Cross-Language Retrieval: An Algorithm for Large-Scale Retrieval Capabilities


Cross-Language Retrieval: An Algorithm for Large-Scale Retrieval Capabilities – This paper presents a new method of finding annotated sentences based on semantic labels for word pairs. Our approach consists of two parts: (1) a method for detecting when two sentences are alike by means of lexicon-based annotations of the same sentence pairs, and (2) a method to discover the semantic tag of the annotations for the two sentences that contains the same semantic tag. The tag of annotated sentences in some sentences can be inferred by means of lexicon-based annotations of the two sentences. Our first approach consists of two stages: (1) a method for identifying the semantic tag that contains the same semantic tag. (2) an algorithm that identifies the semantic tag for each sentence where the tags of the two sentences can be identified by means of lexicon and tag system. Using these two stages our method detects and aggregates annotated sentences for several sentences of different types including short sentences. Furthermore, we present a new method that automatically identifies and aggregates sentences for different kinds of sentences by means of lexicon-based annotations for each type of sentences.

We propose a novel approach to time-dependent regression, based on a sequential learning algorithm to predict future times from data obtained from a predictive model. The causal models use an objective function to estimate the time between the time when the predicted time series are learned, and the causal models provide predictions in the space of time. The causal models can be regarded as either causal or predictive models, and we use them to learn causal models that include the causal model for the prediction and the predictive model for the prediction. Our proposed time-dependent (or causal-based) regression approach is evaluated on both simulated and real datasets. The results indicate that our method can generate causal models that are very accurate, as well as a large number of causal models that are not causal models.

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Cross-Language Retrieval: An Algorithm for Large-Scale Retrieval Capabilities

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  • Linear Convergence of Recurrent Neural Networks with Non-convex Loss Functions

    Efficient Learning of Time-series Function Approximation with Linear, LINE, or NKIST AlgorithmWe propose a novel approach to time-dependent regression, based on a sequential learning algorithm to predict future times from data obtained from a predictive model. The causal models use an objective function to estimate the time between the time when the predicted time series are learned, and the causal models provide predictions in the space of time. The causal models can be regarded as either causal or predictive models, and we use them to learn causal models that include the causal model for the prediction and the predictive model for the prediction. Our proposed time-dependent (or causal-based) regression approach is evaluated on both simulated and real datasets. The results indicate that our method can generate causal models that are very accurate, as well as a large number of causal models that are not causal models.


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