Semiconductor Energy Laboratory Co., Ltd.

Kazuki Higashi

Junpei Momo

Kunitaka Yamamoto

Takahiro Fukutome

Motoki Nakashima

A highly accurate document search, particularly a search for a document relating to intellectual property, is achieved with an easy input method. A document search system includes a processing portion. The processing portion has a function of extracting a keyword included in text data, a function of extracting a related term of the keyword from words included in a plurality of pieces of first reference text analysis data, a function of giving a weight to each of the keyword and the related term, a function of giving a score to each of a plurality of pieces of second reference text analysis data on the basis of the weight, a function of ranking the plurality of pieces of second reference text analysis data on the basis of the score to generate ranking data, and a function of outputting the ranking data.

Document search system, document search method, program, and non-transitory computer readable storage medium

A novel content classification method is provided. A content classification method using machine learning for a learning model and a classifier fabrication method are provided. In Step 1, a data set containing a plurality of contents is acquired. Learning labels are attached to m contents, and the learning labels are not attached to the remaining contents. In Step 2, a first learning model is created by machine learning using the m contents. In Step 3, judgment labels are attached to the plurality of contents using the first learning model and are displayed on a GUI. In Step 4, new learning labels are attached to k contents in the plurality of contents. In Step 5, a second learning model is created by the machine learning using the k contents. In Step 6, judgment labels are attached to the plurality of contents using the second learning model and are displayed on the GUI.

Content classification method

A document search system that enables efficient document search regardless of the ability of a user is achieved. Document search is performed using a document search system in which database document data is stored. After first document data and second document data are input to the document search system, the document search system extracts a plurality of terms from the first document data. The extraction of the terms is performed using morphological analysis, for example. Next, the extracted terms are weighted on the basis of the second document data. For example, texts included in a document represented by the second document data are classified into first and second texts. Among the terms extracted from the first document data, the weight of the term included in the first text is set larger than the weights of the other terms. The classification of the texts can be performed in accordance with a rule basis or using machine learning. After that, the similarity of the database document data to the first document data is calculated on the basis of the weighted term.

Document search system and document search method

Highly accurate document search, especially intellectual property-related document search, is achieved with a simple input method. A processing portion has a function of generating text analysis data from text data input to an input portion; a function of extracting a search word from words included in the text analysis data; and a function of generating first search data from the search word on the basis of weight dictionary data and thesaurus data. A memory portion stores second search data generated when the first search data is modified by a user. The processing portion updates the thesaurus data in accordance with the second search data.

A document retrieval system retrieving a document with the concept of the document taken into account is provided. The system includes a processing portion and the processing portion creates a retrieval graph from a retrieval composition. The retrieval graph includes first to m-th retrieval local graphs (m is an integer of greater than or equal to 1), and the retrieval local graphs are each constituted by two nodes and one edge. The processing portion performs retrieval of first to m-th sentences on a reference document. The i-th sentence (i is an integer of greater than or equal to 1 and less than or equal to m) includes one of the two nodes in the i-th retrieval local graph or a related term or a hyponym of the one of the two nodes; the other of the two nodes in the i-th retrieval local graph or a related term or a hyponym of the other of the two nodes; and the edge in the i-th retrieval local graph or a related term or a hyponym of the edge. A mark is assigned to the score of the reference document in accordance with the number of sentences included in the reference document among the first to m-th sentences.

Growing community of inventors

Atsugi, Japan

Kazuki Higashi