N-gram language modelling, a proven and effective method in NLP, is widely used to calculate the probability of a sentence in natural language. Language pattern is a linguistic level between ...word/character and sentence, which exists in pattern grammar. In this research, the approach of language model and language pattern are combined for the first time, and language patterns are studied by use of the N-gram model. Chinese function-word-centered patterns are extracted from the LCMC corpus, and aligned into pattern chains. The language model is trained from these chains to investigate the properties and distribution of Chinese function words, the interaction of content words and function words, and the interaction between patterns. The results indicate that there are approximately 10,000 function-word-centered patterns in the texts, which are distributed exponentially. This research summarizes the most common function-word-centered patterns and content-word-centered patterns, and discusses the interactions of patterns based on corpus data. The bigram language model of these patterns reflects the restrictions of function words. In addition, the research adopts an innovative method to visualize the interactions between patterns. This research fills the research gap between word/character and sentence and reveals basic Chinese pattern categories and the interactions between patterns, which makes a significant contribution to Chinese linguistic research, and improves the efficiency of NLP.
Interpreting a seemingly simple function word like “or,” “behind,” or “more” can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition ...theories have often relied on positing a foundation of innate knowledge. Yet recent neural‐network‐based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof‐of‐concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non‐symbolic general statistical learning algorithms, without any prior knowledge of linguistic meaning.
It has been proposed that starting from meaning that the child derives directly from shared experience with others, adult narrative enriches this meaning and its structure, providing causal links ...between unseen intentional states and actions. This would require a means for representing meaning from experience-a situation model-and a mechanism that allows information to be extracted from sentences and mapped onto the situation model that has been derived from experience, thus enriching that representation. We present a hypothesis and theory concerning how the language processing infrastructure for grammatical constructions can naturally be extended to narrative constructions to provide a mechanism for using language to enrich meaning derived from physical experience. Toward this aim, the grammatical construction models are augmented with additional structures for representing relations between events across sentences. Simulation results demonstrate proof of concept for how the narrative construction model supports multiple successive levels of meaning creation which allows the system to learn about the intentionality of mental states, and argument substitution which allows extensions to metaphorical language and analogical problem solving. Cross-linguistic validity of the system is demonstrated in Japanese. The narrative construction model is then integrated into the cognitive system of a humanoid robot that provides the memory systems and world-interaction required for representing meaning in a situation model. In this context proof of concept is demonstrated for how the system enriches meaning in the situation model that has been directly derived from experience. In terms of links to empirical data, the model predicts strong usage based effects: that is, that the narrative constructions used by children will be highly correlated with those that they experience. It also relies on the notion of narrative or discourse function words. Both of these are validated in the experimental literature.
Contemporary language models heavily rely on large corpora for their training. The larger the corpus, the better a model can capture various semantic relationships. The issue at hand appears to be ...the limited scope of the corpora used. One potential solution to this problem is the application of data augmentation techniques to expand the existing corpus. Data augmentation encompasses several techniques for corpus augmentation. In this article, we delve deeper into the analysis of three techniques: Synonym Replacement, Back Translation, and Reduction of Function Words. Utilizing these three techniques, we prepared diverse versions of the corpus employed for training Word2Vec Skip-gram models. These techniques were validated through extrinsic evaluation, wherein Word2Vec Skip-gram models were used to generate word vectors for classifying fake news articles. Performance measures of the generated classifiers were analyzed. The study highlights significant statistical differences in classifier outcomes between augmented and original corpora. Specifically, Back Translation significantly enhances accuracy, notably with Support Vector and Bernoulli Naive Bayes models. Conversely, the Reduction of Function Words (FWD) improves Logistic Regression, while the original corpus excels in Random Forest classification. The article also includes an intrinsic evaluation involving lexical semantic relations between word pairs. The intrinsic evaluation highlights nuanced differences in semantic relations across augmented corpora. Notably, the Back Translation (BT) corpus better aligns with established lexical resources, showcasing promising improvements in understanding specific semantic relationships.
The present study investigated the effect of certain unique morphophonemic features of Kannada words on the rate of stutters in a group of 22 adolescent and adult persons who stuttered in an oral ...reading task. A linear regression analysis showed that word length ranging from 1 to 8 syllables was a potent variable in the occurrence of stutters accounting for 25.3% of stutters. A composite index of morphophonemic complexity with points assigned for sandhi, geminates, consonant clusters, and number of morphemes accounted for a small 7.5% variability in observed stutter rates. Sandhi words and the hybrid content-function words were no more effective than other words in determining stutter rates. Results are discussed in relation to past findings for other languages and current neurolinguistic models of speech production.
This article is concerned with the possibility that syntactic structure may feature “small stuff” not just at the very top of the clause, but also lower down, in the domain that would usually be ...regarded as the lexical domain. The analysis is based on a range of (dialectal) Dutch and Frisian data, suggesting an initial analysis for the relevant morphosyntactic facts in terms of which a “smaller” higher category - what superficially looks like the definite and indefinite article in the relevant systems - seems to be located at the bottom of a nominal structure.
Many studies show that, when children learn new content words, they can utilize the appropriate types and appropriate amount of linguistic information. However, no existing study has examined what ...factors influence children to select the appropriate types and appropriate amount of linguistic information. In this study, we reviewed previous research on how children learn content words that are appropriate for their linguistic context. Through our review of the literature, we found three factors—validity, frequency, and redundancy—that influence how children select linguistic information. Based on these three factors, we further introduced studies that highlight the effectiveness of linguistic information on function word learning to understand how children learn function words, and how the factors affect function word learning in children. Existing studies also report that linguistic information is involved in function word learning. The three factors also affected the selection of their linguistic information. However, the extent that validity, frequency, and redundancy affected content words varied. In particular, linguistic information with a high validity is effective for learning content words, but not for learning function words.
Highlights • More disfluencies in L2 compared to L1. • More disfluencies on content words compared to function words in both L1 and L2. • More disfluencies on content words in L1 compared to L2. • ...More disfluencies on function words in L2 compared to L1. • Type of disfluencies did not vary between two languages.
Our case presented with cerebral infarction with global aphasia. At the beginning of the onset, the patient was mute, but a few days later showed real word recurrent utterance (RWRU) of the word ...“mild”. About 10 days after onset, her RU changed to RWRU with function words. About 8 months after onset, significant improvements were detected in the comprehension, repetition, reading aloud, and reading compre-hension subtests of the Standard Language Test of Aphasia. However, at the same time, a new RWRU called “Yakult” (name of a beverage product) was also mixed in her speech, and the appearances of RWRUs were limited to the content word parts. Based on “good maintenance of comprehension and repetition” and “the fact that her RUs were RWRUs”, we considered that the failure of the word-finding process in the phono-logical output lexicon could have caused the output of her RUs. In addition, the strong stereotypy of RU indicated that the promotion or suppression of the intended / un-intended vocabulary was difficult to control. We considered the possibility that damage to the basal ganglia, common in RU cases, had an effect on the imbalance of neural circuit activity (hyperdirect pathway, direct pathway, an indirect pathway) of the basal ganglia.
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