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公开(公告)号：US20240338393A1
公开(公告)日：2024-10-10
申请号：US18628271
申请日：2024-04-05
Applicant: NEC Laboratories America, Inc.
Inventor： Christopher Malon , Iain Melvin , Christopher A. White
IPC: G06F16/332 , G06F16/338 , G06F40/30
CPC classification number: G06F16/3323 , G06F16/338 , G06F40/30
Abstract: Systems and methods are provided for analyzing and visualizing document corpuses based on user-defined semantic features, including initializing a Natural Language Inference (NLI) classification model pre-trained on a diverse linguistic dataset, analyzing a corpus of textual documents with semantic features described in natural language by a user. For each semantic feature, a classification process is executed using the NLI model to assess implication strength between sentences in the documents and the semantic feature, the classification process including a confidence scoring mechanism to quantify implication strength. Implication scores can be aggregated for each of the documents to form a composite semantic implication profile, and a dimensionality reduction technique, can be applied to the composite semantic implication profiles of each of the documents to generate a two-dimensional semantic space representation. The two-dimensional semantic space representation can be dynamically adjusted based on iterative user feedback regarding the accuracy of semantic implication assessments.
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公开(公告)号：US20230035641A1
公开(公告)日：2023-02-02
申请号：US17840987
申请日：2022-06-15
Applicant: NEC Laboratories America, Inc.
Inventor： Christopher Malon
IPC: G06N3/08 , G06F40/166 , G06F40/40 , G06F40/258
Abstract: A method for neural network training is provided. The method inputs a training set of textual claims, lists of evidence including gold evidence chains, and claim labels labelling the evidence with respect to the textual claims. The claim labels include refutes, supports, and not enough information (NEI). The method computes an initial set of document retrievals for each of the textual claims. The method also includes computing an initial set of page element retrievals including sentence retrievals from the initial set of document retrievals for each of the textual claims. The method creates, from the training set of textual claims, a Leave Out Training Set which includes input texts and target texts relating to the labels. The method trains a sequence-to-sequence neural network to generate new target texts from new input texts using the Leave Out Training Set.
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公开(公告)号：US20140236578A1
公开(公告)日：2014-08-21
申请号：US14166273
申请日：2014-01-28
Applicant: NEC Laboratories America, Inc.
Inventor： Christopher Malon , Bing Bai
CPC classification number: G06F17/28 , G06F17/2785 , G06N3/02
Abstract: Systems and methods are disclosed to answer free form questions using recursive neural network (RNN) by defining feature representations at every node of a parse trees of questions and supporting sentences, when applied recursively, starting with token vectors from a neural probabilistic language model; and extracting answers to arbitrary natural language questions from supporting sentences.
Abstract translation: 公开了系统和方法，通过在从神经概率语言模型的令牌向量开始递归应用时，通过定义问题和支持句子的解析树的每个节点上的特征表示，使用递归神经网络（RNN）来回答自由形式问题; 并从支持句子中提取任意自然语言问题的答案。
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4.发明申请公开(公告)号：US20140180977A1
公开(公告)日：2014-06-26
申请号：US14077400
申请日：2013-11-12
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto , Pierre-Francois Laquerre , Christopher Malon , Hans-Peter Graf , Iain Melvin
IPC: G06N99/00
CPC classification number: G06N99/005 , G06K9/00147 , G06K9/4652 , G06K9/623 , G06K9/6262 , G06K9/6857 , G06T7/0012 , G06T2207/10056 , G06T2207/20081 , G06T2207/30024
Abstract: Systems and methods are disclosed for classifying histological tissues or specimens with two phases. In a first phase, the method includes providing off-line training using a processor during which one or more classifiers are trained based on examples, including: finding a split of features into sets of increasing computational cost, assigning a computational cost to each set; training for each set of features a classifier using training examples; training for each classifier, a utility function that scores a usefulness of extracting the next feature set for a given tissue unit using the training examples. In a second phase, the method includes applying the classifiers to an unknown tissue sample with extracting the first set of features for all tissue units; deciding for which tissue unit to extract the next set of features by finding the tissue unit for which a score: S=U−h*C is maximized, where U is a utility function, C is a cost of acquiring the feature and h is a weighting parameter; iterating until a stopping criterion is met or no more feature can be computed; and issuing a tissue-level decision based on a current state.
Abstract translation: 公开了用于对两个阶段的组织学组织或标本进行分类的系统和方法。 在第一阶段中，该方法包括使用处理器提供离线训练，在该训练期间，基于示例对一个或多个分类器进行训练，包括：将特征分组发现成增加计算成本的集合，为每个集合分配计算成本; 训练每组功能一个分类器使用训练样例; 每个分类器的训练，一个效用函数，其使用训练示例评估为给定组织单位提取下一个特征集的有用性。 在第二阶段中，该方法包括通过提取所有组织单元的第一组特征将分类器应用于未知组织样本; 决定哪个组织单元通过找到最大化分数S = U-h * C的组织单位来提取下一组特征，其中U是效用函数，C是获取特征的成本，h是 加权参数; 迭代直到满足停止标准或不能计算更多的特征; 以及基于当前状态发布组织级决定。
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公开(公告)号：US12205027B2
公开(公告)日：2025-01-21
申请号：US17840987
申请日：2022-06-15
Applicant: NEC Laboratories America, Inc.
Inventor： Christopher Malon
IPC: G06F17/00 , G06F40/166 , G06F40/258 , G06F40/40 , G06N3/08
Abstract: A method for neural network training is provided. The method inputs a training set of textual claims, lists of evidence including gold evidence chains, and claim labels labelling the evidence with respect to the textual claims. The claim labels include refutes, supports, and not enough information (NEI). The method computes an initial set of document retrievals for each of the textual claims. The method also includes computing an initial set of page element retrievals including sentence retrievals from the initial set of document retrievals for each of the textual claims. The method creates, from the training set of textual claims, a Leave Out Training Set which includes input texts and target texts relating to the labels. The method trains a sequence-to-sequence neural network to generate new target texts from new input texts using the Leave Out Training Set.
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Patent Agency Ranking
公开(公告)号：US20220327586A1
公开(公告)日：2022-10-13
申请号：US17716347
申请日：2022-04-08
Applicant: NEC Laboratories America, Inc.
Inventor： Christopher Malon
Abstract: Systems and methods for opinion summarization are provided for extracting and counting frequent opinions. The method includes performing a frequency analysis on an inputted list of product reviews for a single item and an inputted corpus of reviews for a product category containing the single item to identify one or more frequent phrases; fine tuning a pretrained transformer model to produce a trained neural network claim generator model, and generating a trained neural network opposing claim generator model based on the trained neural network claim generator model. The method further includes generating a pair of opposing claims for each of the one or more frequent phrases, wherein a generated positive claim is entailed by the product reviews for the single item and a negative claim refutes the positive claim, and outputting a count of sentences entailing the positive claim and a count of sentences entailing the negative claim.
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公开(公告)号：US20220327489A1
公开(公告)日：2022-10-13
申请号：US17714434
申请日：2022-04-06
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Iain Melvin , Christopher A White , Christopher Malon , Hans Peter Graf
IPC: G06Q10/10 , G06F40/279
Abstract: Systems and methods for matching job descriptions with job applicants is provided. The method includes allocating each of one or more job applicants' curriculum vitae (CV) into sections; applying max pooled word embedding to each section of the job applicants' CVs; using concatenated max-pooling and average-pooling to compose the section embeddings into an applicant's CV representation; allocating each of one or more job position descriptions into specified sections; applying max pooled word embedding to each section of the job position descriptions; using concatenated max-pooling and average-pooling to compose the section embeddings into a job representation; calculating a cosine similarity between each of the job representations and each of the CV representations to perform job-to-applicant matching; and presenting an ordered list of the one or more job applicants or an ordered list of the one or more job position descriptions to a user.
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公开(公告)号：US09060685B2
公开(公告)日：2015-06-23
申请号：US13850694
申请日：2013-03-26
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto , Pierre-Francois Laquerre , Christopher Malon , Hans-Peter Graf
CPC classification number: A61B5/0033 , G06F19/36 , G06K9/00127 , G06K9/6227 , G06K9/6256 , G06T7/0012 , G06T2207/10056 , G06T2207/20081 , G06T2207/20084 , G06T2207/30024
Abstract: Disclosed is a computer implemented method for fully automated tissue diagnosis that trains a region of interest (ROI) classifier in a supervised manner, wherein labels are given only at a tissue level, the training using a multiple-instance learning variant of backpropagation, and trains a tissue classifier that uses the output of the ROI classifier. For a given tissue, the method finds ROIs, extracts feature vectors in each ROI, applies the ROI classifier to each feature vector thereby obtaining a set of probabilities, provides the probabilities to the tissue classifier and outputs a final diagnosis for the whole tissue.
Abstract translation: 公开了一种用于全自动组织诊断的计算机实现方法，其以受监督的方式训练感兴趣区域（ROI）分类器，其中仅在组织水平给出标签，使用反向传播的多实例学习变体的培训和火车 使用ROI分类器的输出的组织分类器。 对于给定的组织，该方法找到ROI，在每个ROI中提取特征向量，将ROI分类器应用于每个特征向量，从而获得一组概率，为组织分类器提供概率并输出整个组织的最终诊断。
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公开(公告)号：US20130315465A1
公开(公告)日：2013-11-28
申请号：US13850694
申请日：2013-03-26
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto , Pierre-Francois Laquerre , Christopher Malon , Hans-Peter Graf
CPC classification number: A61B5/0033 , G06F19/36 , G06K9/00127 , G06K9/6227 , G06K9/6256 , G06T7/0012 , G06T2207/10056 , G06T2207/20081 , G06T2207/20084 , G06T2207/30024
Abstract: Disclosed is a computer implemented method for fully automated tissue diagnosis that trains a region of interest (ROI) classifier in a supervised manner, wherein labels are given only at a tissue level, the training using a multiple-instance learning variant of backpropagation, and trains a tissue classifier that uses the output of the ROI classifier. For a given tissue, the method finds ROIs, extracts feature vectors in each ROI, applies the ROI classifier to each feature vector thereby obtaining a set of probabilities, provides the probabilities to the tissue classifier and outputs a final diagnosis for the whole tissue.
Abstract translation: 公开了一种用于全自动组织诊断的计算机实现方法，其以受监督的方式训练感兴趣区域（ROI）分类器，其中仅在组织水平给出标签，使用反向传播的多实例学习变体的培训和火车 使用ROI分类器的输出的组织分类器。 对于给定的组织，该方法找到ROI，在每个ROI中提取特征向量，将ROI分类器应用于每个特征向量，从而获得一组概率，为组织分类器提供概率并输出整个组织的最终诊断。
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公开(公告)号：US20220277197A1
公开(公告)日：2022-09-01
申请号：US17674461
申请日：2022-02-17
Applicant: NEC Laboratories America, Inc.
Inventor： Christopher Malon
IPC: G06N3/08 , G06F40/279 , G06F40/166 , G06F40/242
Abstract: Methods and systems for language processing include augmenting an original training dataset to produce an augmented dataset that includes a first example that includes a first scrambled replacement for a first word and a definition of the first word, and a second example that includes a second scrambled replacement for the first word and a definition of an alternative to the first word. A neural network classifier is trained using the augmented dataset.
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