Abstract:
Methods are disclosed for assembling a plurality of double-stranded DNA fragments into DNA molecules in a single in vitro recombination reaction comprising contacting the plurality of double-stranded DNA fragments with a bacterial extract derived from a RecA deficient bacterial strain so as to assemble the plurality of DNA fragments into DNA molecules.
Abstract:
Disclosed are a table recognition method and apparatus. The table recognition method includes steps of obtaining an image vision feature and a character content feature of a table image; fusing the image vision feature and the character content feature of the table image to acquire a first fusion feature, and carrying out recognition based on the first fusion feature to acquire a table structure; and performing, based on the table structure, character recognition on the table image to acquire table character contents.
Abstract:
A method, an apparatus and an electronic device for performing entity linking, and a non-transitory computer-readable recording medium are provided. The method includes constructing training data including a plurality of sets of labeled data using an existing unambiguous entity database where unambiguous entities corresponding to respective entity words are stored, each set of the labeled data including a text having an entity word and an unambiguous entity linked with the entity word; training an unambiguous entity recognition model whose output is a matching probability between an entity word in a text and an unambiguous entity using the training data; and inputting a text having an entity word to be recognized into the unambiguous entity recognition model, and determining an unambiguous entity linked with the entity word to be recognized based on an output result of the unambiguous entity recognition model.
Abstract:
Knowledge graph processing method and device are disclosed. The method includes steps of obtaining an entity set containing a first entity, a second entity, and relation information; acquiring text information and image information related to the first entity and the second entity; generating a first structural information vector of the first entity and a second structural information vector of the second entity, and creating a first text information vector of the first entity, a first image information vector of the first entity, a second text information vector of the second entity, and a second image information vector of the second entity; and building a joint loss function so as to attain a first target vector of the first entity, a second target vector of the second entity, and a target relation vector of the relation information between the first entity and the second entity.
Abstract:
Methods are disclosed for assembling a plurality of double-stranded DNA fragments into DNA molecules in a single in vitro recombination reaction comprising contacting the plurality of double-stranded DNA fragments with a bacterial extract derived from a RecA deficient bacterial strain so as to assemble the plurality of DNA fragments into DNA molecules.
Abstract:
An optical data storage system utilizes optical fibers for transfer of information to and from storage media. The storage media comprises magneto-optical storage disks. The optical fibers are low-birefringence optical fibers. As compared with conventional approaches, a polarization state conveyed by the optical fiber is accurately reproduced with reduced noise. Various noise reduction techniques are provided by substantially decreasing or eliminating spurious reflections (or the effects thereof) at end faces and of an optical fiber. In particular, various techniques, such as index matching, a cover slip method, laser modulation, or angle polishing, may be used to eliminate spurious reflections (or the effects thereof) at the front end face of the optical fiber. Various techniques, such as angle cleaving, index matching, or multi-mode fiber splicing, may be used to eliminate spurious reflections (or the effects thereof) at the back end face of the optical fiber.
Abstract:
A method and an apparatus are provided for training a named entity recognition (NER) model. By constructing tag annotations for tags and causing the tag annotations to contain information for indicating the positions of tokens in named entities, corresponding to the tags, respectively, in the process of training the NER model, the NER model can better understand the different positions of different tokens in the same named entity, so that the trained NER model can more accurately recognize named entities.
Abstract:
A method and an apparatus for sequence labeling on an entity text, and a non-transitory computer-readable recording medium are provided. In the method, a start position of an entity text within a target text is determined. Then, a first matrix is generated based on the start position of the entity text. Elements in the first matrix indicates focusable weights of each word with respect to other words in the target text. Then, a named entity recognition model is generated using the first matrix. The named entity recognition model is obtained by training using first training data, the first training data includes word embeddings corresponding to respective texts in a training text set, and the texts are texts whose entity label has been labeled. Then, the target text is input to the named entity recognition model, and probability distribution of the entity label is output.
Abstract:
A method and an apparatus for named entity recognition, and a non-transitory computer-readable recording medium are provided. In the method, text elements are traversed according to a text span to obtain candidate entity words. Then, a class to which the candidate entity word belongs is recognized. The recognizing of the class includes generating a prompt template corresponding to the candidate entity word, and concatenating the text to be recognized and the prompt template to obtain a concatenated text; generating vector representations of the text elements in the concatenated text; generating the vector representation of the candidate entity word according to the vector representations of the text elements of each candidate entity word in the concatenated text, and the vector representation of the text element of the mask word; and classifying the vector representation of the candidate entity word to obtain the class of the candidate entity word.
Abstract:
A method, an apparatus and an electronic device for performing entity linking, and a non-transitory computer-readable recording medium are provided. The method includes constructing training data including a plurality of sets of labeled data using an existing unambiguous entity database where unambiguous entities corresponding to respective entity words are stored, each set of the labeled data including a text having an entity word and an unambiguous entity linked with the entity word; training an unambiguous entity recognition model whose output is a matching probability between an entity word in a text and an unambiguous entity using the training data; and inputting a text having an entity word to be recognized into the unambiguous entity recognition model, and determining an unambiguous entity linked with the entity word to be recognized based on an output result of the unambiguous entity recognition model.