Patent Document ID: 9779087
Application ID: 14105973

Base Claim:
1. A computer-implemented method, comprising: obtaining, at a computing device having one or more processors, (i) an aligned bi-text for a source language and a target language, the aligned bi-text comprising a plurality of source-target sentence pairs, and (ii) a supervised sequence model for the source language; labeling, at the computing device, each word of a source side of the aligned bi-text using the supervised sequence model to obtain a labeled source side of the aligned bi-text; projecting, at the computing device, labels from the labeled source side to a target side of the aligned bi-text to obtain a labeled target side of the aligned bi-text, wherein each label of the labeled source and target sides of the aligned bi-text is a named entity type tag for a particular word; filtering, at the computing device, the labeled target side of the aligned bi-text for the target language to obtain a filtered target side of the aligned bi-text for training a sequence model for the target language for a named entity segmentation system, wherein the filtering comprises discarding any particular source-target sentence pair when (i) a threshold amount of tokens of the particular source-target sentence pair are unaligned or (ii) a source named entity of the particular source-target sentence pair is not aligned with a target sentence token; training, at the computing device, the sequence model for the target language using posterior regularization with soft constraints on the filtered target side to learn a set of parameters for the target language; obtaining, at the computing device, a trained sequence model for the target language using the set of parameters for the target language, the trained sequence model being configured to model a probability distribution over possible labels for text in the target language; receiving, at the computing device, an input text in the target language; analyzing, at the computing device, the input text using the trained sequence model for the target language; and generating, at the computing device, an output based on the analyzing of the input text using the trained sequence model.

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Claim 2:
2. The computer-implemented method of claim 1 , wherein training the sequence model using posterior regularization with soft constraints includes optimizing an objective function using stochastic projected gradients for parameters for the sequence model and optimal dual variables, wherein the objective function is defined as: max θ ⁢ min λ ≥ 0 ⁢ b · λ + log ⁢ ∑ Y ⁢ p θ ⁡ ( Y ❘ X ) ⁢ e - λ * · ϕ ⁡ ( Y , X ) - γ ⁢  θ  , where θ represents the parameters for the sequence model, λ* represents the optimal dual variables, b represents a vector of values that serve as upper bounds to the expectations of every constraint feature φ, p θ (Y|X) represents a probability distribution for all sentences in a corpus X and their labelings Y, and γ∥θ| represents a gamma distribution of the constraint features φ.