This indicates that the confidence score of any link connecting 253- to any source word is at most 1 /N .

From multiple alignments of the same sentence pair, we select high confidence links from different alignments based on their link confidence scores and alignment agreement ratio.

Where C (A) is the confidence score of the alignment A as defined in formula 1.

Select links whose confidence scores are higher than an empirically chosen threshold H as anchor links 1.

2When two equally close alignment links have the same confidence score ), we randomly select one of the tied links as the anchor link.

For each sentence pair, we also calculate the sentence alignment confidence score — log 0 (A|S, T).

measure suggests the possibility of selecting the alignment with the highest confidence score to obtain better alignments.

For each sentence pair in the CE test set, we calculate the confidence scores of the HMM alignment, the Block Model alignment and the MaXEnt alignment, then select the alignment with the highest confidence score .

Word embedding is used as the input to learn translation confidence score , which is combined with commonly used features in the conventional log-linear model.

yum] is the confidence score of how plausible the parent node should be created.

The recurrent input vector film] is concatenated with parent node representation sum] to compute the confidence score yum] .

The one-hot representation vector is used as the input, and a one-hidden-layer network generates a confidence score .

To train the neural network, we add the confidence scores to the conventional log-linear model as features.

We use recurrent neural network to generate two smoothed translation confidence scores based on source and target word embeddings.

RNNLM (Mikolov et al., 2010) is firstly used to generate the source and target word embeddings, which are fed into a one-hidden-layer neural network to get a translation confidence score .

Together with other commonly used features, the translation confidence score is integrated into a conventional log-linear model.

Given the representations of the smaller phrase pairs, recursive auto-encoder can generate the representation of the parent phrase pair with a reordering confidence score .

The two systems differed in acoustic models, confidence scoring model, state tracking method and parameters, number of supported routes (8 vs 40, for D81 and D82 respectively), presence of minor bugs, and user population.

As a baseline, we construct a handcrafted state tracking rule that follows a strategy common in commercial systems: it returns the SLU result with the maximum confidence score , ignoring all other hypotheses.

For example, if the user says “no” to an explicit confirmation or “go back” to an implicit confirmation, they are asked the same question again, which gives an opportunity for a higher confidence score .

Base features consider information about the current turn, including rank of the current SLU result (current hypothesis), the SLU result confidence score (s) in the current N-best list, the difference between the current hypothesis score and the best hypothesis score in the current N-best list, etc.

Those include the number of times an SLU result has been observed before, the number of times an SLU result has been observed before at a specific rank such as rank 1, the sum and average of confidence scores of SLU results across all past recognitions, the number of possible past user negations 0r confirmations 0f the current SLU result etc.

For example, from the current turn, we use the number of distinct SLU results, the entropy of the confidence scores , the best path score of the word confusion network, etc.

For dialog state tracking, most commercial systems use handcrafted heuristics, selecting the SLU result with the highest confidence score , and discarding alternatives.

As described in Section 3.2, the weight of each variable is a linear combination of the language model score, three classifier confidence scores , and three classifier disagreement scores.

Finally, the language model score, classifier confidence scores , and classifier disagreement scores are normalized to take values in [0, 1], based on the H00 2011 development data.

The confidence scores f (s’ , t) of classifiers, where t E E and E is the set of classifiers.

For each article instance in s’, the classifier computes the difference between the maximum confidence score among all possible choices of articles, and the confidence score of the observed article.

Next, to compute whpyg, we collect language model score and confidence scores from the article (ART), preposition (PREP), and noun number (NOUN) classifier, i.e., E = {ART, PREP, NOUN}.

When measuring the gain due to 21131213312 2 1 (change cat to cats), the weight wNoungmluml is likely to be small since A cats will get a low language model score, a low article classifier confidence score, and a low noun number classifier confidence score .

In particular, MetaMap (Aronson, 2001) can take a text string as the input, segment it into phrases, and then map each phrase to multiple UMLS CUIs with confidence scores .

The confidence score is an indicator of the quality of the phrase-to-concept mapping by MetaMap.

confidence score as well as more detailed information about this concept.

As shown in Equation (3), the Balanced method regularizes the weights through two components: (1) normalized confidence score of each aspect,

Although MetaMap is able to rank all the candidate concepts with the confidence score and pick the most likely one, the accuracy is not very high.

i(e) is the normalized confidence score of the mapping for concept 6 generated by MetaMap.

Since each concept mapping is associated with a confidence score , we can incorporate them into the regularization function as follows:

The preliminary relation extractor of our optimization framework is not limited to the MaxEnt extractor, and can take any sentence level relation extractor with confidence scores .

Furthermore, the confidence scores which MultiR outputs are not normalized to the same scale, which brings us difficulties in setting up a confidence threshold to select the candidates.

We first train a preliminary sentence level extractor which can output confidence scores for its predictions, e.g., a maximum entropy or logistic regression model, and use this local extractor to produce local predictions.

Now the confidence score of a relation 7“ E R75 being assigned to tuple t can be calculated as:

The first component is the sum of the original confidence scores of all the selected candidates, and the second one is the sum of the maximal mention-level confidence scores of all the selected candidates.

Next, we built a decision tree classifier that improves the results on average by combining the outputs of the three models together with their confidence scores .

Finally, we will consider other alternatives to the decision tree framework when combining the results of the models with their confidence scores .

As the features of our DT classifier, we use the results of the models that are implemented in Section 4.1, together with the confidence scores calculated for each instance.

To calculate a confidence score for the models, we note that since each model makes its selection based on the language that gives the highest probability, a confidence score should indicate the relative highness of that probability compared to the probabilities of other languages.

where ,u’ and 0’ are the mean and the standard deviation values respectively, for a set of confidence scores calculated for a model on a small development set of 25 annotated queries from each language.

We speculate that it may be helpful to introduce a confidence score for each pattern.

For the second key, we utilize opinion words and opinion patterns with their confidence scores to represent an opinion target.

where conf denotes confidence score estimated by RWR, f req(-) has the same meaning as in Section 3.2.

where T is the opinion target set in which each element is classified as positive during opinion target refinement, s(ti) denotes confidence score exported by the target refining classifier.

Each node has an initial confidence score , e.g.

Each candidate has associated with it an initial confidence score , also detailed below.

Initial confidence scores of all candidates for a single NE mention are normalized to sum to l.

One is a setup where a ranking based solely on different initial confidence scores is used

The novelty of our approach is to use “pseudo negative examples” with reliable confidence score estimated by a classifier trained with positive and unlabeled examples.

Using naive Bayes classifier, we can estimate the confidence score c(d, s) that the sense of a data instance “(1”, whose features are f1, f2, ..., fn,

01 foreacth(T—P—N) 02 classify d by WSD system I— (P, T-P) 03 c(d, pos) <— the confidence score that d is

04 predicted as positive defined in equation (2) 05 c(d, neg) <— the confidence score that d is

We use a confidence score to label only the instances that the classifiers are most certain about.

We compute a confidence score for instance 2' with respect to semantic class Ck, by considering both the score of the Ck, classifier as well as the scores of the competing classifiers.

After each training cycle, all of the classifiers are applied to the remaining unlabeled instances and each classifier labels the (positive) instances that it is most confident about (i.e., the instances that it classifies with a confidence score 2 60f).

3.3 Cluster display, feature f-Measure and confidence score

In addition, for each verb in a cluster, a confidence score is displayed which is the ratio between the sum of the F-measures of its cluster maXimised features over the sum of the F-measures of the overall cluster maXimised features.

Verbs whose confidence score is O are considered as orphan data.

Section 3) highlight strong cluster cohesion with a number of clusters close to the number of gold classes (13 clusters for 11 gold classes); a low number of orphan verbs (i.e., verbs whose confidence score is zero); and a high Cumulated Micro Precision (CMP = 0.3) indicating homogeneous clusters in terms of maximis-ing features.

(ii) Context-sensitive disambiguation of morphological features (e.g., by combination with a chunker and adjustment of confidence scores for morphological features over all tokens in the current chunk, cf.

A novel feature of our approach as compared to existing applications of these methods is that confidence scores are not attached to plain strings, but to ontological descriptions: Tufis, for example, assigned confidence scores not to tools (as in a weighted majority vote), but rather, assessed the ‘credibility’ of a tool with respect to the predicted tag.

1: If a tool supports a description with its analysis, the confidence score is increased by l (or by l /n if the tool proposes n alternative annotations).

The hasCase descriptions have identical confidence scores , so that the first hasCase description that the algorithm encounters is chosen for the set of resulting descriptions, the other one is ruled out because of their inconsistency.

The label propagation assigns a confidence score 0 = (01,...,cm) to each node U 2 ul, ..., um, where the score is a real number between —1 and l. A score close to 1 indicates that we are very confident that the node (user) is a chronic critic.

Thus, by minimizing E(c), we assign the confidence scores considering the results of the opinion mining and agreement relationships among the users.

To avoid this problem, Yin and Tan (2011) introduced a neutral fact, which decreases each confidence score by considering the distance from the seeds.

Finally, we observe a negative correlation between the classifier confidence scores and a SAC, as expected.

In other words, the goal is to show that the confidence scores of a sentiment classifier are negatively correlated with SAC.

The confidence score of a classifier8 for given text t is computed as follows:

Table 3 presents the accuracy of the classifiers along with the correlations between the confidence score and observed SAC values.

Distribution of Confidence Scores .

The vast majority of tokens has a confidence score close to l, the median lies at 0.9966.

The new KB covers all super relations and stores the knowledge in the format of (rela-tionJiame, argument_l, argumentl, confidence), where the confidence is computed based on the relation detector confidence score and relation popularity in the corpus.

If we detect multiple relations in the question, and the same answer is generated from more than one relations, we sum up all those confidence scores to make such answers more preferable.

In this scenario, we sort the answers based upon the confidence scores and only consider up to p answers for each question.

Confidence scores from an ASR system (which incorporate N-gram probabilities) are optimized in order to produce the most likely sequence of words rather than the accuracy of individual word detections.

For each term t and document d we propose interpolating the ASR confidence score for a particular detection td with the top scoring hit in d which we’ll call £1.

However to verify that this approach is worth pursuing, we sweep a range of small 04 values, on the assumption that we still do want to mostly rely on the ASR confidence score for term detection.