Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
415
result(s) for
"Word processing History."
Sort by:
Track changes : a literary history of word processing
\"The story of writing in the digital age is every bit as messy as the ink-stained rags that littered the floor of Gutenberg's print shop or the hot molten lead of the linotype machine. During the period of the pivotal growth and widespread adoption of word processing as a writing technology, some authors embraced it as a marvel while others decried it as the death of literature. The product of years of archival research and numerous interviews conducted by the author, Track Changes is the first literary history of word processing. Matthew Kirschenbaum examines how the interests and ideals of creative authorship came to coexist with the computer revolution. Who were the first adopters? What kind of anxieties did they share? Was word processing perceived as just a better typewriter or something more? How did it change our understanding of writing? Track Changes balances the stories of individual writers with a consideration of how the seemingly ineffable act of writing is always grounded in particular instruments and media, from quills to keyboards. Along the way, we discover the candidates for the first novel written on a word processor, explore the surprisingly varied reasons why writers of both popular and serious literature adopted the technology, trace the spread of new metaphors and ideas from word processing in fiction and poetry, and consider the fate of literary scholarship and memory in an era when the final remnants of authorship may consist of folders on a hard drive or documents in the cloud.\"--Provided by publisher.
Book
Historical representations of social groups across 200 years of word embeddings from Google Books
Using word embeddings from 850 billion words in English-language Google Books, we provide an extensive analysis of historical change and stability in social group representations (stereotypes) across a long timeframe (from 1800 to 1999), for a large number of social group targets (Black, White, Asian, Irish, Hispanic, Native American, Man, Woman, Old, Young, Fat, Thin, Rich, Poor), and their emergent, bottom-up associations with 14,000 words and a subset of 600 traits. The results provide a nuanced picture of change and persistence in stereotypes across 200 y. Change was observed in the top-associated words and traits: Whether analyzing the top 10 or 50 associates, at least 50% of top associates changed across successive decades. Despite this changing content of top-associated words, the average valence (positivity/negativity) of these top stereotypes was generally persistent. Ultimately, through advances in the availability of historical word embeddings, this study offers a comprehensive characterization of both change and persistence in social group representations as revealed through books of the English-speaking world from 1800 to 1999.
Journal Article
The Revised Hierarchical Model: A critical review and assessment
Brysbaert and Duyck (this issue) suggest that it is time to abandon the Revised Hierarchical Model (Kroll and Stewart, 1994) in favor of connectionist models such as BIA+ (Dijkstra and Van Heuven, 2002) that more accurately account for the recent evidence on non-selective access in bilingual word recognition. In this brief response, we first review the history of the Revised Hierarchical Model (RHM), consider the set of issues that it was proposed to address and then evaluate the evidence that supports and fails to support the initial claims of the model. Although fifteen years of new research findings require a number of revisions to the RHM, we argue that the central issues to which the model was addressed, the way in which new lexical forms are mapped to meaning and the consequence of language learning history for lexical processing, cannot be accounted for solely within models of word recognition.
Journal Article
On the fractal patterns of language structures
Natural Language Processing (NLP) makes use of Artificial Intelligence algorithms to extract meaningful information from unstructured texts, i.e., content that lacks metadata and cannot easily be indexed or mapped onto standard database fields. It has several applications, from sentiment analysis and text summary to automatic language translation. In this work, we use NLP to figure out similar structural linguistic patterns among several different languages. We apply the word2vec algorithm that creates a vector representation for the words in a multidimensional space that maintains the meaning relationship between the words. From a large corpus we built this vectorial representation in a 100-dimensional space for English, Portuguese, German, Spanish, Russian, French, Chinese, Japanese, Korean, Italian, Arabic, Hebrew, Basque, Dutch, Swedish, Finnish, and Estonian. Then, we calculated the fractal dimensions of the structure that represents each language. The structures are multi-fractals with two different dimensions that we use, in addition to the token-dictionary size rate of the languages, to represent the languages in a three-dimensional space. Finally, analyzing the distance among languages in this space, we conclude that the closeness there is tendentially related to the distance in the Phylogenetic tree that depicts the lines of evolutionary descent of the languages from a common ancestor.
Journal Article
A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records
Background
Electronic Medical Record (EMR) comprises patients’ medical information gathered by medical stuff for providing better health care. Named Entity Recognition (NER) is a sub-field of information extraction aimed at identifying specific entity terms such as disease, test, symptom, genes etc. NER can be a relief for healthcare providers and medical specialists to extract useful information automatically and avoid unnecessary and unrelated information in EMR. However, limited resources of available EMR pose a great challenge for mining entity terms. Therefore, a multitask bi-directional RNN model is proposed here as a potential solution of data augmentation to enhance NER performance with limited data.
Methods
A multitask bi-directional RNN model is proposed for extracting entity terms from Chinese EMR. The proposed model can be divided into a shared layer and a task specific layer. Firstly, vector representation of each word is obtained as a concatenation of word embedding and character embedding. Then Bi-directional RNN is used to extract context information from sentence. After that, all these layers are shared by two different task layers, namely the parts-of-speech tagging task layer and the named entity recognition task layer. These two tasks layers are trained alternatively so that the knowledge learned from named entity recognition task can be enhanced by the knowledge gained from parts-of-speech tagging task.
Results
The performance of our proposed model has been evaluated in terms of micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in all cases. For instance, experimental results conducted on the discharge summaries show that the micro average F-score and the macro average F-score are improved by 2.41% point and 4.16% point, respectively, and the overall accuracy is improved by 5.66% point.
Conclusions
In this paper, a novel multitask bi-directional RNN model is proposed for improving the performance of named entity recognition in EMR. Evaluation results using real datasets demonstrate the effectiveness of the proposed model.
Journal Article
Recognizing two dialects in one written form: A Stroop study
This study aims to examine the influence of dialectal experience on logographic visual word recognition. Two groups of Chinese monolectals and three groups of Chinese bi-dialectals performed Stroop color-naming in Standard Chinese (SC), and two of the bi-dialectal groups also in their regional dialects. The participant groups differed in dialectal experiences. The ink-character relation was manipulated in semantics, segments, and tones separately, as congruent, competing, or different, yielding ten Stroop conditions for comparison. All the groups showed Stroop interference for the conditions of segmental competition, as well as evidence for semantic activation by the characters. Bi-dialectal experience, even receptive, could benefit conflict resolution in the Stroop task. Chinese characters can automatically activate words in both dialects. Comparing naming in Standard Chinese and naming in the bi-dialectals’ regional dialects, still, a regional-dialect disadvantage suggests that the activation is biased with literacy and lexico-specific inter-dialectal relations.
Journal Article
Compositionality, communication, and commitments
In recent years, there has been increasing interest in Rich Meaning Approaches (RMA) that understand the meanings of words as rich conceptual structures, such as Pustejovsky’s generative lexicon. The reason for this is based on compositionality, as rich meanings have been shown to be indispensable for explaining conflict resolution in compositional processes. However, while the benefits of postulating rich meanings to explain conflict resolution are undeniable, the overall contribution of rich meanings to sentence comprehension has not yet been discussed. This paper aims to show that inferentialism counts as a version of RMA and that, once this is recognised, it can provide a robust rationale for the role of rich meanings in sentence comprehension. The rationale is based on the idea that rich meanings are indispensable for pragmatic purposes as they play a role in facilitating communication. As I argue, rich meanings not only assist in composing the semantic (truth-conditional) content of complete sentences, but also provide crucial information for determining the discursive commitments and entitlements established by utterances. Consequently, examining the implications of inferentialism for compositional processes a) offers new insights into their function and outputs and b) presents an alternative to the representationalist perspective on sentence comprehension.
Journal Article
Portraying the life cycle of ideas in social psychology through functional (textual) data analysis: a toolkit for digital history
This paper presents a method for the digital history of a discipline (social psychology in this application) through the analysis of scientific publications. The titles of a comprehensive set of papers published in the Journal of Personality and Social Psychology (1965–2021) were collected, yielding a total of 10,222 items. The corpus thus constructed underwent several stages of preprocessing until the final conversion into a terms x time-points matrix, where terms are stemmed words and multi-words. After normalizing frequencies via a chi square-like transformation, clusters of words portraying similar temporal patterns were identified by functional (textual) data analysis and distance-based curve clustering. Among the best candidates in terms of the number of clusters, the solutions with six, nine and thirteen clusters (from lower to higher resolution) have been chosen and the nesting relationship demonstrated. They reveal—at different levels of granularity—increasing, decreasing, and stable keywords trends, highlighting methods, theories, and application domains that have become more popular in recent years, lost popularity, or have remained in common use. Moreover, this method allows to highlight historical issues (such as crises in the discipline or debates over the use of terms). The results highlight the core topics of social psychology in the past and today, underlying the crucial contribution of this method for the digital history of a discipline.
Journal Article
White Matter Morphometric Changes Uniquely Predict Children's Reading Acquisition
This study examined whether variations in brain development between kindergarten and Grade 3 predicted individual differences in reading ability at Grade 3. Structural MRI measurements indicated that increases in the volume of two left temporo-parietal white matter clusters are unique predictors of reading outcomes above and beyond family history, socioeconomic status, and cognitive and preliteracy measures at baseline. Using diffusion MRI, we identified the left arcuate fasciculus and superior corona radiata as key fibers within the two clusters. Bias-free regression analyses using regions of interest from prior literature revealed that volume changes in temporo-parietal white matter, together with preliteracy measures, predicted 56% of the variance in reading outcomes. Our findings demonstrate the important contribution of developmental differences in areas of left dorsal white matter, often implicated in phonological processing, as a sensitive early biomarker for later reading abilities, and by extension, reading difficulties.
Journal Article
High-resolution climate reconstruction from historical Chinese weather records using optimized natural language processing
Reconstructing high-resolution climate data from historical documents is hindered by subjectivity and a lack of standardization. This study develops and validates a novel framework to overcome these challenges. In this paper, a historical weather classification lexicon is constructed by optimizing natural language processing (NLP) techniques. Leveraging semantic clustering and dynamic expansion, this lexicon effectively captures the linguistic diversity associated with weather events across different regions and intensity levels. Building on this lexicon, we propose a multi-dimensional index system to quantify historical weather grades. This system includes indicators such as weather intensity, agricultural impact, economic impact, social impact, and population casualties. For each indicator, scientific and objective weights are assigned using the entropy method combined with expert judgment. To validate the effectiveness of our approach, we extracted low-temperature weather records from historical documents of Guangdong and Hebei provinces in China. The results show that the overall trend of low-temperature weather in these two provinces is consistent with existing research on climate change during the Qing Dynasty. Moreover, the provincial trend maps reveal not only synchronous change patterns but also significant regional differences. A Random Forest model was employed to validate our index, achieving a classification accuracy of 94.0%, with Area Under the Curve(AUC) scores exceeding 0.98 for low-grade events. This data-driven methodology offers a replicable and scalable tool for converting qualitative historical narratives into high-resolution quantitative climate data, thereby enhancing our understanding of past climate variability and its societal impacts.
Journal Article