Web-scale distributional similarity and entity set expansion

Pantel, Patrick; Crestan, Eric; Borkovsky, Arkady; Popescu, Ana-Maria; Vyas, Vishnu

doi:10.3115/1699571.1699635

Cited by 175 publications

(129 citation statements)

References 44 publications

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“…MapReduce has been used for computing similarity between words or objects on the Web [1,9,20]. Several works discuss using CPU and GPU environments.…”

Section: Related Workmentioning

confidence: 99%

Parallelization of large vector similarity computations in a hybrid CPU+GPU environment

Czarnul

2017

J Supercomput

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The paper presents design, implementation and tuning of a hybrid parallel OpenMP+CUDA code for computation of similarity between pairs of a large number of multidimensional vectors. The problem has a wide range of applications, and consequently its optimization is of high importance, especially on currently widespread hybrid CPU+GPU systems targeted in the paper. The following are presented and tested for computation of all vector pairs: tuning of a GPU kernel with consideration of memory coalescing and using shared memory, minimization of GPU memory allocation costs, optimization of CPU-GPU communication in terms of size of data sent, overlapping CPU-GPU communication and kernel execution, concurrent kernel execution, determination of best sizes for data batches processed on CPUs and GPUs along with best GPU grid sizes. It is shown that all codes scale in hybrid environments with various relative performances of compute devices, even for a case when comparisons of various vector pairs take various amounts of time. Tests were performed on two high-performance hybrid systems with: 2 x Intel Xeon E5-2640 CPU + 2 x NVIDIA Tesla K20m and latest generation 2 x Intel Xeon CPU E5-2620 v4 + NVIDIA's Pascal generation GTX 1070 cards. Results demonstrate expected improvements and beneficial optimizations important for users incorporating such types of computations into their parallel codes run on similar systems.

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“…MapReduce has been used for computing similarity between words or objects on the Web [1,9,20]. Several works discuss using CPU and GPU environments.…”

Section: Related Workmentioning

confidence: 99%

Parallelization of large vector similarity computations in a hybrid CPU+GPU environment

Czarnul

2017

J Supercomput

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“…For example, "dog" and "cat" should have a high peer similarity score. Following existing work (Hearst, 1992;Kozareva et al, 2008;Shi et al, 2010;Agirre et al, 2009;Pantel et al, 2009), we built a peer similarity graph containing about 40.5 million nodes and 1.33 billion edges.…”

Section: Building Term Clustersmentioning

confidence: 99%

Unsupervised Template Mining for Semantic Category Understanding

Shi¹,

Shi²,

Lin³

et al. 2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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We propose an unsupervised approach to constructing templates from a large collection of semantic category names, and use the templates as the semantic representation of categories. The main challenge is that many terms have multiple meanings, resulting in a lot of wrong templates. Statistical data and semantic knowledge are extracted from a web corpus to improve template generation. A nonlinear scoring function is proposed and demonstrated to be effective. Experiments show that our approach achieves significantly better results than baseline methods. As an immediate application, we apply the extracted templates to the cleaning of a category collection and see promising results (precision improved from 81% to 89%).

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“…In such work, a word is represented by the distribution of other words that co-occur with it. Distributional representations of words have been successfully used in many language processing tasks such as entity set expansion (Pantel et al, 2009), part-of-speech (POS) tagging and chunking (Huang and Yates, 2009), ontology learning (Curran, 2005), computing semantic textual similarity (Besançon et al, 1999), and lexical inference (Kotlerman et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Learning to Predict Distributions of Words Across Domains

Bollegala

Weir

Carroll

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Although the distributional hypothesis has been applied successfully in many natural language processing tasks, systems using distributional information have been limited to a single domain because the distribution of a word can vary between domains as the word's predominant meaning changes. However, if it were possible to predict how the distribution of a word changes from one domain to another, the predictions could be used to adapt a system trained in one domain to work in another. We propose an unsupervised method to predict the distribution of a word in one domain, given its distribution in another domain. We evaluate our method on two tasks: cross-domain partof-speech tagging and cross-domain sentiment classification. In both tasks, our method significantly outperforms competitive baselines and returns results that are statistically comparable to current stateof-the-art methods, while requiring no task-specific customisations.

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Web-scale distributional similarity and entity set expansion

Cited by 175 publications

References 44 publications

Parallelization of large vector similarity computations in a hybrid CPU+GPU environment

Parallelization of large vector similarity computations in a hybrid CPU+GPU environment

Unsupervised Template Mining for Semantic Category Understanding

Learning to Predict Distributions of Words Across Domains

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