An official website of the United States government
As the number, size and complexity of building construction projects increase, code compliance checking becomes more challenging because of the time-consuming, costly, and error-prone nature of a manual checking process. A fully automated code compliance checking would be desirable in facilitating a more efficient, cost effective, and human error-proof code checking. Such automation requires automated information extraction from building designs and building codes, and automated information transformation to a format that allows automated reasoning. Natural Language Processing (NLP) is an important technology to support such automated processing of building codes, because building codes are represented in natural language texts. Part-of-speech (POS) tagging, as an important basis of NLP tasks, must have a high performance to ensure the quality of the automated processing of building codes in such a compliance checking system. However, no systematic testing of existing POS taggers on domain specific building codes data have been performed. To address this gap, the authors analyzed the performance of seven state-of-the-at POS taggers on tagging building codes and compared their results to a manually-labeled gold standard. The authors aim to: (1) find the best performing tagger in terms of accuracy, and (2) identify common sources of errors. In providing the POS tags, the authors used the Penn Treebank tagset, which is a widely used tagset with a proper balance between conciseness and information richness. An average accuracy of 88.80% was found on the testing data. The Standford coreNLP tagger outperformed the other taggers in the experiment. Common sources of errors were identified to be: (1) word ambiguity, (2) rare words, and (3) unique meaning of common English words in the construction context. The found result of machine taggers on building codes calls for performance improvement, such as error-fixing transformational rules and machine taggers that are trained on building codes.
more »
« less
A Scalable Solution for Rule-Based Part-of-Speech Tagging on Novel Hardware Accelerators
Part-of-speech (POS) tagging is the foundation of many natural language processing applications. Rule-based POS tagging is a wellknown solution, which assigns tags to the words using a set of predefined rules. Many researchers favor statistical-based approaches over rule-based methods for better empirical accuracy. However, until now, the computational cost of rule-based POS tagging has made it difficult to study whether more complex rules or larger rulesets could lead to accuracy competitive with statistical approaches. In this paper, we leverage two hardware accelerators, the Automata Processor (AP) and Field Programmable Gate Arrays (FPGA), to accelerate rule-based POS tagging by converting rules to regular expressions and exploiting the highly-parallel regular-expressionmatching ability of these accelerators. We study the relationship between rule set size and accuracy, and observe that adding more rules only poses minimal overhead on the AP and FPGA. This allows a substantial increase in the number and complexity of rules, leading to accuracy improvement. Our experiments on Treebank and Brown corpora achieve up to 2,600X and 1,914X speedups on the AP and on the FPGA respectively over rule-based methods on the CPU in the rule-matching stage, up to 58× speedup over the Perceptron POS tagger on the CPU in total testing time, and up to 253× speedup over the LSTM tagger on the GPU in total testing time, while showing a competitive accuracy compared to neural-network and statistical solutions.
more »
« less
- PAR ID:
- 10066046
- Date Published:
- Journal Name:
- KDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Page Range / eLocation ID:
- 665 to 674
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The processing demands of current and emerging applications, such as image/video processing, are increasing due to the deluge of data, generated by mobile and edge devices. This raises challenges for a vast range of computing systems, starting from smart-phones and reaching cloud and data centers. Heterogeneous computing demonstrates its ability as an efficient computing model due to its capability to adapt to various workload requirements. Field programmable gate arrays (FPGAs) provide power and performance benefits and have been used in many application domains from embedded systems to the cloud. In this paper, we used a closely coupled CPU-FPGA heterogeneous system to accelerate a sliding window based image processing algorithm, Canny edge detector. We accelerated Canny using two different implementations: Code partitioned and data partitioned. In the data partitioned implementation, we proposed a weighted round-robin based algorithm that partitions input images and distributes the load between the CPU and the FPGA based on latency. The paper also compares the performance of the proposed accelerators with separate CPU and FPGA implementations. Using our hybrid CPU-FPGA based algorithm, we achieved a speedup of up to 4.8× over a CPU-only and up to 2.1× over a FPGA-only implementations. Moreover, the estimated total energy consumption of our algorithm is more efficient than a CPU-only implementation. Our results show a significant reduction in energy-delay product (EDP) compared to the CPU-only implementation, and comparable EDP results to the FPGA-only implementation.more » « less
-
Unsupervised cross-lingual projection for part-of-speech (POS) tagging relies on the use of parallel data to project POS tags from a source language for which a POS tagger is available onto a target language across word-level alignments. The projected tags then form the basis for learning a POS model for the target language. However, languages with rich morphology often yield sparse word alignments because words corresponding to the same citation form do not align well. We hypothesize that for morphologically complex languages, it is more efficient to use the stem rather than the word as the core unit of abstraction. Our contributions are: 1) we propose an unsupervised stem-based cross-lingual approach for POS tagging for low-resource languages of rich morphology; 2) we further investigate morpheme-level alignment and projection; and 3) we examine whether the use of linguistic priors for morphological segmentation improves POS tagging. We conduct experiments using six source languages and eight morphologically complex target languages of diverse typologies. Our results show that the stem-based approach improves the POS models for all the target languages, with an average relative error reduction of 10.3% in accuracy per target language, and outperforms the word-based approach that operates on three-times more data for about two thirds of the language pairs we consider. Moreover, we show that morpheme-level alignment and projection and the use of linguistic priors for morphological segmentation further improve POS tagging.more » « less
-
Field programmable gate arrays (FPGAs) provide both performance and power benefits to heterogeneous systems. In this work, we used a closely-coupled CPU-FPGA heterogeneous system to accelerate the Canny edge detector algorithm and compared the performance of the hybrid implementation with that of the optimized separate CPU and FPGA implementations. Our results show up to 4.8X speedup for the hybrid implementation over the CPU only implementation and up to 2.1X over the FPGA only implementation.more » « less
-
Constructing k-nearest neighbor (kNN) graphs is a fundamental component in many machine learning and scientific computing applications. Despite its prevalence, efficiently building all-nearest-neighbor graphs at scale on distributed heterogeneous HPC systems remains challenging, especially for large sparse non-integer datasets. We introduce optimizations for algorithms based on forests of random projection trees. Our novel GPU kernels for batched, within leaf, exact searches achieve 1.18× speedup over sparse reference kernels with less peak memory, and up to 19× speedup over CPU for memory-intensive problems. Our library,PyRKNN, implements distributed randomized projection forests for approximate kNN search. Optimizations to reduce and hide communication overhead allow us to achieve 5× speedup, in per iteration performance, relative to GOFMM (another projection tree, MPI-based kNN library), for a 64M 128d dataset on 1,024 processes. On a single-node we achieve speedup over FAISS-GPU for dense datasets and up to 10× speedup over CPU-only libraries.PyRKNNuniquely supports distributed memory kNN graph construction for both dense and sparse coordinates on CPU and GPU accelerators.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3219819.3219889
