在社交媒体上使用多任务神经网络对噪声进行建模以识别命名实体
Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media
摘要：
识别文档中的命名实体是许多NLP应用程序中的关键任务。虽然目前最先进的方法，这个任务达到了高性能的干净的文本（如新闻类），这些算法显着降低，当它们被移动到嘈杂的环境，如社交媒体领域。我们提出了两个系统，解决了使用字符级语音和音位，词嵌入，和词性标签作为功能的社会媒体数据处理的挑战。第一个模型是多任务端到端双向长短期记忆（BLSTM）-条件随机场（CRF）网络，其输出层包含两个CRF分类器。第二个模型使用多任务BLSTM网络作为特征提取器，将学习转移到CRF分类器进行最终预测。我们的系统在2017年嘈杂用户生成文本研讨会数据集上的表现优于当前最先进的F1分数2.45%和3.69%，为社交媒体环境建立了更合适的方法。
英文摘要：
Recognizing named entities in a document is a key task in many NLP applications. Although current state-of-the-art approaches to this task reach a high performance on clean text (e.g. newswire genres), those algorithms dramatically degrade when they are moved to noisy environments such as social media domains. We present two systems that address the challenges of processing social media data using character-level phonetics and phonology, word embeddings, and Part-of-Speech tags as features. The first model is a multitask end-to-end Bidirectional Long Short-Term Memory (BLSTM)-Conditional Random Field (CRF) network whose output layer contains two CRF classifiers. The second model uses a multitask BLSTM network as feature extractor that transfers the learning to a CRF classifier for the final prediction. Our systems outperform the current F1 scores of the state of the art on the Workshop on Noisy User-generated Text 2017 dataset by 2.45% and 3.69%, establishing a more suitable approach for social media environments.
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。
