SOFTWARE ENTREPRENEURSHIP: KNOWLEDGE NETWORKS AND PERFORMANCE OF SOFTWARE VENTURES IN CHINA AND RUSSIA

This study examines the impact of entrepreneurs’ network structure and knowledge homogeneity/heterogeneity of their network members on product development, and revenue growth of software ventures in China and Russia. The empirical data are composed of structured interviews with 159 software entrepreneurs in Beijing and Moscow. The study found that structural holes and knowledge heterogeneity affect positively product diversity in interactive ways. The study also found that knowledge homogeneity accelerates product development. Product development speed enhances revenue growth in the long term. However, the combination of speed with dense and homogeneous networks harms revenue growth over time. The effects of structural holes and knowledge heterogeneity on product diversity and revenue growth over time are more salient in Russia due to the unique institutional, social, and cultural conditions present in the country.http://deepblue.lib.umich.edu/bitstream/2027.42/40137/3/wp751.pd

SOFTWARE ENTREPRENEURSHIP: KNOWLEDGE NETWORKS AND PERFORMANCE OF SOFTWARE VENTURES IN CHINA AND RUSSIA

This study examines the impact of entrepreneurs’ network structure and knowledge homogeneity/heterogeneity of their network members on product development, and revenue growth of software ventures in China and Russia. The empirical data are composed of structured interviews with 159 software entrepreneurs in Beijing and Moscow. The study found that structural holes and knowledge heterogeneity affect positively product diversity in interactive ways. The study also found that knowledge homogeneity accelerates product development. Product development speed enhances revenue growth in the long term. However, the combination of speed with dense and homogeneous networks harms revenue growth over time. The effects of structural holes and knowledge heterogeneity on product diversity and revenue growth over time are more salient in Russia due to the unique institutional, social, and cultural conditions present in the country.networks, knowledge, entrepreneurs, software, China, Russia.

Neural Cross-Lingual Entity Linking

A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which involves linking mentions written in non-English documents to entries in the English Wikipedia: to compare textual clues across languages we need to compute similarity between textual fragments across languages. In this paper, we propose a neural EL model that trains fine-grained similarities and dissimilarities between the query and candidate document from multiple perspectives, combined with convolution and tensor networks. Further, we show that this English-trained system can be applied, in zero-shot learning, to other languages by making surprisingly effective use of multi-lingual embeddings. The proposed system has strong empirical evidence yielding state-of-the-art results in English as well as cross-lingual: Spanish and Chinese TAC 2015 datasets.Comment: Association for the Advancement of Artificial Intelligence (AAAI), 201

Notes from \u3ci\u3eThe Future of California’s Garment Industry\u3c/i\u3e

The document includes the notes from various workshops, brainstorming sessions, discussions, and strategy sessions that took place at the conference focusing on The Future of California’s Garment Industry

Deep Short Text Classification with Knowledge Powered Attention

Short text classification is one of important tasks in Natural Language Processing (NLP). Unlike paragraphs or documents, short texts are more ambiguous since they have not enough contextual information, which poses a great challenge for classification. In this paper, we retrieve knowledge from external knowledge source to enhance the semantic representation of short texts. We take conceptual information as a kind of knowledge and incorporate it into deep neural networks. For the purpose of measuring the importance of knowledge, we introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered Attention (STCKA). We utilize Concept towards Short Text (C- ST) attention and Concept towards Concept Set (C-CS) attention to acquire the weight of concepts from two aspects. And we classify a short text with the help of conceptual information. Unlike traditional approaches, our model acts like a human being who has intrinsic ability to make decisions based on observation (i.e., training data for machines) and pays more attention to important knowledge. We also conduct extensive experiments on four public datasets for different tasks. The experimental results and case studies show that our model outperforms the state-of-the-art methods, justifying the effectiveness of knowledge powered attention

Digital libraries and minority languages

Digital libraries have a pivotal role to play in the preservation and maintenance of international cultures in general and minority languages in particular. This paper outlines a software tool for building digital libraries that is well adapted for creating and distributing local information collections in minority languages, and describes some contexts in which it is used. The system can make multilingual documents available in structured collections and allows them to be accessed via multilingual interfaces. It is issued under a free open-source licence, which encourages participatory design of the software, and an end-user interface allows community-based localization of the various language interfaces - of which there are many