scholarly journals CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank

2007 ◽  
Vol 33 (3) ◽  
pp. 355-396 ◽  
Author(s):  
Julia Hockenmaier ◽  
Mark Steedman
Keyword(s):  
Long Range ◽  
State Of The Art ◽  
Categorial Grammar ◽  
Linguistic Data ◽  
Combinatory Categorial Grammar ◽  
Extensive Analysis ◽  
Dependency Structures

This article presents an algorithm for translating the Penn Treebank into a corpus of Combinatory Categorial Grammar (CCG) derivations augmented with local and long-range word-word dependencies. The resulting corpus, CCGbank, includes 99.4% of the sentences in the Penn Treebank. It is available from the Linguistic Data Consortium, and has been used to train wide-coverage statistical parsers that obtain state-of-the-art rates of dependency recovery. In order to obtain linguistically adequate CCG analyses, and to eliminate noise and inconsistencies in the original annotation, an extensive analysis of the constructions and annotations in the Penn Treebank was called for, and a substantial number of changes to the Treebank were necessary. We discuss the implications of our findings for the extraction of other linguistically expressive grammars from the Treebank, and for the design of future treebanks.

scholarly journals Discovering Language Properties through Corpus-Based Dictionary Data Analysis

2017 ◽  
Vol 6 (2) ◽  
pp. 61-70
Author(s):  
Paul Lyddon ◽  
Keyword(s):  
Data Analysis ◽  
Language Use ◽  
State Of The Art ◽  
Structural Characteristics ◽  
Language Teacher ◽  
Linguistic Data ◽  
Large Samples ◽  
Widespread Availability ◽  
Real Language

To reveal underlying patterns in real language use, linguists have increasingly come to rely on corpus analyses, involving the evaluation of statistical frequencies in generally sizable bodies of natural linguistic data. However, accessing and analyzing large samples of raw language is neither always practical nor even truly necessary, especially in cases pertaining to structural characteristics. In fact, the requisite data can oftentimes be gleaned from a state-of-the-art (i.e., corpus-based) dictionary. Moreover, given the widespread availability of easily searchable electronic dictionaries nowadays, almost any language teacher or learner can use one to answer a number of these types of queries. This paper illustrates this claim with a step-by-step analysis of corpus-based dictionary data for the purpose of formulating the sound-symbol relations in English words with vowels preceding –gh.

scholarly journals CSR in Management Sciences: Is It “a Road to Nowhere”?

Economies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 198
Author(s):  
Andrzej Janowski
Keyword(s):  
Environmental Management ◽  
Corporate Social Responsibility ◽  
Literature Review ◽  
Social Responsibility ◽  
Impact Factor ◽  
State Of The Art ◽  
Future Research ◽  
Management Journal ◽  
Extensive Analysis ◽  
Corporate Social

Over the years, the idea of corporate social responsibility has attracted the interest both of practitioners and researchers. While a discourse has occurred in the context of the meaning of Corporate Social Responsibility (CSR), there is no extensive analysis of the latest contributions. To address this gap, three objectives have been formulated for the purpose of this paper: to analyse development trends in CSR definitions, to assess the applicability of scientific efforts in the above-mentioned area and to demonstrate future research opportunities and threats based on the state-of-the-art in CSR. An organized literature review and bibliometric methods have been proposed based on 55 articles published in the “Corporate Social Responsibility and Environmental Management Journal”, the top ranked periodical with the highest Impact Factor, from its beginnings to January 2021. This research provides a clear overview of the recent CSR trends that are present in the literature, by pinpointing those works that refer to significant characteristics and indicate future trajectories of CSR in a managerial practical context, particularly important for the effectiveness of future CSR-oriented organizational activities. This paper summarises the existing CSR-related knowledge and constitutes the first attempt to investigate the process of the creation and development of the corporate social responsibility concept from its origins to its contemporary multidimensionality.

Centralizing Integrated Long Range Planning and Budgeting

2011 ◽  
pp. 55-75
Author(s):  
Taylor Valore
Keyword(s):  
Long Range ◽  
State Of The Art ◽  
Technical Aspects ◽  
American University ◽  
Independent Events ◽  
Long Range Planning ◽  
American University In Cairo ◽  
Annual Planning

Upon relocation to a new, state-of-the-art, 260-acre campus outside of Cairo, Egypt, the American University in Cairo (AUC) sought to revamp its annual planning and budgeting processes to address several deficiencies. Primarily, long-range planning and annual budgeting were two independent events with little synchronization. This case study will detail the process and technical aspects of AUC’s transition to a centralized and synchronized planning and budgeting cycle focused on determining appropriate workflows and leveraging database technologies to track planning initiatives throughout an approvals process. Readers will be able to weigh the drawbacks of centralization against the benefits of standardized budget review and planning.

scholarly journals A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation

2020 ◽  
Vol 8 ◽  
pp. 93-108 ◽  
Author(s):  
Jian Guan ◽  
Fei Huang ◽  
Zhihao Zhao ◽  
Xiaoyan Zhu ◽  
Minlie Huang
Keyword(s):  
Long Range ◽  
Temporal Order ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Fine Tuning ◽  
Causal Relationships ◽  
Commonsense Knowledge ◽  
Language Generation ◽  
Story Generation ◽  
Global Coherence

Story generation, namely, generating a reasonable story from a leading context, is an important but challenging task. In spite of the success in modeling fluency and local coherence, existing neural language generation models (e.g., GPT-2) still suffer from repetition, logic conflicts, and lack of long-range coherence in generated stories. We conjecture that this is because of the difficulty of associating relevant commonsense knowledge, understanding the causal relationships, and planning entities and events with proper temporal order. In this paper, we devise a knowledge-enhanced pretraining model for commonsense story generation. We propose to utilize commonsense knowledge from external knowledge bases to generate reasonable stories. To further capture the causal and temporal dependencies between the sentences in a reasonable story, we use multi-task learning, which combines a discriminative objective to distinguish true and fake stories during fine-tuning. Automatic and manual evaluation shows that our model can generate more reasonable stories than state-of-the-art baselines, particularly in terms of logic and global coherence.

scholarly journals Self-Attention ConvLSTM for Spatiotemporal Prediction

2020 ◽  
Vol 34 (07) ◽  
pp. 11531-11538
Author(s):  
Zhihui Lin ◽  
Maomao Li ◽  
Zhuobin Zheng ◽  
Yangyang Cheng ◽  
Chun Yuan
Keyword(s):  
Long Range ◽  
State Of The Art ◽  
The Self ◽  
Time Step ◽  
Traffic Flow Prediction ◽  
Spatial Features ◽  
Gating Mechanism ◽  
Spatial Dependencies ◽  
Previous State ◽  
Global And Local

Spatiotemporal prediction is challenging due to the complex dynamic motion and appearance changes. Existing work concentrates on embedding additional cells into the standard ConvLSTM to memorize spatial appearances during the prediction. These models always rely on the convolution layers to capture the spatial dependence, which are local and inefficient. However, long-range spatial dependencies are significant for spatial applications. To extract spatial features with both global and local dependencies, we introduce the self-attention mechanism into ConvLSTM. Specifically, a novel self-attention memory (SAM) is proposed to memorize features with long-range dependencies in terms of spatial and temporal domains. Based on the self-attention, SAM can produce features by aggregating features across all positions of both the input itself and memory features with pair-wise similarity scores. Moreover, the additional memory is updated by a gating mechanism on aggregated features and an established highway with the memory of the previous time step. Therefore, through SAM, we can extract features with long-range spatiotemporal dependencies. Furthermore, we embed the SAM into a standard ConvLSTM to construct a self-attention ConvLSTM (SA-ConvLSTM) for the spatiotemporal prediction. In experiments, we apply the SA-ConvLSTM to perform frame prediction on the MovingMNIST and KTH datasets and traffic flow prediction on the TexiBJ dataset. Our SA-ConvLSTM achieves state-of-the-art results on both datasets with fewer parameters and higher time efficiency than previous state-of-the-art method.

scholarly journals A STUDY INVESTIGATING CHALLENGES IN THE INTERFACE BETWEEN PRODUCT DEVELOPMENT AND MANUFACTURING IN THE DEVELOPMENT OF SOFTWARE-INTENSIVE AUTOMOTIVE SYSTEMS

2012 ◽  
Vol 22 (07) ◽  
pp. 965-1004 ◽  
Author(s):  
J. PERNSTÅL ◽  
A. MAGAZINIUS ◽  
T. GORSCHEK
Keyword(s):  
Product Development ◽  
Automotive Industry ◽  
State Of The Art ◽  
Future Research ◽  
Manufacturing Processes ◽  
Process Assessment ◽  
Automotive Systems ◽  
Extensive Analysis ◽  
Key Issues ◽  
Software Intensive

The automotive industry is facing a tremendous growth in the engineering of software-intensive systems, giving rise to various challenges. To prevent problems related to the fit of new software technologies in vehicles and the manufacturing processes, a well functioning interaction between the functions for product development and manufacturing is crucial. This is complicated by the fact that the changeable nature of software development causes unprecedented needs for collaboration and coordination between these two functions. This paper reports on a process assessment that focuses on the interface between the functions for product development and manufacturing in the development and design of software-intensive automotive systems. The main purpose of the study was to identify the key issues for improvement in the area assessed. The assessment was performed at two Swedish automotive companies where data were collected from documents and in interviews with practitioners. Nine key improvement issues were established ranging from challenges in requirements engineering to the need for knowledge transfer between manufacturing and product development. In addition, to increase the understandability of the results and map possible avenues for solution and future research, the paper provides an extensive analysis of each improvement issue in relation to the state-of-the-art.

Sign in / Sign up

Export Citation Format

Share Document