Abstract As DNA sequencing outpaces improvements in computer speed, there is a critical need to accelerate tasks like alignment and SNP calling. Crossbow cloud-computing software tool that combines the aligner Bowtie caller SOAPsnp. Executing parallel using Hadoop, analyzes data comprising 38-fold coverage of human genome three hours 320-CPU cluster rented from cloud computing service for about $85. available http://bowtie-bio.sourceforge.net/crossbow/ .

WTF ("Who to Follow") is Twitter's user recommendation service, which responsible for creating millions of connections daily between users based on shared interests, common connections, and other related factors. This paper provides an architectural overview shares lessons we learned in building running the service over past few years. Particularly noteworthy was our design decision process entire Twitter graph memory a single server, significantly reduced complexity allowed us develop...

This half-day tutorial introduces participants to data-intensive text processing with the MapReduce programming model [1], using open-source Hadoop implementation. The focus will be on scalability and tradeoffs associated distributed of large datasets. Content include general discussions about algorithm design, presentation illustrative algorithms, case studies in HLT applications, as well practical advice writing programs running clusters.

Modeling sentence similarity is complicated by the ambiguity and variability of linguistic expression.To cope with these challenges, we propose a model for comparing sentences that uses multiplicity perspectives.We first each using convolutional neural network extracts features at multiple levels granularity types pooling.We then compare our representations several granularities metrics.We apply to three tasks, including Microsoft Research paraphrase identification task two SemEval semantic...

In the natural language processing literature, neural networks are becoming increasingly deeper and complex. The recent poster child of this trend is deep representation model, which includes BERT, ELMo, GPT. These developments have led to conviction that previous-generation, shallower for understanding obsolete. paper, however, we demonstrate rudimentary, lightweight can still be made competitive without architecture changes, external training data, or additional input features. We propose...

Software toolkits play an essential role in information retrieval research. Most open-source developed by academics are designed to facilitate the evaluation of models over standard test collections. Efforts generally directed toward better ranking and less attention is usually given scalability other operational considerations. On hand, Lucene has become de facto platform industry for building search applications (outside a small number companies that deploy custom infrastructure). Compared...

The combination of recent developments in question-answering research and the availability unparalleled resources developed specifically for automatic semantic processing text medical domain provides a unique opportunity to explore complex question answering clinical medicine. This article presents system designed satisfy information needs physicians practicing evidence-based We have series knowledge extractors, which employ knowledge-based statistical techniques, automatically identifying...

In this paper, we provide a characterization of the topological features Twitter follow graph, analyzing properties such as degree distributions, connected components, shortest path lengths, clustering coefficients, and assortativity. For each these properties, compare contrast with available data from other social networks. These analyses set authoritative statistics that community can reference. addition, use to investigate an often-posed question: Is network or information network? The...

ABSTRACT Cloud computing is a platform that resides in large data center and able to dynamically provide servers with the ability address wide range of needs, from scientific research e-commerce. The provision resources as if it were utility such electricity, while potentially revolutionary service, presents many major problems information policy, including issues privacy, security, reliability, access, regulation. This article explores nature potential cloud computing, policy raised,...

This work proposes the use of a pretrained sequence-to-sequence model for document ranking. Our approach is fundamentally different from commonly adopted classification-based formulation based on encoder-only transformer architectures such as BERT. We show how can be trained to generate relevance labels “target tokens”, and underlying logits these target tokens interpreted probabilities Experimental results MS MARCO passage ranking task that our superior strong models. On three other...

Textual similarity measurement is a challenging problem, as it requires understanding the semantics of input sentences.Most previous neural network models use coarse-grained sentence modeling, which has difficulty capturing fine-grained word-level information for semantic comparisons.As an alternative, we propose to explicitly model pairwise word interactions and present novel focus mechanism identify important correspondences better measurement.Our ideas are implemented in architecture that...

Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Jimmy Lin. Proceedings of the 2019 Conference North American Chapter Association for Computational Linguistics (Demonstrations). 2019.

We present simple BERT-based models for relation extraction and semantic role labeling. In recent years, state-of-the-art performance has been achieved using neural by incorporating lexical syntactic features such as part-of-speech tags dependency trees. this paper, extensive experiments on datasets these two tasks show that without any external features, a model can achieve performance. To our knowledge, we are the first to successfully apply BERT in manner. Our provide strong baselines...

Large-scale pre-trained language models such as BERT have brought significant improvements to NLP applications. However, they are also notorious for being slow in inference, which makes them difficult deploy real-time We propose a simple but effective method, DeeBERT, accelerate inference. Our approach allows samples exit earlier without passing through the entire model. Experiments show that DeeBERT is able save up ~40% inference time with minimal degradation model quality. Further analyses...

Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. It aims to provide effective, reproducible, easy-to-use first-stage in multi-stage ranking architecture. Our self-contained as standard package comes queries, relevance judgments, pre-built indexes, evaluation scripts many commonly used IR test collections. We aim support, out of the box, entire lifecycle efforts aimed at improving modern neural approaches. In particular,...

One technique to improve the retrieval effectiveness of a search engine is expand documents with terms that are related or representative documents' content.From perspective question answering system, this might comprise questions document can potentially answer. Following observation, we propose simple method predicts which queries will be issued for given and then expands it those predictions vanilla sequence-to-sequence model, trained using datasets consisting pairs query relevant...

The success of data-driven solutions to difficult problems, along with the dropping costs storing and processing massive amounts data, has led growing interest in large-scale machine learning. This paper presents a case study Twitter's integration learning tools into its existing Hadoop-based, Pig-centric analytics platform. We begin an overview this platform, which handles "traditional" data warehousing business intelligence tasks for organization. core work lies recent Pig extensions...

This work tackles the perennial problem of reproducible baselines in information retrieval research, focusing on bag-of-words ranking models. Although academic researchers have a long history building and sharing systems, they are primarily designed to facilitate publication research papers. As such, these systems often incomplete, inflexible, poorly documented, difficult use, slow, particularly context modern web-scale collections. Furthermore, growing complexity software ecosystems...

We present, to our knowledge, the first application of BERT document classification. A few characteristics task might lead one think that is not most appropriate model: syntactic structures matter less for content categories, documents can often be longer than typical input, and have multiple labels. Nevertheless, we show a straightforward classification model using able achieve state art across four popular datasets. To address computational expense associated with inference, distill...

We explore the application of deep residual learning and dilated convolutions to keyword spotting task, using recently-released Google Speech Commands Dataset as our benchmark. Our best network (ResNet) implementation significantly outperforms Google's previous convolutional neural networks in terms accuracy. By varying model depth width, we can achieve compact models that also outperform small-footprint variants. To knowledge, are first examine these approaches for spotting, results...

A vital step towards the widespread adoption of neural retrieval models is their resource efficiency throughout training, indexing and query workflows. The IR community made great advancements in training effective dual-encoder dense (DR) recently. text model uses a single vector representation per passage to score match, which enables low-latency first-stage with nearest neighbor search. Increasingly common, approaches require enormous compute power, as they either conduct negative sampling...

The advent of deep neural networks pre-trained via language modeling tasks has spurred a number successful applications in natural processing. This work explores one such popular model, BERT, the context document ranking. We propose two variants, called monoBERT and duoBERT, that formulate ranking problem as pointwise pairwise classification, respectively. These models are arranged multi-stage architecture to form an end-to-end search system. One major advantage this design is ability trade...

Long Short-Term Memory Networks (LSTMs) have been applied to daily discharge prediction with remarkable success. Many practical scenarios, however, require predictions at more granular timescales. For instance, accurate of short but extreme flood peaks can make a life-saving difference, yet such may escape the coarse temporal resolution predictions. Naively training an LSTM on hourly data, entails very long input sequences that learning hard and computationally expensive. In this study, we...