1 Yahoo! Research, First Ave. Sunnyvale, CA , U.S.A., .. indeed, there has been to date no uniform terminology established for the The goal was to tell whether a given sentence is subjective or not judging. Authors: MEZEI, Adina-Maria1 [email protected] define and exemplify objective and (CHARACTER) subjective sentences occurring in narrative fiction. How to use subjective in a sentence. Example sentences with the word subjective. subjective example sentences.
We witnessed performance improvements across the board.
Give me an example of a subjective sentence?
Zipf heavy-tailed and Uniform primary key distributions. An HBase region is stored as a sequence of searchable key-value maps. The topmost is a mutable in-memory store, called MemStore, which absorbs the recent write put operations. Once a MemStore overflows, it is flushed to disk, creating a new HFile. HBase adopts multi-versioned concurrency control — that is, MemStore stores all data modifications as separate versions. Multiple versions of one key may therefore reside in MemStore and the HFile tier.
EBSCOhost | | PERSPECTIVES IN FICTION. CHARACTER-SUBJECTIVE AND OBJECTIVE SENTENCES.
A read get operation, which retrieves the value by key, scans the HFile data in BlockCache, seeking the latest version. To reduce the number of disk accesses, HFiles are merged in the background. This process, called compaction, removes the redundant cells and creates larger files. However, their traditional design makes no attempt to compact the in-memory data.
This stems from historical reasons: With recent changes in the hardware landscape, the overall MemStore size managed by RegionServer can be multiple gigabytes, leaving a lot of headroom for optimization. This work pattern decreases the frequency of flushes to HDFS, thereby reducing the write amplification and the overall disk footprint.
With fewer flushes, the write operations are stalled less frequently as the MemStore overflows, and as a result, the write performance is improved. Less data on disk also implies less pressure on the block cache, higher hit rates, and eventually better read response times.
Finally, having fewer disk writes also means having less compaction happening in the background, i. All in all, the effect of in-memory compaction can be thought of as a catalyst that enables the system to move faster as a whole.
The rule-based objective classifier achieved Based on these results, we expect that the initial training set generated by these classifiers is of relatively high quality.
PERSPECTIVES IN FICTION. CHARACTER-SUBJECTIVE AND OBJECTIVE SENTENCES.
These subjective and objective sentences form our initial training set. Consequently, we decided to learn extraction patterns that are correlated with objectivity and using them as features in a machine learning algorithm. They use AutoSlog-TS algorithm to learn extraction pattern.
It does not need annotated texts for training.
Give me an example of a subjective sentence? | Yahoo Answers
In our experiments, the subjective sentences were the relevant texts, and the objective sentences were the irrelevant texts. We trained the EP learner on the initial training set to generate patterns associated with objectivity and patterns associated with subjectivity. AutoSlog-TS merely ranks patterns in order of their association with the relevant texts, so we automatically selected the best patterns for each class.
We use two thresholds to select: Pr relevant pattern i.
Next, we incorporated the learned EPs into the rule-based classifiers. The subjective patterns were added to the set of strong subjective clues. The strategy used by the rule-based subjective classifier remained the same. However, the strategy used by the rule-based objective classifier was augmented as follows: Adding EPs to the rule-based classifiers clearly expanded their coverage with relatively smaller drops in precision.
We used naive Bayes as our learning algorithm. We trained the naive Bayes classifier using the initial training set and several types of features. The features for training: The following table shows the performance of the naive Bayes classifier on the test set.
The classifier achieves relatively balanced recall and precision for both subjective and objective sentences. The training sentences will be similar to one another and less heterogenous than the set of sentences that the classifier will ultimately be applied to. We therefore try to improve the classifier by generating a new training set using the classifier itself.
We hypothesized that the naive Bayes classifier might reliably label a different, and more diverse, set of sentences in the unlabeled corpus than the rule-based classifiers did. The procedure we use is a variant of self-training.