Wikipedia serves as the world's largest and most popular online reference encyclopedia, rich in structured knowledge and authoritative citations. Recently, numerous works have leveraged large language models to automatically generate Wikipedia-like articles. However, existing approaches primarily focus on producing singular narrative-type content, overlooking higher information-density structured elements such as timeline and table. To address these limitations, we propose WikiMAG, a multi-agent guided framework for generating structured Wikipedia-like articles. This framework employs a collaborative multi-agent mechanism to orchestrate the creation process, featuring three synergistic core components: Progressive planner first constructs the coarse-grained outline framework and then annotate fine-grained types for outline units, encompassing narrative, timeline, and table formats; Reflective inspector dynamically curates high-quality references via multi-round interactive feedback, thereby enhancing the authority and relevance of citations; Versatile writer integrates fine-grained outline details and high-quality reference information to generate information-rich articles, incorporating the three annotated formats. We evaluate WikiMAG on two public datasets, FreshWiki and WikiGenBen, across outline, writing, and verifiability dimensions. Compared with the best baseline method, our method achieves an average improvement of 6.73 points and 4.39 points in Heading Soft Recall and the METEOR metric (a machine translation and text generation evaluation metric) respectively, and an average increase of 16.84 percentage points in Citation Rate.

The Flat-LAttice Transformer (FLAT) has achieved notable success in Chinese named entity recognition (NER) by integrating lexical information into the widely-used Transformer encoder. FLAT enhances each sentence by constructing a flat lattice, a token sequence with characters and matched lexicon words, and calculating self-attention among tokens. However, FLAT faces a quadruple complexity challenge, especially with lengthy sentences containing numerous matched words, significantly increasing memory and computational costs. To alleviate this issue, we propose a novel lightweight lexicon-enhanced Transformer (LLET) for Chinese NER. Specifically, we introduce two distinct variants that focus on character attention to characters and words, both jointly and separately. Experimental results conducted on four public Chinese NER datasets show that both variants achieve significant memory savings while maintaining comparable performance when compared to FLAT.

Named Entity Recognition (NER) is a fundamental task in natural language processing. Syntax plays a significant role in helping to recognize the boundaries and types of entities. In comparison to English, Chinese NER, due to the absence of explicit delimiters, often faces challenges in determining entity boundaries. Similarly, syntactic parsing results can also lead to errors caused by wrong segmentation. In this paper, we propose the dual-grained syntax-aware Transformer network to mitigate the noise from single-grained syntactic parsing results by incorporating dual-grained syntactic information. Specifically, we first introduce syntax-aware Transformers to model dual-grained syntax-aware features and a contextual Transformer to model contextual features. We then design a triple feature aggregation module to dynamically fuse these features. We validate the effectiveness of our approach on three public datasets.

Integrating lexical knowledge in Chinese named entity recognition (NER) has been proven effective. Among the existing methods, Flat-LAttice Transformer (FLAT) has achieved great success in both performance and efficiency. FLAT performs lexical enhancement for each sentence by constructing a flat lattice (i.e., a sequence of tokens including the characters in a sentence and the matched words in a lexicon) and calculating self-attention with a fully-connected structure. However, the different interactions between tokens, which can bring different aspects of semantic information for Chinese NER, cannot be well captured by self-attention with a fully-connected structure. In this paper, we propose a novel Multi-View Transformer (MVT) to effectively capture the different interactions between tokens. We first define four views to capture four different token interaction structures. We then construct a view-aware visible matrix for each view according to the corresponding structure and introduce a view-aware dot-product attention for each view to limit the attention scope by incorporating the corresponding visible matrix. Finally, we design three different MVT variants to fuse the multi-view features at different levels of the Transformer architecture. Experimental results conducted on four public Chinese NER datasets show the effectiveness of the proposed method. Specifically, on the most challenging dataset Weibo, which is in an informal text style, MVT outperforms FLAT in F1 score by 2.56%, and when combined with BERT, MVT outperforms FLAT in F1 score by 3.03%.

Analyzing character sentiment information through audio is an essential and challenging task. Using convolutional and recurrent neural network approaches for audio sentiment analysis has achieved initial results, proving that deep learning methods can be helpful for sentiment analysis of audio. With the rise of multimodal research, using audio and text feature fusion for sentiment analysis has better results. However, this approach uses the pipeline approach for analysis, which needs to use an additional speech-to-text model to get text information first so that the error of text information conversion will affect the subsequent judgment. In order to solve this problem, we propose a new end-to-end model, which adopts the model architecture of seq2seq, encodes audio information by the encoder, generates text information of audio by the decoder and understands the textual content, and finally obtains the final emotional state by fusing the features extracted by the decoder and the encoder. Our model has experimented on actual psychological assistance hotline data, and the results show that our method is significantly better than the baseline method, which is a meaningful method.

Recently, lexicon-based Chinese Named Entity Recognition (NER) models have achieved state-of-the-art performance by benefiting from the rich boundary and semantic information contained in the lexicon. However, in the Chinese medical domain, it’s difficult to obtain the medical lexicon related to the target medical corpus. In this paper, we propose a new paradigm, enhancing Chinese medical NER with Auto-mined Lexicon (ALNER), which alleviates the difficulty of obtaining the medical lexicon by designing a data-driven automatic lexicon construction method. We define medical lexicon construction as a high-quality phrase mining task. We perform secondary annotation on the NER annotated data and use the secondary annotated data to train a deep learning-based phrase tagger. Experimental results show that our method can be combined with different lexicon-based Chinese NER models to improve performance and that the method does not require an external medical lexicon.

The integration of lexicon information into character-based models is a hot topic in Chinese Named Entity Recognition(NER) research. Most methods only utilize information from a single lexicon which is usually a general lexicon. However, In the Chinese medical text scenario, due to the large amount of medical terminology, a single lexicon, especially a general lexicon, offers little performance improvement to the Chinese NER. In this paper, we propose a Multi-source Lexicon Information Fusion method for Named Entity Recognition in Chinese Medical Text(MLNER) which can utilize information from both general and medical lexicons. Considering the small medical annotated corpus, we combine the model with the pre-trained model to improve the performance of the model on small datasets by exploiting the rich representation capability of the pre-trained model. Experiments show that our method can effectively improve the performance of NER in Chinese medical text. Our model is also applicable to Chinese NER tasks in other domain specific fields, with good scalability and application value.