Table 6: Comparison of grammar/lexicon observed in the model tagging vs. gold tagging in terms of precision and recall measures for supertagging on CCG—TUT.

Precision and recall of grammar and lexicon.

Table 3: Comparison of grammar/lexicon observed in the model tagging vs. gold tagging in terms of precision and recall measures for supertagging on CCGbank data.

We can obtain a more-fine grained understanding of how the models differ by considering the precision and recall values for the grammars and lexicons of the different models, given in Table 3.

For experience detection, the performance was very promising, closed to 92% in precision and recall when all the features were used.

We not only compared our results with the baseline in terms of precision and recall but also

The performance for the best case with all the features included is very promising, closed to 92% precision and recall .

In order to see the effect of including individual features in the feature set, precision and recall were measured after eliminating a particular feature from the full set.

Note that the precision and recall are macro-averaged values across the two classes, activity and state.

Many researchers are trying to use IE to create large-scale knowledge bases from natural language text on the Web, but existing relation-specific techniques do not scale to the thousands of relations encoded in Web text — while relation-independent techniques suffer from lower precision and recall , and do not canonicalize the relations.

We expect that lists with higher similarity are more likely to contain phrases which are related to our seeds; hence, by varying the similarity threshold one may produce lexicons representing different compromises between lexicon precision and recall .

Open extraction is more scalable, but has lower precision and recall .

Open IE, self-supervised learning of unlexicalized, relation-independent extractors (Banko et al., 2007), is a more scalable approach, but suffers from lower precision and recall , and doesn’t canonicalize the relations.

The goal of set expansion techniques is to generate high precision sets of related items; hence, these techniques are evaluated based on lexicon precision and recall .

do not report (NR) separate values for precision and recall on this dataset.

Differences in both precision and recall between the baseline and the other systems are statistically significant at p < 0.01 using the two-tailed Fisher’s exact test.

Differences in both precision and recall between the baseline and the Span-HMM systems are statistically significant at p < 0.01 using the two-tailed Fisher’s exact test.

This paper presents WOE, an open IE system which improves dramatically on TextRunner’s precision and recall .

WOE can operate in two modes: when restricted to P08 tag features, it runs as quickly as TextRunner, but when set to use dependency-parse features its precision and recall rise even higher.

high precision and recall , they are limited by the availability of training data and are unlikely to scale to the thousands of relations found in text on the Web.

WOE can operate in two modes: when restricted to shallow features like part-of-speech (POS) tags, it runs as quickly as Textrunner, but when set to use dependency-parse features its precision and recall rise even higher.

The precision and recall of our method for detecting conversations is shown in Table 2.

To calculate precision and recall for the two baseline social networks, we set a threshold 75 to derive a binary prediction from the continuous edge weights.

The precision and recall values shown for the baselines in Table 2 represent the highest performance we achieved by varying t between 0 and 1 (maximizing F-measure over 25).

In figure 5 we compare the precision and recall of LDA-SP against the top two performing systems described by Pantel et al.

We find that LDA-SP achieves both higher precision and recall than ISP.IIM-\/.

Figure 5: Precision and recall on the inference filtering task.

It seems that the model gets quickly saturated in terms of incorporating new information and therefore precision and recall do not drastically change for increasing dataset sizes.

For this reason we broke down the summary measures of precision and recall into their original components: true/false positive (TP/FF) and negative (TN/FN) counts presented in the 2 x 2 contingency table of Figure 1.

The optimal solution applying [1* = 0.38 is more balanced between precision and recall and