客户服务Chatbots是对话系统,旨在为客户提供有关不同公司提供的产品/服务的信息。特别地,意图识别是自然语言低估Chatbot系统的能力的核心组件之一。在聊天训练识别的不同意图中,他们有一组是通用的任何客户服务Chatbot。普遍意图可以包括称呼,将对话交给人类代理人,告别。识别这些普遍意图的系统将非常有助于优化特定客户服务聊天训练过程。我们提出了一个普遍意图识别系统的发展,该系统受过培训,以识别28个不同的聊天跳闸中常见的11个意图组。拟议的系统考虑了最先进的单词嵌入模型,例如Word2VEC和BERT,基于卷积和经常性神经网络的深层分类器。所提出的模型能够区分这些普遍意图,均衡精度高达80.4 \%。此外,所提出的系统同样准确地识别短期和长文本请求中表达的意图。同时,错误分类错误通常发生在具有非常相似的语义领域,例如告别和正面评论之间。建议的系统将非常有帮助优化客户服务Chatbot的培训过程,因为我们的系统已经可用并检测到一些意图。与此同时,拟议的方法将是一个合适的基础模型,通过应用转移学习策略培训更具体的聊天措施。
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Named Entity Recognition and Intent Classification are among the most important subfields of the field of Natural Language Processing. Recent research has lead to the development of faster, more sophisticated and efficient models to tackle the problems posed by those two tasks. In this work we explore the effectiveness of two separate families of Deep Learning networks for those tasks: Bidirectional Long Short-Term networks and Transformer-based networks. The models were trained and tested on the ATIS benchmark dataset for both English and Greek languages. The purpose of this paper is to present a comparative study of the two groups of networks for both languages and showcase the results of our experiments. The models, being the current state-of-the-art, yielded impressive results and achieved high performance.
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在网络和社交媒体上生成的大量数据增加了检测在线仇恨言论的需求。检测仇恨言论将减少它们对他人的负面影响和影响。在自然语言处理(NLP)域中的许多努力旨在宣传仇恨言论或检测特定的仇恨言论,如宗教,种族,性别或性取向。讨厌的社区倾向于使用缩写,故意拼写错误和他们的沟通中的编码词来逃避检测,增加了讨厌语音检测任务的更多挑战。因此,词表示将在检测仇恨言论中发挥越来越关的作用。本文研究了利用基于双向LSTM的深度模型中嵌入的域特定词语的可行性,以自动检测/分类仇恨语音。此外,我们调查转移学习语言模型(BERT)对仇恨语音问题作为二进制分类任务。实验表明,与双向LSTM基于LSTM的深层模型嵌入的域特异性词嵌入了93%的F1分数,而BERT在可用仇恨语音数据集中的组合平衡数据集上达到了高达96%的F1分数。
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Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, these systems have used Machine Learning (ML) and Natural Language Processing (NLP) to analyze large volumes of customer feedback and engagement data. The goal is to understand customers in context and provide meaningful answers across various channels. Despite multiple advances in Conversational Artificial Intelligence (AI) and Recommender Systems (RS), it is still challenging to understand the intent behind customer questions during the customer journey. To address this challenge, in this paper, we study and analyze the recent work in Conversational Recommender Systems (CRS) in general and, more specifically, in chatbot-based CRS. We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition. Since performance evaluation is achieved based on different ML models, we use transformer base models to evaluate the proposed approach using a labelled dialogue dataset (MSDialogue) of question-answering interactions between information seekers and answer providers.
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The rapid advancement of AI technology has made text generation tools like GPT-3 and ChatGPT increasingly accessible, scalable, and effective. This can pose serious threat to the credibility of various forms of media if these technologies are used for plagiarism, including scientific literature and news sources. Despite the development of automated methods for paraphrase identification, detecting this type of plagiarism remains a challenge due to the disparate nature of the datasets on which these methods are trained. In this study, we review traditional and current approaches to paraphrase identification and propose a refined typology of paraphrases. We also investigate how this typology is represented in popular datasets and how under-representation of certain types of paraphrases impacts detection capabilities. Finally, we outline new directions for future research and datasets in the pursuit of more effective paraphrase detection using AI.
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通过为患者启用远程医疗服务,远程医疗有助于促进医疗专业人员的机会。随着必要的技术基础设施的出现,这些服务已逐渐流行。自从Covid-19危机开始以来,远程医疗的好处就变得更加明显,因为人们在大流行期间倾向于亲自探望医生。在本文中,我们专注于促进医生和患者之间的聊天课程。我们注意到,随着对远程医疗服务的需求的增加,聊天体验的质量和效率可能至关重要。因此,我们为医学对话开发了一种智能的自动反应生成机制,该机制可帮助医生有效地对咨询请求做出反应,尤其是在繁忙的课程中。我们探索了9个月内收集的医生和患者之间的900,000多个匿名的历史在线信息。我们实施聚类算法,以确定医生最常见的响应,并相应地手动标记数据。然后,我们使用此预处理数据来训练机器学习算法以生成响应。所考虑的算法有两个步骤:过滤(即触发)模型,以滤除不可行的患者消息和一个响应发生器,以建议成功通过触发阶段的响应前3位医生响应。该方法为Precision@3提供了83.28 \%的精度,并显示出其参数的鲁棒性。
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近年来,已经出现了许多巨魔帐户来操纵社交媒体的意见。对于社交网络平台而言,检测和消除巨魔是一个关键问题,因为企业,滥用者和民族国家赞助的巨魔农场使用虚假和自动化的帐户。 NLP技术用于从社交网络文本中提取数据,例如Twitter推文。在许多文本处理应用程序中,诸如BERT之类的单词嵌入表示方法的执行效果要好于先前的NLP技术,从而为各种任务提供了新颖的突破,以精确理解和分类社交网络工作信息。本文实施并比较了九个基于深度学习的巨魔推文检测体系结构,每个bert,elmo和手套词嵌入模型的三个模型。精度,召回,F1分数,AUC和分类精度用于评估每个体系结构。从实验结果中,大多数使用BERT模型的架构改进了巨魔推文检测。具有GRU分类器的基于自定义的基于ELMO的体系结构具有检测巨魔消息的最高AUC。所提出的体系结构可以由各种基于社会的系统用于未来检测巨魔消息。
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虽然罕见疾病的特征在于患病率低,但大约3亿人受到罕见疾病的影响。对这些条件的早期和准确诊断是一般从业者的主要挑战,没有足够的知识来识别它们。除此之外,罕见疾病通常会显示各种表现形式,这可能会使诊断更加困难。延迟的诊断可能会对患者的生命产生负面影响。因此,迫切需要增加关于稀有疾病的科学和医学知识。自然语言处理(NLP)和深度学习可以帮助提取有关罕见疾病的相关信息,以促进其诊断和治疗。本文探讨了几种深度学习技术,例如双向长期内存(BILSTM)网络或基于来自变压器(BERT)的双向编码器表示的深层语境化词表示,以识别罕见疾病及其临床表现(症状和症状) Raredis语料库。该毒品含有超过5,000名罕见疾病和近6,000个临床表现。 Biobert,基于BERT和培训的生物医学Corpora培训的域特定语言表示,获得了最佳结果。特别是,该模型获得罕见疾病的F1分数为85.2%,表现优于所有其他模型。
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The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II. These algorithms combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings as building blocks forming a single system. In short, our approach has three main contributions: (i) a set of techniques that fully integrate word embeddings and lexical chains; (ii) a more robust semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. We intend to assess the knowledge of pre-trained models to evaluate their robustness in the document classification task. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems.
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建模法检索和检索作为预测问题最近被出现为法律智能的主要方法。专注于法律文章检索任务,我们展示了一个名为Lamberta的深度学习框架,该框架被设计用于民法代码,并在意大利民法典上专门培训。为了我们的知识,这是第一项研究提出了基于伯特(来自变压器的双向编码器表示)学习框架的意大利法律制度对意大利法律制度的高级法律文章预测的研究,最近引起了深度学习方法的增加,呈现出色的有效性在几种自然语言处理和学习任务中。我们通过微调意大利文章或其部分的意大利预先训练的意大利预先训练的伯爵来定义Lamberta模型,因为法律文章作为分类任务检索。我们Lamberta框架的一个关键方面是我们构思它以解决极端的分类方案,其特征在于课程数量大,少量学习问题,以及意大利法律预测任务的缺乏测试查询基准。为了解决这些问题,我们为法律文章的无监督标签定义了不同的方法,原则上可以应用于任何法律制度。我们提供了深入了解我们Lamberta模型的解释性和可解释性,并且我们对单一标签以及多标签评估任务进行了广泛的查询模板实验分析。经验证据表明了Lamberta的有效性,以及对广泛使用的深度学习文本分类器和一些构思的几次学习者来说,其优越性是对属性感知预测任务的优势。
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讽刺可以被定义为说或写讽刺与一个人真正想表达的相反,通常是为了侮辱,刺激或娱乐某人。由于文本数据中讽刺性的性质晦涩难懂,因此检测到情感分析研究社区的困难和非常感兴趣。尽管讽刺检测的研究跨越了十多年,但最近已经取得了一些重大进步,包括在多模式环境中采用了无监督的预训练的预训练的变压器,并整合了环境以识别讽刺。在这项研究中,我们旨在简要概述英语计算讽刺研究的最新进步和趋势。我们描述了与讽刺有关的相关数据集,方法,趋势,问题,挑战和任务,这些数据集,趋势,问题,挑战和任务是无法检测到的。我们的研究提供了讽刺数据集,讽刺特征及其提取方法以及各种方法的性能分析,这些表可以帮助相关领域的研究人员了解当前的讽刺检测中最新实践。
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Text classification is a natural language processing (NLP) task relevant to many commercial applications, like e-commerce and customer service. Naturally, classifying such excerpts accurately often represents a challenge, due to intrinsic language aspects, like irony and nuance. To accomplish this task, one must provide a robust numerical representation for documents, a process known as embedding. Embedding represents a key NLP field nowadays, having faced a significant advance in the last decade, especially after the introduction of the word-to-vector concept and the popularization of Deep Learning models for solving NLP tasks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based Language Models (TLMs). Despite the impressive achievements in this field, the literature coverage regarding generating embeddings for Brazilian Portuguese texts is scarce, especially when considering commercial user reviews. Therefore, this work aims to provide a comprehensive experimental study of embedding approaches targeting a binary sentiment classification of user reviews in Brazilian Portuguese. This study includes from classical (Bag-of-Words) to state-of-the-art (Transformer-based) NLP models. The methods are evaluated with five open-source databases with pre-defined data partitions made available in an open digital repository to encourage reproducibility. The Fine-tuned TLMs achieved the best results for all cases, being followed by the Feature-based TLM, LSTM, and CNN, with alternate ranks, depending on the database under analysis.
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基于方面的情绪分析(ABSA)是一种文本分析方法,其定义了与特定目标相关的某些方面的意见的极性。 ABSA的大部分研究都是英文,阿拉伯语有少量的工作。最先前的阿拉伯语研究依赖于深度学习模型,主要依赖于独立于上下文的单词嵌入(例如,e.g.word2vec),其中每个单词都有一个独立于其上下文的固定表示。本文探讨了从预先培训的语言模型(如BERT)的上下文嵌入的建模功能,例如BERT,以及在阿拉伯语方面情感极度分类任务中使用句子对输入。特别是,我们开发一个简单但有效的基于伯特的神经基线来处理这项任务。根据三种不同阿拉伯语数据集的实验结果,我们的BERT架构与简单的线性分类层超出了最先进的作品。在Arabic Hotel评论数据库中实现了89.51%的准确性,73%的人类注册书评论数据集和阿拉伯新闻数据集的85.73%。
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Obtaining labelled data in a particular context could be expensive and time consuming. Although different algorithms, including unsupervised learning, semi-supervised learning, self-learning have been adopted, the performance of text classification varies with context. Given the lack of labelled dataset, we proposed a novel and simple unsupervised text classification model to classify cargo content in international shipping industry using the Standard International Trade Classification (SITC) codes. Our method stems from representing words using pretrained Glove Word Embeddings and finding the most likely label using Cosine Similarity. To compare unsupervised text classification model with supervised classification, we also applied several Transformer models to classify cargo content. Due to lack of training data, the SITC numerical codes and the corresponding textual descriptions were used as training data. A small number of manually labelled cargo content data was used to evaluate the classification performances of the unsupervised classification and the Transformer based supervised classification. The comparison reveals that unsupervised classification significantly outperforms Transformer based supervised classification even after increasing the size of the training dataset by 30%. Lacking training data is a key bottleneck that prohibits deep learning models (such as Transformers) from successful practical applications. Unsupervised classification can provide an alternative efficient and effective method to classify text when there is scarce training data.
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The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.
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Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data. Nevertheless, traditional models often fall short in intrinsic issues of linguistics, such as polysemy and homonymy. Any expert system that makes use of natural language in its core, can be affected by a weak semantic representation of text, resulting in inaccurate outcomes based on poor decisions. To mitigate such issues, we propose a novel approach called Most Suitable Sense Annotation (MSSA), that disambiguates and annotates each word by its specific sense, considering the semantic effects of its context. Our approach brings three main contributions to the semantic representation scenario: (i) an unsupervised technique that disambiguates and annotates words by their senses, (ii) a multi-sense embeddings model that can be extended to any traditional word embeddings algorithm, and (iii) a recurrent methodology that allows our models to be re-used and their representations refined. We test our approach on six different benchmarks for the word similarity task, showing that our approach can produce state-of-the-art results and outperforms several more complex state-of-the-art systems.
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The advances in language-based Artificial Intelligence (AI) technologies applied to build educational applications can present AI for social-good opportunities with a broader positive impact. Across many disciplines, enhancing the quality of mathematics education is crucial in building critical thinking and problem-solving skills at younger ages. Conversational AI systems have started maturing to a point where they could play a significant role in helping students learn fundamental math concepts. This work presents a task-oriented Spoken Dialogue System (SDS) built to support play-based learning of basic math concepts for early childhood education. The system has been evaluated via real-world deployments at school while the students are practicing early math concepts with multimodal interactions. We discuss our efforts to improve the SDS pipeline built for math learning, for which we explore utilizing MathBERT representations for potential enhancement to the Natural Language Understanding (NLU) module. We perform an end-to-end evaluation using real-world deployment outputs from the Automatic Speech Recognition (ASR), Intent Recognition, and Dialogue Manager (DM) components to understand how error propagation affects the overall performance in real-world scenarios.
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本教程展示了工作流程,将文本数据纳入精算分类和回归任务。主要重点是采用基于变压器模型的方法。平均长度为400个单词的车祸描述的数据集,英语和德语可用,以及具有简短财产保险索赔的数据集用来证明这些技术。案例研究应对与多语言环境和长输入序列有关的挑战。他们还展示了解释模型输出,评估和改善模型性能的方法,通过将模型调整到应用程序领域或特定预测任务。最后,该教程提供了在没有或仅有少数标记数据的情况下处理分类任务的实用方法。通过使用最少的预处理和微调的现成自然语言处理(NLP)模型的语言理解技能(NLP)模型实现的结果清楚地证明了用于实际应用的转移学习能力。
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社交媒体的自杀意图检测是一种不断发展的研究,挑战了巨大的挑战。许多有自杀倾向的人通过社交媒体平台分享他们的思想和意见。作为许多研究的一部分,观察到社交媒体的公开职位包含有价值的标准,以有效地检测有自杀思想的个人。防止自杀的最困难的部分是检测和理解可能导致自杀的复杂风险因素和警告标志。这可以通过自动识别用户行为的突然变化来实现。自然语言处理技术可用于收集社交媒体交互的行为和文本特征,这些功能可以传递给特殊设计的框架,以检测人类交互中的异常,这是自杀意图指标。我们可以使用深度学习和/或基于机器学习的分类方法来实现快速检测自杀式思想。出于这种目的,我们可以采用LSTM和CNN模型的组合来检测来自用户的帖子的这种情绪。为了提高准确性,一些方法可以使用更多数据进行培训,使用注意模型提高现有模型等的效率。本文提出了一种LSTM-Incription-CNN组合模型,用于分析社交媒体提交,以检测任何潜在的自杀意图。在评估期间,所提出的模型的准确性为90.3%,F1分数为92.6%,其大于基线模型。
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在线评论在电子商务中发挥重要作用进行决策。大部分人口做出了哪些地方,餐厅访问,以根据各自的平台发布的评论来购买的地方,从哪里购买。欺诈性审查或意见垃圾邮件被分类为一个不诚实或欺骗性的审查。产品或餐厅的肯定审查有助于吸引客户,从而导致销售额增加,而负面评论可能会妨碍餐厅或产品销售的进展,从而导致令人害羞的声誉和损失。欺诈性评论是故意发布的各种在线审查平台,以欺骗客户购买,访问或分散产品或餐厅的注意力。它们也被编写或诋毁产品的辩护。该工作旨在检测和分类审查作为欺骗性或真实性。它涉及使用各种深入学习技术来分类审查和概述涉及基于人的双向LSTM的提出的方法,以解决与基线机器学习技术的评论和比较研究中的语义信息有关的问题,以进行审查分类。
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