Abstract: A process for machine learning processing of a machine learning model in which a natural language processing model and a classification model are combined, the process includes obtaining a first projection matrix that is obtained in an n-th iteration of the machine learning processing and that indicates a correspondence between input data input from the natural language processing model to the classification model and output data output from the classification model, updating a parameter of the natural language processing model, updating a parameter of the classification model by using the first projection matrix, and obtaining, in an n+1-th iteration of the machine learning processing, a second projection matrix that indicates a correspondence between input data input from the updated natural language processing model to the updated classification model and output data output from the updated classification model, wherein the n is a natural number.

Abstract: A computer divides a character string included in text data into a plurality of tokens. The computer searches, by performing matching processing between a token string indicating a specific number of consecutive tokens among the plurality of tokens and dictionary information including a plurality of named entities, the plurality of named entities for a similar named entity whose similarity to the token string is equal to or more than a threshold. The computer converts matching information indicating a result of the matching processing between the token string and the similar named entity into first vector data. The computer generates input data by using a plurality of pieces of vector data converted from the plurality of tokens and the first vector data. The computer generates a named entity recognition model that detects a named entity by performing machine learning using the input data.

Abstract: A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process includes identifying first named entities and first verbs in a dependent relationship respectively with the first named entities from each of a plurality of sentences, vectorizing each of the plurality of sentences based on the first named entities and the first verbs, identifying, out of the plurality of sentences, one or more sentences each having a similarity greater than or equal to a threshold with respect to a specific sentence based on a plurality of vectors generated by the vectorizing, the similarity between two sentences being defined as a similarity between vectors generated by vectorizing the two sentences, and executing training of a machine learning model based on the one or more sentences.

Abstract: A non-transitory computer-readable storage medium storing a vectorization program that causes at least one computer to execute a process, the process includes: receiving a document; and based on information in which words and vectors are associated with each other, when a certain word that is not included in the information is detected from the document, generating a vector corresponding to the certain word by inputting a vector corresponding to each of letters included in the certain word into a machine learning model generated by machine learning based on a first vector associated with a first word included in the information and a vector corresponding to each of letters included in the first word.

Abstract: A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process, the process including updating a parameter of a machine learning model generated by a first machine learning using a plurality of pieces of first training data, by an initial execution of a second machine learning using second training data satisfying a specific condition on the machine learning model, and repeating the second machine learning to update the parameter of the machine learning model, while reducing a degree of influence of the second training data on update of the parameter as a difference between a first value of the parameter before the initial execution of the second machine learning and a second value of the parameter updated by a previous second machine learning increases.

Abstract: A sentence is vectorized and encoded for further being processed by a computer. The encoding process includes, identifying a common ancestor node of a first node corresponding to a first segment in a sentence and a second node corresponding to a second segment in the sentence, the first node and the second node being included in a dependency tree generated based on the sentence, acquiring a vector of the common ancestor node by encoding each node included in the dependency tree in accordance with a path from each of leaf nodes included in the dependency tree to the common ancestor node, and encoding, based on the vector of the common ancestor node, each of nodes included in the dependency tree in accordance with the path from the common ancestor node to the leaf nodes.

Abstract: A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process including: inputting a first vector value and a second vector value to a first learning model to obtain a first output result, the first vector value corresponding to a subject of text data in which a first relationship between the subject and an object is missing, the second vector value corresponding to mask data generated from the text data by masking the subject and the object; inputting a third vector value and the first vector value to a second learning model to obtain a second output result, the third vector value corresponding to a second relationship to be compensated for the text data; and determining, by using the object, the first output result, and the second output result, whether it is possible for the second relationship to compensate for the text data.

Abstract: An inner drum type image recording device is provided which, in exposure of a photosensitive lithographic printing plate, eliminates unnecessary light (reflected scattered light) other than light for main exposure, and eliminates fluctuations in a dot percent. By limiting baffles to a minimum number of positions necessary, fluctuations in the dot percent caused by reflected scattered light are suppressed, and a sufficient space needed for maintenance work on a spinner mirror and the like can be ensured. Further, by making the baffle fan-shaped and disposing the baffle at a periphery of the spinner mirror, light of a subscanning component of reflected scattered light can be reliably blocked.

Abstract: An inner drum type image recording device is provided which, in exposure of a photosensitive lithographic printing plate, eliminates unnecessary light (reflected scattered light) other than light for main exposure, and eliminates fluctuations in a dot percent. By limiting baffles to a minimum number of positions necessary, fluctuations in the dot percent caused by reflected scattered light are suppressed, and a sufficient space needed for maintenance work on a spinner mirror and the like can be ensured. Further, by making the baffle fan-shaped and disposing the baffle at a periphery of the spinner mirror, light of a subscanning component of reflected scattered light can be reliably blocked.