Etiqueter un corpus oral par acquisition automatique à l'aide de knaissances linguistiques
Etiqueter un corpus oral par apprentissage automatique à l'aide de connaissances linguistiques
摘要：
由于Eslo1（“Enquête sociolinguistique d'Orléans”，即“奥尔良社会语言学调查”）活动，大量的口语语料库已经收集并以文本格式转录。这里提出的工作的目的是关联一个形态句法标签，这个语料库的每个单元。为此，我们首先研究了必要标签的特异性，以及它们的各种可能的描述水平。这项研究导致了一个新的原始层次结构的标签。然后，考虑到我们的新标签集与每个可用软件中使用的标签集不同，并且这些软件通常不适合口头数据，我们通过机器学习方法构建了一个新的标签工具，使用Cordial标记的数据并手动校正。我们应用了线性CRF（条件随机场），试图最大限度地利用用于定义标签集的语言知识。我们获得的准确度在85%和90%之间，这取决于所使用的参数。
英文摘要：
Thanks to the Eslo1 ("Enquête sociolinguistique d'Orléans", i.e. "Sociolinguistic Inquiery of Orléans") campain, a large oral corpus has been gathered and transcribed in a textual format. The purpose of the work presented here is to associate a morpho-syntactic label to each unit of this corpus. To this aim, we have first studied the specificities of the necessary labels, and their various possible levels of description. This study has led to a new original hierarchical structuration of labels. Then, considering that our new set of labels was different from the one used in every available software, and that these softwares usually do not fit for oral data, we have built a new labeling tool by a Machine Learning approach, from data labeled by Cordial and corrected by hand. We have applied linear CRF (Conditional Random Fields) trying to take the best possible advantage of the linguistic knowledge that was used to define the set of labels. We obtain an accuracy between 85 and 90%, depending of the parameters used.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
