Training Lexicon Research Articles

Generative adversarial networks for handwriting image generation: a review

AbstractHandwriting synthesis, the task of automatically generating realistic images of handwritten text, has gained increasing attention in recent years, both as a challenge in itself, as well as a task that supports handwriting recognition research. The latter task is to synthesize large image datasets that can then be used to train deep learning models to recognize handwritten text without the need for human-provided annotations. While early attempts at developing handwriting generators yielded limited results [1], more recent works involving generative models of deep neural network architectures have been shown able to produce realistic imitations of human handwriting [2–19]. In this review, we focus on one of the most prevalent and successful architectures in the field of handwriting synthesis, the generative adversarial network (GAN). We describe the capabilities, architecture specifics, and performance of the GAN-based models that have been introduced to the literature since 2019 [2–14]. These models can generate random handwriting styles, imitate reference styles, and produce realistic images of arbitrary text that was not in the training lexicon. The generated images have been shown to contribute to improving handwriting recognition results when augmenting the training samples of recognition models with synthetic images. The synthetic images were often hard to expose as non-real, even by human examiners, but also could be implausible or style-limited. The review includes a discussion of the characteristics of the GAN architecture in comparison with other paradigms in the image-generation domain and highlights the remaining challenges for handwriting synthesis.

Modeling public mood and emotion: Stock market trend prediction with anticipatory computing approach

The science and technology is more and more developed. Digital media such as articles, commentary, videos, animations and others on the Internet is becoming more and more important. English semantic analysis has many basic technologies, many applications are also gradually budding in this basic technology. On the other hand, there is no uniform or complete reorganization of the basic technologies in Chinese semantic analysis. Chinese semantic analysis is difficult than English semantic analysis because it is difficult to judge the true meaning of Chinese words and sentences. This study collects articles about common news sites in Taiwan and related to individual stocks. After the data is preprocessed and Skip-gram, each word is converted to word features using Word2Vec. The Lexicon stores the most relevant words around the keyword. In the prediction stage, this study calculates the impact of new articles on the stock price according to the full training lexicon. Finally, this study uses the deep learning approach - LSTM (Long Short-Term Memory) to evaluate the final results. The aim of this study is to adopt anticipatory computing to explore the public mood and emotion from news articles. Then this study can predict the future stock market trend and can be the reference model to the related industries.

On democratic glossaries: ‘soft power’ and hard markets in post-revolutionary Tunisia

ABSTRACTInside Tunisian civic training programmes funded by foreign donors in the post-revolutionary period, democratic training collapses into neoliberal frames of being and doing. This paper traces the ‘soft power’ that imbues the glossaries of democratisation with a specifically economistic logic. It argues that this economistic logic influences the shaping of an emerging civic public in Tunisia along international objectives despite the translation of the civic training lexicon into standard Arabic or the Tunisian dialect and the multilingual code-switching of the training sessions. Engendering this young civic public as a counter-public to earlier articulations of civic awareness and practice – that are now construed as unruly, violent, and unproductive – the internationally approved glossaries of democratic deliberation and civic action recalibrate democracy as predominantly the space for free competition, production, and consumption. While not unique to Tunisia, the Tunisian case urges us to think of the paradoxes of democratic transition in places where the state simultaneously strives to build institutions of liberal representative democracy and simultaneously alters the meaning of liberal representative democracy along neoliberal lines.

A connectionist account of Spanish determiner production

Evidence from experimental studies of Spanish children's production of determiners reveals that they pay more attention to phonological cues present in nouns than to natural semantics when assigning gender to determiners (Pérez-Pereira, 1991). This experimental work also demonstrated that Spanish children are more likely to produce the correct determiner when given a noun with phonological cues which suggest it is masculine, and more likely to assign masculine gender to nouns with ambiguous cues. In this paper, we investigate the phonological cues available to children and seek to explore the possibility that differential frequency in the linguistic input explains the priority given to masculine forms when children are faced with ambiguous novel items. A connectionist model of determiner production was incrementally trained on a lexicon of determiner-noun phrases taken from parental speech in a longitudinal study of a child between the ages of 1;7 and 2;11 (López Ornat, Fernandez, Gallo & Mariscal, 1994) preserving the type and token frequency information. An analysis of the database of parental productions revealed that while regular feminine nouns were slightly more frequent than regular masculine nouns, irregular masculine nouns outnumbered irregular feminine nouns by roughly 2 to 1. On the basis of this, we made the prediction that as the training lexicon builds up, the network will perform better overall on masculine determiners than would be predicted from their forms alone and will tend to assign masculine gender to ambiguous novel nouns in a test set. The findings indicate that, at least in the case of Spanish gender agreement for determiners and nouns, a general associative learning mechanism can account for important characteristics of the acquisition process seen in children.

Phonemic transcription by analogy in text-to-speech synthesis: Novel word pronunciation and lexicon compression

The synthesis of speech from unrestricted text needs a phonemic transcription including syllabification and lexical stress for each word and symbol. Speech synthesizers currently use large lexicons to provide such transcriptions, but noteveryword has a lexical entry and a backup is required to produce transcriptions for novel words. In addition, synthesizers do not have an infinite amount of memory at their disposal, so it is not always possible continually to append supplementary lexemes for specialized applications in the hope of reducing the probability of encountering a novel word. Transcriptions for novel words are produced by implicit analogy with an existing lexicon. A data-driven technique of extracting context-dependent grapheme-to-phoneme rules with dynamically minimized context lengths from a training lexicon is proposed. Syllable boundary and lexical stress information is included in the transcriptions. The proposed system satisfies certain pragmatic constraints: it can produce transcriptions with sufficient rapidity to maintain real-time processing in a text-to-speech system; the rules take up a small amount of storage size (370 KBytes); and a pronunciation can be generated for any novel word. The quality of the transcription process enables 77·06% of lexemes formerly present in the training lexicon to be excluded, thus reducing the lexicon's memory requirements by 74·18% (of 3·57 MBytes).