Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Grammars for generating isiXhosa and isiZulu weather bulletin verbs

The Met Office has investigated the use of natural language generation (NLG) technologies to streamline the production of weather forecasts. Their approach would be of great benefit in South Africa because there is no fast and large scale producer, automated or otherwise, of textual weather summaries for Nguni languages. This is because of, among other things, the complexity of Nguni languages. The structure of these languages is very different from Indo-European languages, and therefore we cannot reuse existing technologies that were developed for the latter group. Traditional NLG techniques such as templates are not compatible with 'Bantu' languages, and existing works that document scaled-down 'Bantu' language grammars are also not sufficient to generate weather text. In pursuance of generating weather text in isiXhosa and isiZulu - we restricted our text to only verbs in order to ensure a manageable scope. In particular, we have developed a corpus of weather sentences in order to determine verb features. We then created context free verbal grammar rules using an incremental approach. The quality of these rules was evaluated using two linguists. We then investigated the grammatical similarity of isiZulu verbs with their isiXhosa counterparts, and the extent to which a singular merged set of grammar rules can be used to produce correct verbs for both languages. The similarity analysis of the two languages was done through the developed rules' parse trees, and by applying binary similarity measures on the sets of verbs generated by the rules. The parse trees show that the differences between the verb's components are minor, and the similarity measures indicate that the verb sets are at most 59.5% similar (Driver-Kroeber metric). We also examined the importance of the phonological conditioning process by developing functions that calculate the ratio of verbs that will require conditioning out of the total strings that can be generated. We have found that the phonological conditioning process affects at least 45% of strings for isiXhosa, and at least 67% of strings for isiZulu depending on the type of verb root that is used. Overall, this work shows that the differences between isiXhosa and isiZulu verbs are minor, however, the exploitation of these similarities for the goal of creating a unified rule set for both languages cannot be achieved without significant maintainability compromises because there are dependencies that exist in one language and not the other between the verb's 'modules'. Furthermore, the phonological conditioning process should be implemented in order to improve generated text due to the high ratio of verbs it affects

Case reuse in textual case-based reasoning.

Text reuse involves reasoning with textual solutions of previous problems to solve new similar problems. It is an integral part of textual case-based reasoning (TCBR), which applies the CBR problem-solving methodology to situations where experiences are predominantly captured in text form. Here, we explore two key research questions in the context of textual reuse: firstly what parts of a solution are reusable given a problem and secondly how might these relevant parts be reused to generate a textual solution. Reasoning with text is naturally challenging and this is particularly so with text reuse. However significant inroads towards addressing this challenge was made possible with knowledge of problem-solution alignment. This knowledge allows us to identify specific parts of a textual solution that are linked to particular problem attributes or attribute values. Accordingly, a text reuse strategy based on implicit alignment is presented to determine textual solution constructs (words or phrases) that needs adapted. This addresses the question of what to reuse in solution texts and thereby forms the first contribution of this thesis. A generic architecture, the Case Retrieval Reuse Net (CR2N), is used to formalise the reuse strategy. Functionally, this architecture annotates textual constructs in a solution as reusable with adaptation or without adaptation. Key to this annotation is the discovery of reuse evidence mined from neighbourhood characteristics. Experimental results show significant improvements over a retrieve-only system and a baseline reuse technique. We also extended CR2N so that retrieval of similar cases is informed by solutions that are easiest to adapt. This is done by retrieving the top k cases based on their problem similarity and then determining the reusability of their solutions with respect to the target problem. Results from experiments show that reuse-guided retrieval outperforms retrieval without this guidance. Although CR2N exploits implicit alignment to aid text reuse, performance can be greatly improved if there is explicit alignment. Our second contribution is a method to form explicit alignment of structured problem attributes and values to sentences in a textual solution. Thereafter, compositional and transformational approaches to text reuse are introduced to address the question of how to reuse textual solutions. The main idea in the compositional approach is to generate a textual solution by using prototypical sentences across similar authors. While the transformation approach adapts the retrieved solution text by replacing sentences aligned to mismatched problem attributes using sentences from the neighbourhood. Experiments confirm the usefulness of these approaches through strong similarity between generated text and human references. The third and final contribution of this research is the use of Machine Translation (MT) evaluation metrics for TCBR. These metrics have been shown to correlate highly with human expert evaluation. In MT research, multiple human references are typically used as opposed to a single reference or solution per test case. An introspective approach to create multiple references for evaluation is presented. This is particularly useful for CBR domains where single reference cases (or cases with a single solution per problem) typically form the casebase. For such domains we show how multiple references can be generated by exploiting the CBR similarity assumption. Results indicate that TCBR systems evaluated with these MT metrics are closer to human judgements

Hopkins County - Centennial Edition

Centennial Edition of Dawson Springs Progress about the history of Hopkins County Kentucky published on July 25 1974

High school law course and how to vitalize it

This item was digitized by the Internet Archive

Poetics of Artificial Intelligence in Art Practice: (Mis)apprehended Bodies Remixed as Language

With a focus on the last five years, art employing artificial intelligence (AI) has been defined by a spectrum of activity, from the deep learning explorations of neural network researchers to artists critiquing the broader social implications of AI technology. There is an emergence of and increasing access to new tools and techniques for repurposing and manipulating material in unprecedented ways in art. At the same time, there is a dearth of language outside the scientific domain with which to discuss it. A combination of contextual review, comparison of artistic approaches, and practical projects explores speculation that the conceptual repertoire for remix studies can open up to art enabled by AI and machine learning (ML). This research contributes a practical, conceptual and combinatorial approach for artists who do not necessarily have a grounding in engineering or computer science. A bricolage methodology—described by Annette Markham as combining serendipity, proximity and contingency—reveals the poetics of AI-enabled art in the form of an assemblage of techniques that understands poetics as active making (poiesis) as well as an approach to manipulating language. The poetic capacity of AI/ML is understood as an emergent form of remix technique, with the ML at its core functioning like a remix engine. This practice based research presents several projects founded on an interrelation of body, text Bruce Gilchrist. Poetics of Artificial Intelligence in Art Practice: (Mis)apprehended Bodies Remixed as Language. 3 and predictive technology enabled by a human-action-recognition algorithm combined with a natural language generator. A significant number of artistic works have been made around object and facial recognition, while very little (if any) artist activity has focused on human-action-recognition. For this reason, I concentrated my research there

Desarrollo y uso de un software de generación de noticias deportivas con sentimiento en la mejora de procesos en el ámbito periodístico

La generación de texto dejó de ser del dominio exclusivo de los humanos hace años. Hoy en día, existen sistemas de generación de lenguaje natural que escriben resúmenes de documentos; código para construir aplicaciones y textos de todo tipo, incluyendo noticias. Además, cada vez un número mayor de organizaciones son conscientes de los avances que se producen en este campo y adoptan tecnología relacionada para reducir el tiempo que sus trabajadores emplean, recortar sus gastos, etc. Aquí se presenta un trabajo multidisciplinar, en el entorno de una Cátedra en la que colaboran la Universidad Carlos III de Madrid y la Corporación de Radio y Televisión Española, S. A. En él se exponen el desarrollo de una herramienta de generación de noticias deportivas capaz de redactar el texto en función de la afición a la que vaya dirigida la noticia, y la guía para la integración de la mencionada herramienta en la corporación siguiendo el Ciclo de Mejora de los Procesos de negocio.Text generation ceased to be exclusive human domain years ago. Currently, there exists Natural Language Generation (NLG) system that synthesize abstracts, generate code for building applications, and write other texts, including news. Additionally, more and more organizations are becoming aware of the progress that is being made in the field and are including NLG technology into their structures to reduce expenses and help employees save time. This document displays a multidisciplinary thesis within the fellowship involving Universidad Carlos III de Madrid and Corporación de Radio y Televisión Española S. A. The presented work depicts the development of an automatic sports news generator that can tailor the text depending on the sports fans the text is intended to. Furthermore, a guide is provided on how to incorporate such tool in RTVE following CMP, a continuous business process improvement methodology.Doble Grado en Ingeniería Informática y Administración de Empresa

Bowdoin Orient v.95, no.1-34 (1965-1966)

https://digitalcommons.bowdoin.edu/bowdoinorient-1960s/1006/thumbnail.jp

Winona Daily News

https://openriver.winona.edu/winonadailynews/2146/thumbnail.jp