Word Frequency

metric ID: wordfrequency

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Publication: TIPSTER (2002)

DOI: nan

Zipf’s law states that the frequency of word tokens in a large corpus of natural language is inversely proportional to the rank. The law is investigated for two languages English …...

6482

Trend-based Document Clustering for Sensitive and Stable Topic Detection

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Publication: TIPSTER (2008)

DOI: nan

. The ability to detect new topics and track them is important given the huge amounts of documents. This paper introduces a trend-based document clustering algorithm for analyzing them. Its …...

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How Specialized are Specialized Corpora? Behavioral Evaluation of Corpus Representativeness for Maltese.

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AbstractIn this paper we bring to light a novel intersection between corpus linguistics and behavioral data that can be employed as an evaluation metric for resources for low-density languages, drawing …...

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Creating a massively parallel Bible corpus

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Publication: TIPSTER (2014)

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AbstractWe present our ongoing effort to create a massively parallel Bible corpus. While an ever-increasing number of Bible translations is available in electronic form on the internet, there is no …...

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A Turkish Database for Psycholinguistic Studies Based on Frequency, Age of Acquisition, and Imageability

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AbstractThis study primarily aims to build a Turkish psycholinguistic database including three variables: word frequency, age of acquisition (AoA), and imageability, where AoA and imageability information are limited to nouns. …...

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A large-scale study of the effects of word frequency and predictability in naturalistic reading

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Publication: TIPSTER (2019)

DOI: nan

AbstractA number of psycholinguistic studies have factorially manipulated words’ contextual predictabilities and corpus frequencies and shown separable effects of each on measures of human sentence processing, a pattern which has …...

21879

Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation

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AbstractSequence-to-sequence models for open-domain dialogue generation tend to favor generic, uninformative responses. Past work has focused on word frequency-based approaches to improving specificity, such as penalizing responses with only common …...

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Serial Recall Effects in Neural Language Modeling

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Publication: TIPSTER (2019)

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AbstractSerial recall experiments study the ability of humans to recall words in the order in which they occurred. The following serial recall effects are generally investigated in studies with humans: …...

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UTFPR at SemEval-2021 Task 1: Complexity Prediction by Combining BERT Vectors and Classic Features

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AbstractWe describe the UTFPR systems submitted to the Lexical Complexity Prediction shared task of SemEval 2021. They perform complexity prediction by combining classic features, such as word frequency, n-gram frequency, …...

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Word Discriminations for Vocabulary Inventory Prediction

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AbstractThe aim of vocabulary inventory prediction is to predict a learner’s whole vocabulary based on a limited sample of query words. This paper approaches the problem starting from the 2-parameter …...

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NyLLex: A Novel Resource of Swedish Words Annotated with Reading Proficiency Level

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AbstractWhat makes a text easy to read or not, depends on a variety of factors. One of the most prominent is, however, if the text contains easy, and avoids difficult, …...

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Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency Gaps

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Publication: TIPSTER (2022)

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AbstractIR models using a pretrained language model significantly outperform lexical approaches like BM25. In particular, SPLADE, which encodes texts to sparse vectors, is an effective model for practical use because …...

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Word Acquisition in Neural Language Models

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Publication: TIPSTER (2022)

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AbstractWe investigate how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words on the MacArthur-Bates Communicative Development Inventory (Fenson et …...

26786