Index of papers in Proc. ACL 2014 that mention
  • randomly sampled
Tamura, Akihiro and Watanabe, Taro and Sumita, Eiichiro
Training
To reduce computation, we employ NCE, which uses randomly sampled sentences from all target language sentences in Q as e‘, and calculate the expected values by a beam search with beam width W to truncate alignments with low scores.
Training
where 6+ is a target language sentence aligned to f+ in the training data, i.e., (f+, 6+) 6 T, e‘ is a randomly sampled pseudo-target language sentence with length |e+|, and N denotes the number of pseudo-target language sentences per source sentence f+.
Training
In a simple implementation, each 6— is generated by repeating a random sampling from a set of target words (V6) |e+| times and lining them up sequentially.
randomly sampled is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Hashimoto, Chikara and Torisawa, Kentaro and Kloetzer, Julien and Sano, Motoki and Varga, István and Oh, Jong-Hoon and Kidawara, Yutaka
Experiments
For the test data, we randomly sampled 23,650 examples of (event causality candidate, original sentence) among which 3,645 were positive from 2,451,254 event causality candidates extracted from our web corpus (Section 3.1).
Experiments
Note that, for the diversity of the sampled scenarios, our sampling proceeded as follows: (i) Randomly sample a beginning event phrase from the generated scenarios.
Experiments
(ii) Randomly sample an effect phrase for the beginning event phrase from the scenarios.
randomly sampled is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Volkova, Svitlana and Coppersmith, Glen and Van Durme, Benjamin
Conclusions and Future Work
This may be an effect of ‘sparseness’ of relevant user data, in that users talk about politics very sporadically compared to a random sample of their neighbors.
Identifying Twitter Social Graph
In the Fall of 2012, leading up to the elections, we randomly sampled n = 516 Democratic and m = 515 Republican users.
Identifying Twitter Social Graph
For each such user we collect recent tweets and randomly sample their immediate k = 10 neighbors from follower, friend, user mention, reply, retweet and hashtag social circles.
Identifying Twitter Social Graph
Similar to the candidate-centric graph, for each user we collect recent tweets and randomly sample user social circles in the Fall of 2012.
randomly sampled is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Flati, Tiziano and Vannella, Daniele and Pasini, Tommaso and Navigli, Roberto
Phase 1: Inducing the Page Taxonomy
Taxonomy quality To evaluate the quality of our page taxonomy we randomly sampled 1,000 Wikipedia pages.
Phase 1: Inducing the Page Taxonomy
It was established by selecting the combination, among all possible permutations, which maximized precision on a tuning set of 100 randomly sampled pages, disjoint from our page dataset.
Phase 3: Category taxonomy refinement
Category taxonomy quality To estimate the quality of the category taxonomy, we randomly sampled 1,000 categories and, for each of them, we manually associated the super-categories which were deemed to be appropriate hypemyms.
randomly sampled is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Mitra, Sunny and Mitra, Ritwik and Riedl, Martin and Biemann, Chris and Mukherjee, Animesh and Goyal, Pawan
Conclusions
Through manual evaluation we found that the algorithm could correctly identify 60.4% birth cases from a set of 48 random samples and 57% split/join cases from a set of 21 randomly picked samples.
Evaluation framework
We selected 48 random samples of candidate words for birth cases and 21 random samples for split/join cases.
Evaluation framework
A further analysis of the words marked due to birth in the random samples indicates that there are 22 technology-related words, 2 slangs, 3 economics related words and 2 general words.
randomly sampled is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
van Gompel, Maarten and van den Bosch, Antal
Data preparation
The parallel corpus is randomly sampled into two large and equally-sized parts.
Data preparation
The final test set is created by randomly sampling the desired number of test instances.
Experiments & Results
The final test sets are a randomly sampled 5, 000 sentence pairs from the 200, 000-sentence test split for each language pair.
randomly sampled is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: