NCUEE - NLP at S em E val-2022 Task 11: C hinese Named Entity Recognition Using the BERT - B i LSTM - CRF Model (2022)

Abstract: This study describes the model design of the NCUEE-NLP system for the Chinese track of the SemEval-2022 MultiCoNER task. We use the BERT embedding for character representation and train the BiLSTM-CRF model to recognize complex named entities. A total of 21 teams participated in this track, with each team allowed a maximum of six submissions. Our best submission, with a macro-averaging F1-score of 0.7418, ranked the seventh position out of 21 teams.

## introduction named entity recognition (ner) is a fundamental task in information extraction that locates the mentions of named entities and classifies them (e.g., person, organization and location) in unstructured texts. the ner is a traditional nlp task that has been solved as a sequence labeling problem, where entity boundaries and category labels are jointly predicted. it is difficult to recognize complex named entities like the titles of creative works (e.g., books, songs, movies) that can take the form of any linguistic constituent @xcite . syntactic and semantic ambiguity makes it challenging to recognize such complex named entities based on their context. the semeval-2022 task 11 (multiconer) organized a challenge to develop multilingual complex ner system for 11 human languages @xcite . this task focuses on detecting semantically ambiguous and complex entities in short and low-context settings. the languages include: english, spanish, dutch, russian, turkish, korean, farsi, german, chinese, hindi, and bangla. the named entity categories are person (labeled as per), location (loc), group (grp), corporation (corp), product (prod), and creative work (cw). the task evaluation framework is divided in three broad tracks. 1) multi-lingual (track 1): participants train a single multi-lingual ner model for all the languages; 2) mono-lingual (track 2-12): participants train a model that works for only one language; 3) code-mixed (track 13): testing samples include tokens from any of the 11 mentioned languages in the shared task. we only participated the track 9 for chinese language. chinese ner is more difficult to process than english ner. chinese language is logographic and provides no conventional features like capitalization. in addition, due to a lack of delimiters between characters, chinese ner is correlated with word segmentation tasks, and named entity boundaries are also word boundaries. however, incorrectly segmented entity boundaries will cause error propagation in ner. for example, in a short context "這首歌出現在華特迪士尼動畫動物方城 市中" (this song appeared in the walt disney animation zootopia), a creative work "動物方城市" (zootopia) may be incorrectly segmented into three words: "動物" (animal), "方"(square), and "城市" (city). hence, it has been shown that characterbased approaches outperform word-based methods for chinese ner @xcite @xcite . recently, deep learning techniques have been widely used for chinese ner, mostly with promising results. a character-based lstm (long short-term memory)-crf (conditional random field) model with radical-level features was proposed for chinese ner @xcite . the me-cner model exploited multiple embeddings-based character representation to improve chinese ner performance @xcite . a joint training objective technique for different types of neural embeddings was adopted for chinese ner in social media, based on weibo messages @xcite . the bilstm (bidirectional lstm)-crf model was trained based on character-word mixed embeddings to improve the recognition effectiveness of chinese ner @xcite . a bilstm-crf model with a self-attention mechanism was proposed to integrate part-of-speech labeling information to capture the semantic features of input sequences for chinese clinical ner @xcite . a residual dilated cnn (convolution neural network) with crf was also presented to enhance chinese clinical ner in terms of computational performance and training time @xcite ). an me-mgnn (multiple embeddings enhanced multi-graph neural network) model was proposed to derive a character representation based on multiple embeddings at different granularities from the radical, character to word levels. multiple gated graph sequence neural networks, along with standard bilstm-crf, were then used to recognize chinese named entities in the healthcare domain @xcite . this paper describes the ncuee-nlp (national central university, dept. of electrical engineering, natural language processing lab) system for the chinese track of semeval-2022 task 11. we find that the neural computing approaches based on the bilstm-crf achieved impressive results for chinese ner. hence, we follow the investigated results to develop character-based bilstm-crf models. ## the ncuee-nlp system figure 1 shows our ncuee-nlp system architecture for the chinese ner. our bert-bilstm-crf model is composed of three main parts: 1) bert embeddings, 2) bidirectional lstm networks, and 3) crf sequence labeling. ## conclusion this study describes the ncuee-nlp system in the chinese track of semeval-2022 multiconer task, including system design, implementation and evaluation. we used the bert embedding to represent each character in the original sentences and trained bilstm-crf using datasets provided by the organizers to predict the named entity categories. our best submission had a marco-averaging f1-score of 0.7418, ranking in the 7th position among a total of 21 participating teams.