Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Among statistical approaches to Chinese word segmentation, the  word-based n-gram  ( generative ) model and the  character-based tagging  ( discriminative ) model are two dominant approaches in the literature. The former gives excellent performance for the  in-vocabulary  (IV) words; hover, it handles  out-of-vocabulary  (OOV) words poorly. On the other hand, though the latter is more robust for OOV words, it fails to deliver satisfactory performance for IV words. These two approaches behave differently due to the unit they use (word vs. character) and the model form they adopt (generative vs. discriminative). In general, character-based approaches are more robust than word-based ones, as the vocabulary of characters is a closed set; and discriminative models are more robust than generative ones, since they can flexibly include all kinds of available information, such as future context.   This article first proposes a character-based  n -gram model to enhance the robustness of the generative approach. Then the proposed generative model is further integrated with the character-based discriminative model to take advantage of both approaches. Our experiments show that this integrated approach outperforms all the existing approaches reported in the literature. Afterwards, a complete and detailed error analysis is conducted. Since a significant portion of the critical errors is related to numerical/foreign strings, character-type information is then incorporated into the model to further improve its performance. Last, the proposed integrated approach is tested on cross-domain corpora, and a semi-supervised domain adaptation algorithm is proposed and shown to be effective in our experiments.
['Kun Wang', 'Chengqing Zong', 'Keh-Yih Su']
I am writing a research paper on a idea Integrating Generative and Discriminative Character-Based Models for Chinese Word Segmentation
[{"from": "human", "value": "I am writing a research paper on a idea Integrating Generative and Discriminative Character-Based Models for Chinese Word Segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Among statistical approaches to Chinese word segmentation, the  word-based n-gram  ( generative ) model and the  character-based tagging  ( discriminative ) model are two dominant approaches in the literature. The former gives excellent performance for the  in-vocabulary  (IV) words; hover, it handles  out-of-vocabulary  (OOV) words poorly. On the other hand, though the latter is more robust for OOV words, it fails to deliver satisfactory performance for IV words. These two approaches behave differently due to the unit they use (word vs. character) and the model form they adopt (generative vs. discriminative). In general, character-based approaches are more robust than word-based ones, as the vocabulary of characters is a closed set; and discriminative models are more robust than generative ones, since they can flexibly include all kinds of available information, such as future context.   This article first proposes a character-based  n -gram model to enhance the robustness of the generative approach. Then the proposed generative model is further integrated with the character-based discriminative model to take advantage of both approaches. Our experiments show that this integrated approach outperforms all the existing approaches reported in the literature. Afterwards, a complete and detailed error analysis is conducted. Since a significant portion of the critical errors is related to numerical/foreign strings, character-type information is then incorporated into the model to further improve its performance. Last, the proposed integrated approach is tested on cross-domain corpora, and a semi-supervised domain adaptation algorithm is proposed and shown to be effective in our experiments."}]