University of Massachusetts Amherst

About: University of Massachusetts Amherst is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 37274 authors who have published 83965 publications receiving 3834996 citations. The organization is also known as: UMass Amherst & Massachusetts State College.

Cellulose Degradation in Anaerobic Environments

Abstract: In anaerobic environments rich in decaying plant material, the decomposition of cellulose is brought about by complex communities of interacting microorganisms. Because the substrate, cellulose, is insoluble, bacterial and fungal degradation occurs exocellularly, either in association with the outer cell envelope layer or extracellularly. Products of cellulose hydrolysis are available as carbon and energy sources for other microbes that inhabit environments in which cellulose is biodegraded, and this availability forms the basis of many microbial interactions that occur in these environments. This review discusses interactions among members of cellulose-decomposing microbial communities in various environments. It considers cellulose decomposing communities in soils, sediments, and aquatic environments, as well as those that degrade cellulose in association with animals. These microbial communities contribute significantly to the cycling of carbon on a global scale.

A Deep Relevance Matching Model for Ad-hoc Retrieval

Abstract: In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.