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2 results about "Language technology" patented technology

Language technology, often called human language technology (HLT), studies methods of how computer programs or electronic devices can analyze, produce, modify or respond to human texts and speech. It consists of natural language processing (NLP) and computational linguistics (CL) on the one hand, and speech technology on the other. It also includes many application oriented aspects of these. Working with language technology often requires broad knowledge not only about linguistics but also about computer science.

A dialogue processing method, device and computer readable storage medium

This invention provides a dialogue processing method, apparatus, and computer-readable storage medium, belonging to the field of natural language processing technology. The dialogue processing method includes acquiring multiple sets of dialogue streams; determining at least one topic boundary based on the semantics of the multiple sets of dialogue streams; segmenting all dialogue streams into at least two topic segments based on each topic boundary; assigning dynamic weights to memory items in each topic segment, wherein each memory item stores at least one piece of memory information; the dynamic weights are determined at least based on the timeliness of the memory item, its relevance to the current query, and its coherence with the current topic; and, in response to the current dialogue, acquiring multiple candidate memory items from all topic segments, and, based on the dynamic weight of each candidate memory item, filtering and outputting a set of memory items that meets a target length from the multiple candidate memory items.
Owner:LENOVO (BEIJING) LTD

A semantically anchored guided approximate contrastive learning method and system

PendingCN122309794ASemantic alignmentAlgorithm
This invention discloses a semantically anchored, approximate contrastive learning method and system, relating to the fields of vision and language technology. It involves acquiring image and text data; extracting image embedding vectors and text embedding vectors using an image encoder and a text encoder, respectively; updating dynamic image and text queues through cross-modal momentum contrast and constructing a fused multimodal representation, which serves as a shared semantic anchor; progressively calibrating the image and text embedding vectors towards the shared semantic anchor within the contrastive learning framework to generate calibrated image and text embedding vectors; and performing image-text retrieval based on the calibrated image and text embedding vectors. This invention relaxes the strict requirement for complete semantic alignment of positive sample pairs, effectively mitigating cross-modal semantic bias.
Owner:SHENYANG AEROSPACE UNIVERSITY