Ranking Measures and Loss Functions in Learning to Rank (2009)(About) > While most learning-to-rank methods learn the ranking function by minimizing the loss functions, it is the ranking measures (such as NDCG and MAP) that are used to evaluate the performance of the learned ranking function. In this work, we reveal the relationship between ranking measures and loss functions in learning-to-rank methods, such as Ranking SVM, RankBoost, RankNet, and ListMLE.
> We show that these loss functions are upper bounds of the measure-based ranking errors. As a result, the minimization of these loss functions will lead to the maximization of the ranking measures. The key to obtaining this result is to model ranking as a sequence of classification tasks, and define a so-called essential loss as the weighted sum of the classification errors of individual tasks in the sequence.
android - speech recognition reduce possible search results - Stack Overflow(About) > You cannot change what google returns. You can only process the results. Fortunately, you can process the results to increase the chance of a match. For example, you could use a phonetic matching algorithm like Soundex
Phoneme Recognition (caveat emptor) – CMUSphinx Open Source Speech Recognition(About) Frequently, people want to use Sphinx to do phoneme recognition. In other words, they would like to convert speech to a stream of phonemes rather than words. This is possible, although the results can be disappointing. The reason is that automatic speech recognition relies heavily on contextual constraints (i.e. language modeling) to guide the search algorithm.
[En direct] La structure de Notre-Dame de Paris «est sauvée» - France - RFI(About) La structure de Notre-Dame de Paris « est sauvée et préservée dans sa globalité », ont affirmé les pompiers lundi soir à 23h **après des heures d'angoisse**.
"Nous rebâtirons Notre-Dame, parce que c’est ce que les Français attendent, parce que c’est ce que notre histoire mérite, parce que c’est notre destin profond"
[1806.04411] Named Entity Recognition with Extremely Limited Data(About) **"Named Entity Search (NES)"**
> We propose exploring **named entity recognition as a search task**, where the named entity class of interest is a query, and entities of that class are the relevant "documents". What should that query look like? Can we even perform NER-style labeling with tens of labels? This study presents an exploration of CRF-based NER models with handcrafted features and of how we might transform them into search queries.
> We do not propose this as a replacement
for NER, but as something to be used for an ephemeral or contextual
class of entity, when it does not make sense to label hundreds or
thousands of instances to learn a classifier