6 results about "Grammatical tense" patented technology

The invention discloses a homework correction method based on image recognition, relates to the technical field of homework correction, and comprises the following steps: breaking through the limitation of the existing homework correction technology on surface processing of consecutive characters, and bringing consecutive deformation into an expression intention analysis framework through linkage of stroke topological characteristics and semantic compensation; metaphor words intensified by students deliberately, such as a s with lengthened strokes, can be recognized, and the rhetorical structure is prevented from being misjudged as word use errors; the method comprises the following steps of: forcibly merging and eliminating the segmentation of physical paging on a long rhetorical sentence through a cross-row syntax tree, and ensuring that a not only... but also type emphasis sentence is completely analyzed; a dynamic visualization mechanism is adopted to convert an abstract grammar rule into a perceptible scene: a time axis animation of a tense error visually presents dislocation of an action and a time node, and a depth rhetorical analysis triggered by a hand-drawn wavy line accurately marks a metaphor ontology-metaphor body mapping relationship, so that students can understand the essence of the error at a glance.

A device identifies, in multi-context text data, unrelated text and coreference text, and extracts coreference clusters, coreference sentences, and coreference sentiments based on the coreference text. The device extracts unrelated sentences from the unrelated text, and assigns tenses to the coreference sentences and the unrelated sentences. The device extracts phrases and entities from the coreference sentences and unrelated sentences, and assigns tense flags that exclude present tense sentences. The device selects past tense phrases and future tense phrases, and combines the past tense phrases and the future tense phrases to generate phrases. The device identifies invalid phrases in the phrases, and identifies similarities between the coreference sentences and the invalid phrases. The device processes the coreference text, the coreference tenses, the coreference sentiments, and the similarities, with a reinforcement learning model, to generate final context text.

The present invention discloses a homework grading method based on image recognition, which relates to the technical field of homework grading. The present invention comprises the following steps: breaking through the surface processing limitations of existing homework grading technologies on cursive characters, integrating cursive deformation into an expression intention analysis framework through the linkage of stroke topological features and semantic compensation, and being able to identify metaphors deliberately emphasized by students, such as "as" with elongated strokes, to avoid misjudging rhetorical structures as word errors; eliminating the fragmentation of long rhetorical sentences by physical paging through forced merging of cross-line syntax trees, ensuring complete parsing of emphasized sentences such as "not only...but also"; and converting abstract grammatical rules into perceptible scenarios through a dynamic visualization mechanism: a timeline animation of tense errors intuitively presents the misalignment of actions and time nodes, and in-depth rhetorical analysis triggered by hand-drawn wavy lines accurately marks the metaphor subject-metaphor mapping relationship, enabling students to understand the essence of the error at a glance.

The embodiment of the invention discloses a document-level time sequence relation extraction method, which is based on event time argument feature fusion, utilizes a time argument theory in linguistics to guide a model extraction effect, and fuses tenses, postures and time adverbs of sentences as additional information into word embedding expression of event trigger words, so that the time sequence relation extraction efficiency is improved. The method comprises the following steps: capturing a long-distance inter-sentence relationship by using utterance features generated by a rhetorical structure theory analyzer, capturing a dependency relationship between a document structure and words through syntactic analysis, capturing time features by using time parameters in semantic role annotation, and learning and transmitting the features by a convolutional network based on a gated graph. By adopting the technical scheme of the invention, an effect better than that of a baseline is obtained on an event time sequence relation extraction task.