of young Mandarin and English learners

Previous studies showed that children learning a language with an obligatory singular/plural distinction (Russian and English) learn the meaning of the number word for one earlier than children learning Japanese, a language without obligatory number morphology (Barner, Libenson, Cheung, & Takasaki, 2009; Sarnecka, Kamenskaya, Yamana, Ogura, & Yudovina, 2007). This can be explained by differences in number morphology, but it can also be explained by many other differences between the languages and the environments of the children who were compared. The present study tests the hypothesis that the morphological singular/plural distinction supports the early acquisition of the meaning of the number word for one by comparing young English learners to age and SES matched young Mandarin Chinese learners. Mandarin does not have obligatory number morphology but is more similar to English than Japanese in many crucial respects. Corpus analyses show that, compared to English learners, Mandarin learners hear number words more frequently, are more likely to hear number words followed by a noun, and are more likely to hear number words in contexts where they denote a cardinal value. Two tasks show that, despite these advantages, Mandarin learners learn the meaning of the number word for one three to six months later than do English learners. These results provide the strongest evidence to date that prior knowledge of the numerical meaning of the distinction between singular and plural supports the acquisition of the meaning of the number word for one.     

All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language

Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non‐adjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics influenced learning of both adjacent and non‐adjacent dependencies. Additionally, though accuracy was similar for both types of structure, participants’ knowledge of the deterministic non‐adjacent dependencies was more explicit than their knowledge of the probabilistic adjacent dependencies. The results are discussed in the context of current theories of statistical learning and language acquisition.     

Probing Lexical Ambiguity: Word Vectors Encode Number and Relatedness of Senses

Lexical ambiguity—the phenomenon of a single word having multiple, distinguishable senses—is pervasive in language. Both the degree of ambiguity of a word (roughly, its number of senses) and the relatedness of those senses have been found to have widespread effects on language acquisition and processing. Recently, distributional approaches to semantics, in which a word's meaning is determined by its contexts, have led to successful research quantifying the degree of ambiguity, but these measures have not distinguished between the ambiguity of words with multiple related senses versus multiple unrelated meanings. In this work, we present the first assessment of whether distributional meaning representations can capture the ambiguity structure of a word, including both the number and relatedness of senses. On a very large sample of English words, we find that some, but not all, distributional semantic representations that we test exhibit detectable differences between sets of monosemes (unambiguous words; N = 964), polysemes (with multiple related senses; N = 4,096), and homonyms (with multiple unrelated senses; N = 355). Our findings begin to answer open questions from earlier work regarding whether distributional semantic representations of words, which successfully capture various semantic relationships, also reflect fine-grained aspects of meaning structure that influence human behavior. Our findings emphasize the importance of measuring whether proposed lexical representations capture such distinctions: In addition to standard benchmarks that test the similarity structure of distributional semantic models, we need to also consider whether they have cognitively plausible ambiguity structure.     

Fine-grained sensitivity to statistical information in adult word learning

A language learner trying to acquire a new word must often sift through many potential relations between particular words and their possible meanings. In principle, statistical information about the distribution of those mappings could serve as one important source of data, but little is known about whether learners can in fact track multiple word-referent mappings, and, if they do, the precision with which they can represent those statistics. To test this, two experiments contrasted a pair of possibilities: that learners encode the fine-grained statistics of mappings in the input - both high- and low-frequency mappings - or, alternatively, that only high frequency mappings are represented. Participants were briefly trained on novel word-novel object pairs combined with varying frequencies: some objects were paired with one word, other objects with multiple words with differing frequencies (ranging from 10% to 80%). Results showed that participants were exquisitely sensitive to very small statistical differences in mappings. The second experiment showed that word learners' representation of low frequency mappings is modulated as a function of the variability in the environment. Implications for Mutual Exclusivity and Bayesian accounts of word learning are discussed.     

Using variability to guide dimensional weighting: associative mechanisms in early word learning

At 14 months, children appear to struggle to apply their fairly well-developed speech perception abilities to learning similar sounding words (e.g., bih/dih; Stager & Werker, 1997). However, variability in nonphonetic aspects of the training stimuli seems to aid word learning at this age. Extant theories of early word learning cannot account for this benefit of variability. We offer a simple explanation for this range of effects based on associative learning. Simulations suggest that if infants encode both noncontrastive information (e.g., cues to speaker voice) and meaningful linguistic cues (e.g., place of articulation or voicing), then associative learning mechanisms predict these variability effects in early word learning. Crucially, this means that despite the importance of task variables in predicting performance, this body of work shows that phonological categories are still developing at this age, and that the structure of noninformative cues has critical influences on word learning abilities.     

Dynamic self-organization and early lexical development in children

In this study we present a self-organizing connectionist model of early lexical development. We call this model DevLex-II, based on the earlier DevLex model. DevLex-II can simulate a variety of empirical patterns in children's acquisition of words. These include a clear vocabulary spurt, effects of word frequency and length on age of acquisition, and individual differences as a function of phonological short-term memory and associative capacity. Further results from lesioned models indicate developmental plasticity in the network's recovery from damage, in a non-monotonic fashion. We attribute the network's abilities in accounting for lexical development to interactive dynamics in the learning process. In particular, variations displayed by the model in the rate and size of early vocabulary development are modulated by (a) input characteristics, such as word frequency and word length, (b) consolidation of lexical-semantic representation, meaning-form association, and phonological short-term memory, and (c) delayed processes due to interactions among timing, severity, and recoverability of lesion. Together, DevLex and DevLex-II provide an accurate computational account of early lexical development.     

Familiar words can serve as a semantic seed for syntactic bootstrapping

Young children can exploit the syntactic context of a novel word to narrow down its probable meaning. But how do they learn which contexts are linked to which semantic features in the first place? We investigate if 3‐ to 4‐year‐old children (n = 60) can learn about a syntactic context from tracking its use with only a few familiar words. After watching a 5‐min training video in which a novel function word (i.e., ‘ko’) replaced either personal pronouns or articles, children were able to infer semantic properties for novel words co‐occurring with the newly learned function word (i.e., objects vs. actions). These findings implicate a mechanism by which a distributional analysis, associated with a small vocabulary of known words, could be sufficient to identify some properties associated with specific syntactic contexts.     

The secret is in the sound: from unsegmented speech to lexical categories

When learning language, young children are faced with many seemingly formidable challenges, including discovering words embedded in a continuous stream of sounds and determining what role these words play in syntactic constructions. We suggest that knowledge of phoneme distributions may play a crucial part in helping children segment words and determine their lexical category, and we propose an integrated model of how children might go from unsegmented speech to lexical categories. We corroborated this theoretical model using a two‐stage computational analysis of a large corpus of English child‐directed speech. First, we used transition probabilities between phonemes to find words in unsegmented speech. Second, we used distributional information about word edges – the beginning and ending phonemes of words – to predict whether the segmented words from the first stage were nouns, verbs, or something else. The results indicate that discovering lexical units and their associated syntactic category in child‐directed speech is possible by attending to the statistics of single phoneme transitions and word‐initial and final phonemes. Thus, we suggest that a core computational principle in language acquisition is that the same source of information is used to learn about different aspects of linguistic structure.     

Language specific prosodic preferences during the first half year of life: Evidence from German and French infants

There is converging evidence that infants are sensitive to prosodic cues from birth onwards and use this kind of information in their earliest steps into the acquisition of words and syntactic regularities of their target language. Regarding word segmentation, it has been found that English-learning infants segment trochaic words by 7.5 months of age, and iambic words only by 10.5 months of age [Jusczyk, P. W., Houston, D. M., & Newsome, M. (1999). The beginnings of word segmentation in English-learning infants. Cognitive Psychology, 39, 159–207]. The question remains how to interpret this finding in relation to results showing that English-learning infants develop a preference for trochaic over iambic words between 6 and 9 months of age [Jusczyk, P. W., Cutler, A., & Redanz, N. (1993). Preference for the predominant stress patterns of English words. Child Development, 64, 675–687]. In the following, we report the results of four experiments using the headturn preference procedure (HPP) to explore the trochaic bias issue in German- and French-learning infants. For German, a trochaic preference was found at 6 but not at 4 months, suggesting an emergence of this preference between both ages (Experiments 1 and 2). For French, 6-month-old infants did not show a preference for either stress pattern (Experiment 3) while they were found to discriminate between the two stress patterns (Experiment 4). Our findings are the first to demonstrate that the trochaic bias is acquired by 6 months of age, is language specific and can be predicted by the rhythmic properties of the language in acquisition. We discuss the implications of this very early acquisition for our understanding of the emergence of segmentation abilities.     

A fresh look at the predictors of naming accuracy and errors in Alzheimer's disease

In recent years, a considerable number of studies have tried to establish which characteristics of objects and their names predict the responses of patients with Alzheimer's disease (AD) in the picture-naming task. The frequency of use of words and their age of acquisition (AoA) have been implicated as two of the most influential variables, with naming being best preserved for objects with high-frequency, early-acquired names. The present study takes a fresh look at the predictors of naming success in Spanish and English AD patients using a range of measures of word frequency and AoA along with visual complexity, imageability, and word length as predictors. Analyses using generalized linear mixed modelling found that naming accuracy was better predicted by AoA ratings taken from older adults than conventional ratings from young adults. Older frequency measures based on written language samples predicted accuracy better than more modern measures based on the frequencies of words in film subtitles. Replacing adult frequency with an estimate of cumulative (lifespan) frequency did not reduce the impact of AoA. Semantic error rates were predicted by both written word frequency and senior AoA while null response errors were only predicted by frequency. Visual complexity, imageability, and word length did not predict naming accuracy or errors.     

Gavagai Is as Gavagai Does: Learning Nouns and Verbs From Cross‐Situational Statistics

Learning to map words onto their referents is difficult, because there are multiple possibilities for forming these mappings. Cross‐situational learning studies have shown that word‐object mappings can be learned across multiple situations, as can verbs when presented in a syntactic context. However, these previous studies have presented either nouns or verbs in ambiguous contexts and thus bypass much of the complexity of multiple grammatical categories in speech. We show that noun word learning in adults is robust when objects are moving, and that verbs can also be learned from similar scenes without additional syntactic information. Furthermore, we show that both nouns and verbs can be acquired simultaneously, thus resolving category‐level as well as individual word‐level ambiguity. However, nouns were learned more quickly than verbs, and we discuss this in light of previous studies investigating the noun advantage in word learning.     

A Computational Model for the Item‐Based Induction of Construction Networks

According to usage‐based approaches to language acquisition, linguistic knowledge is represented in the form of constructions—form‐meaning pairings—at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item‐based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous context. To determine meanings of constructions starting from ambiguous contexts, we rely on the principle of cross‐situational learning. While this mechanism has been implemented in several computational models, these models typically focus on learning mappings between words and referents. In contrast, in our model, we show how cross‐situational learning can be applied consistently to learn correspondences between form and meaning beyond such simple correspondences.     

Kindergarten predictors of three aspects of reading achievement

Kindergarten measures of intelligence, auditory perception, visual perception, and associative learning were used to predict three aspects of reading achievement (word attack, word recognition, and comprehension) at the end of Grades 1,2, and 3 for 79 subjects. The predictability of each measure was a function not only of grade, but also of the aspect of reading achievement being predicted. Multiple correlations of the predictors tended to increase across grade levels and were highest for the comprehension aspect of reading. The Number Facility subtest of the PMA was the over-all best predictor or reading achievement. Possible reasons for this and other findings are discussed.     

Composition in distributional models of semantics

Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in sentential priming) suggesting that semantic similarity is more complex than simply a relation between isolated words. This article proposes a framework for representing the meaning of word combinations in vector space. Central to our approach is vector composition, which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models that we evaluate empirically on a phrase similarity task.