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Bootstrapping is a term used in language acquisition in the field of linguistics. It refers to the idea that humans are born innately equipped with a mental faculty that forms the basis of language. It is this language faculty that allows children to effortlessly acquire language. [1] As a process, bootstrapping can be divided into different ...
Prosodic bootstrapping. Prosodic bootstrapping (also known as phonological bootstrapping) in linguistics refers to the hypothesis that learners of a primary language (L1) use prosodic features such as pitch, tempo, rhythm, amplitude, and other auditory aspects from the speech signal as a cue to identify other properties of grammar, such as ...
Semantic bootstrapping is a linguistic theory of child language acquisition which proposes that children can acquire the syntax of a language by first learning and recognizing semantic elements and building upon, or bootstrapping from, that knowledge. [1] This theory proposes that children, when acquiring words, will recognize that words label ...
Syntactic bootstrapping. Syntactic bootstrapping is a theory in developmental psycholinguistics and language acquisition which proposes that children learn word meanings by recognizing syntactic categories (such as nouns, adjectives, etc.) and the structure of their language. It is proposed that children have innate knowledge of the links ...
Artificial intelligence and machine learning. Bootstrapping is a technique used to iteratively improve a classifier 's performance. Typically, multiple classifiers will be trained on different sets of the input data, and on prediction tasks the output of the different classifiers will be combined.
Distributional semantics [1] is a research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between linguistic items based on their distributional properties in large samples of language data. The basic idea of distributional semantics can be summed up in the so-called distributional ...
Hoff-Ginsberg and Shatz present a clear and broad academic literature review on language acquisition. They focus on semantic and syntactic development, models of acquisition, and briefly summarize previous research findings. As the article has good breadth, it only briefly touches on the concept of bootstrapping.
The most direct way of using the Web (and other corpora) to enhance WSD performance is the automatic acquisition of sense-tagged corpora, the fundamental resource to feed supervised WSD algorithms. Although this is far from being commonplace in the WSD literature, a number of different and effective strategies to achieve this goal have already ...