词性（POS）标签的一部分在自然语言处理（NLP）中起重要作用。它的应用程序可以在许多NLP任务中找到，例如命名实体识别，句法解析，依赖性解析和文本块。在本文进行的调查中，我们利用了两个广泛使用的工具包的技术，即Clearnlp和Stanford Pos Tagger，以及为越南人开发了两个新的POS标签，然后将它们与三个著名的越南标签者进行比较，即vntagger和rdrpostagger。我们进行系统的比较，以找出具有最佳性能的标签器。我们还设计了一个新功能集来衡量统计标签者的性能。我们由Stanford Tagger和新功能集构建的新标签者可以在标记准确性方面胜过所有其他当前的越南标签。此外，我们还分析了某些功能对统计标签者的性能的感情。最后，实验结果还表明，基于转换的标签器Rdrpostagger的运行速度明显快于任何其他统计标签器。

自动言论（POS）标记是许多自然语言处理（NLP）任务的预处理步骤，例如名称实体识别（NER），语音处理，信息提取，单词sense sisse disampigation和Machine Translation。它已经在英语和欧洲语言方面取得了令人鼓舞的结果，但是使用印度语言，尤其是在Odia语言中，由于缺乏支持工具，资源和语言形态丰富性，因此尚未得到很好的探索。不幸的是，我们无法为ODIA找到一个开源POS标记，并且仅尝试为ODIA语言开发POS标记器的尝试。这项研究工作的主要贡献是介绍有条件的随机场（CRF）和基于深度学习的方法（CNN和双向长期短期记忆）来开发ODIA的语音部分。我们使用了一个公开访问的语料库，并用印度标准局（BIS）标签设定了数据集。但是，全球的大多数语言都使用了带有通用依赖项（UD）标签集注释的数据集。因此，要保持统一性，odia数据集应使用相同的标签集。因此，我们已经构建了一个从BIS标签集到UD标签集的简单映射。我们对CRF模型进行了各种特征集输入，观察到构造特征集的影响。基于深度学习的模型包括BI-LSTM网络，CNN网络，CRF层，角色序列信息和预训练的单词向量。通过使用卷积神经网络（CNN）和BI-LSTM网络提取角色序列信息。实施了神经序列标记模型的六种不同组合，并研究了其性能指标。已经观察到具有字符序列特征和预训练的单词矢量的BI-LSTM模型取得了显着的最新结果。

Part of Speech (POS) tagging is crucial to Natural Language Processing (NLP). It is a well-studied topic in several resource-rich languages. However, the development of computational linguistic resources is still in its infancy despite the existence of numerous languages that are historically and literary rich. Assamese, an Indian scheduled language, spoken by more than 25 million people, falls under this category. In this paper, we present a Deep Learning (DL)-based POS tagger for Assamese. The development process is divided into two stages. In the first phase, several pre-trained word embeddings are employed to train several tagging models. This allows us to evaluate the performance of the word embeddings in the POS tagging task. The top-performing model from the first phase is employed to annotate another set of new sentences. In the second phase, the model is trained further using the fresh dataset. Finally, we attain a tagging accuracy of 86.52% in F1 score. The model may serve as a baseline for further study on DL-based Assamese POS tagging.

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.

命名实体识别是一项信息提取任务，可作为其他自然语言处理任务的预处理步骤，例如机器翻译，信息检索和问题答案。命名实体识别能够识别专有名称以及开放域文本中的时间和数字表达式。对于诸如阿拉伯语，阿姆哈拉语和希伯来语之类的闪族语言，由于这些语言的结构严重变化，指定的实体识别任务更具挑战性。在本文中，我们提出了一个基于双向长期记忆的Amharic命名实体识别系统，并带有条件随机字段层。我们注释了一种新的Amharic命名实体识别数据集（8,070个句子，具有182,691个令牌），并将合成少数群体过度采样技术应用于我们的数据集，以减轻不平衡的分类问题。我们命名的实体识别系统的F_1得分为93％，这是Amharic命名实体识别的新最新结果。

对任何人类语言的文本的语法分析通常涉及许多基本的处理任务，例如令牌化，形态标记和依赖性解析。最先进的系统可以在具有大数据集的语言上实现这些任务的高精度，但是对于几乎没有带注释的数据的他的他加禄语等语言的结果很差。为了解决他加禄语语言的此问题，我们研究了在没有带注释的他加禄语数据的情况下使用辅助数据源来创建特定于任务模型的使用。我们还探索了单词嵌入和数据扩展的使用，以提高性能，而只有少量带注释的他加禄语数据可用。我们表明，与最先进的监督基线相比，这些零射击和几乎没有射击的方法在对域内和域外的塔加尔teact文本进行了语法分析方面进行了实质性改进。

在本文中，我们研究了波斯语的G2P转换的端到端和多模块框架的应用。结果表明，我们提出的多模型G2P系统在准确性和速度方面优于我们的端到端系统。该系统由发音词典作为我们的查找表组成，以及使用GRU和Transformer架构创建的波斯语中的同符，OOV和EZAFE的单独模型。该系统是序列级别而不是单词级别，它使其能够有效地捕获单词（跨字信息）之间的不成文关系，而无需进行任何预处理，而无需进行任何预歧歧义和EZAFE识别。经过评估后，我们的系统达到了94.48％的单词级准确性，表现优于先前的波斯语G2P系统。

本地语言识别（NLI）是培训（通过监督机器学习）的任务，该分类器猜测文本作者的母语。在过去的十年中，这项任务已经进行了广泛的研究，多年来，NLI系统的性能稳步改善。我们专注于NLI任务的另一个方面，即分析由\ emph {Aupplable}机器学习算法培训的NLI分类器的内部组件，以获取其分类决策的解释，并具有获得的最终目标，即获得最终的目标。深入了解语言现象````赋予说话者''的母语''。我们使用这种观点来解决NLI和（研究得多的）伴侣任务，即猜测是由本地人还是非本地人说的文本。使用三个不同出处的数据集（英语学习者论文的两个数据集和社交媒体帖子的数据集），我们研究哪种语言特征（词汇，形态学，句法和统计）最有效地解决了我们的两项任务，即，最大的表明说话者的L1。我们还提出了两个案例研究，一个关于西班牙语，另一个关于意大利英语学习者，其中我们分析了分类器对发现这些L1最重要的单个语言特征。总体而言，我们的研究表明，使用可解释的机器学习可能是TH的宝贵工具

确保适当的标点符号和字母外壳是朝向应用复杂的自然语言处理算法的关键预处理步骤。这对于缺少标点符号和壳体的文本源，例如自动语音识别系统的原始输出。此外，简短的短信和微博的平台提供不可靠且经常错误的标点符号和套管。本调查概述了历史和最先进的技术，用于恢复标点符号和纠正单词套管。此外，突出了当前的挑战和研究方向。

我们在佩恩 - 赫尔辛基解析的早期现代英语（PPCEME）中的第一个解析结果，是一个190万字的TreeBank，这是句法变化研究的重要资源。我们描述了PPCEME的关键特征，使其成为解析的挑战，包括比Penn TreeBank中更大且更多样化的功能标签。我们使用伯克利神经解析器的修改版本为此语料库提出了结果，以及Gabbard等人的功能标签恢复的方法（2006）。尽管其简单性，这种方法令人惊讶地令人惊讶地令人惊讶的是，建议可以以足够的准确度恢复原始结构，以支持语言应用（例如，寻找涉及的句法结构）。然而，对于函数标签的子集（例如，指示直接演讲的标签），需要额外的工作，我们讨论了这种方法的一些进一步限制。由此产生的解析器将用于在网上解析早期英语书籍，一个11亿字形的语料库，其实用性对于句法变化的效用将大大增加，加入准确的解析树。

Pronoun resolution is a challenging subset of an essential field in natural language processing called coreference resolution. Coreference resolution is about finding all entities in the text that refers to the same real-world entity. This paper presents a hybrid model combining multiple rulebased sieves with a machine-learning sieve for pronouns. For this purpose, seven high-precision rule-based sieves are designed for the Persian language. Then, a random forest classifier links pronouns to the previous partial clusters. The presented method demonstrates exemplary performance using pipeline design and combining the advantages of machine learning and rulebased methods. This method has solved some challenges in end-to-end models. In this paper, the authors develop a Persian coreference corpus called Mehr in the form of 400 documents. This corpus fixes some weaknesses of the previous corpora in the Persian language. Finally, the efficiency of the presented system compared to the earlier model in Persian is reported by evaluating the proposed method on the Mehr and Uppsala test sets.

Grammar Detection, also referred to as Parts of Speech Tagging of raw text, is considered an underlying building block of the various Natural Language Processing pipelines like named entity recognition, question answering, and sentiment analysis. In short, forgiven a sentence, Parts of Speech tagging is the task of specifying and tagging each word of a sentence with nouns, verbs, adjectives, adverbs, and more. Sentiment Analysis may well be a procedure accustomed to determining if a given sentence's emotional tone is neutral, positive or negative. To assign polarity scores to the thesis or entities within phrase, in-text analysis and analytics, machine learning and natural language processing, approaches are incorporated. This Sentiment Analysis using POS tagger helps us urge a summary of the broader public over a specific topic. For this, we are using the Viterbi algorithm, Hidden Markov Model, Constraint based Viterbi algorithm for POS tagging. By comparing the accuracies, we select the foremost accurate result of the model for Sentiment Analysis for determining the character of the sentence.

为低资源语言开发自然语言处理资源是一个具有挑战性的，但必不可少的任务。在本文中，我们为古吉拉特提提出了一种形态学分析仪。我们使用了基于双向LSTM的方法来执行语素边界检测和语法特征标记。我们创建了一个带有引理和语法特征的古吉拉特语的数据集。本文讨论的基于Bi-LSTM的Morph分析仪模型，有效地处理了语言形态，而不知道任何手工制作的后缀规则。据我们所知，这是Gujarati语言的第一个DataSet和Morph分析仪模型，它执行语法特征标记和语素边界检测任务。

We propose a transition-based approach that, by training a single model, can efficiently parse any input sentence with both constituent and dependency trees, supporting both continuous/projective and discontinuous/non-projective syntactic structures. To that end, we develop a Pointer Network architecture with two separate task-specific decoders and a common encoder, and follow a multitask learning strategy to jointly train them. The resulting quadratic system, not only becomes the first parser that can jointly produce both unrestricted constituent and dependency trees from a single model, but also proves that both syntactic formalisms can benefit from each other during training, achieving state-of-the-art accuracies in several widely-used benchmarks such as the continuous English and Chinese Penn Treebanks, as well as the discontinuous German NEGRA and TIGER datasets.

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject-verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarise the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgements, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.

在科学研究中，该方法是解决科学问题和关键研究对象的必不可少手段。随着科学的发展，正在提出，修改和使用许多科学方法。作者在抽象和身体文本中描述了该方法的详细信息，并且反映该方法名称的学术文献中的关键实体称为方法实体。在大量的学术文献中探索各种方法实体有助于学者了解现有方法，为研究任务选择适当的方法并提出新方法。此外，方法实体的演变可以揭示纪律的发展并促进知识发现。因此，本文对方法论和经验作品进行了系统的综述，重点是从全文学术文献中提取方法实体，并努力使用这些提取的方法实体来建立知识服务。首先提出了本综述涉及的关键概念的定义。基于这些定义，我们系统地审查了提取和评估方法实体的方法和指标，重点是每种方法的利弊。我们还调查了如何使用提取的方法实体来构建新应用程序。最后，讨论了现有作品的限制以及潜在的下一步。

当前的语言模型可以产生高质量的文本。他们只是复制他们之前看到的文本，或者他们学习了普遍的语言抽象吗？要取笑这些可能性，我们介绍了乌鸦，这是一套评估生成文本的新颖性，专注于顺序结构（n-gram）和句法结构。我们将这些分析应用于四种神经语言模型（LSTM，变压器，变换器-XL和GPT-2）。对于本地结构 - 例如，单个依赖性 - 模型生成的文本比来自每个模型的测试集的人类生成文本的基线显着不那么新颖。对于大规模结构 - 例如，总句结构 - 模型生成的文本与人生成的基线一样新颖甚至更新颖，但模型仍然有时复制，在某些情况下，在训练集中重复超过1000字超过1,000字的通道。我们还表现了广泛的手动分析，表明GPT-2的新文本通常在形态学和语法中形成良好，但具有合理的语义问题（例如，是自相矛盾）。

虽然有几种可用于匈牙利语的源语言处理管道，但它们都不满足当今NLP应用程序的要求。语言处理管道应由接近最先进的lemmatization，形态学分析，实体识别和单词嵌入。工业文本处理应用程序必须满足非功能性的软件质量要求，更重要的是，支持多种语言的框架越来越受青睐。本文介绍了哈普西，匈牙利匈牙利语言处理管道。呈现的工具为最重要的基本语言分析任务提供组件。它是开源，可在许可证下提供。我们的系统建立在Spacy的NLP组件之上，这意味着它快速，具有丰富的NLP应用程序和扩展生态系统，具有广泛的文档和众所周知的API。除了底层模型的概述外，我们还对共同的基准数据集呈现严格的评估。我们的实验证实，母鹿在所有子组织中具有高精度，同时保持资源有效的预测能力。

Automatic keyword extraction (AKE) has gained more importance with the increasing amount of digital textual data that modern computing systems process. It has various applications in information retrieval (IR) and natural language processing (NLP), including text summarisation, topic analysis and document indexing. This paper proposes a simple but effective post-processing-based universal approach to improve the performance of any AKE methods, via an enhanced level of semantic-awareness supported by PoS-tagging. To demonstrate the performance of the proposed approach, we considered word types retrieved from a PoS-tagging step and two representative sources of semantic information -- specialised terms defined in one or more context-dependent thesauri, and named entities in Wikipedia. The above three steps can be simply added to the end of any AKE methods as part of a post-processor, which simply re-evaluate all candidate keywords following some context-specific and semantic-aware criteria. For five state-of-the-art (SOTA) AKE methods, our experimental results with 17 selected datasets showed that the proposed approach improved their performances both consistently (up to 100\% in terms of improved cases) and significantly (between 10.2\% and 53.8\%, with an average of 25.8\%, in terms of F1-score and across all five methods), especially when all the three enhancement steps are used. Our results have profound implications considering the ease to apply our proposed approach to any AKE methods and to further extend it.

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available. In this paper, we introduce two new neural architectures-one based on bidirectional LSTMs and conditional random fields, and the other that constructs and labels segments using a transition-based approach inspired by shift-reduce parsers. Our models rely on two sources of information about words: character-based word representations learned from the supervised corpus and unsupervised word representations learned from unannotated corpora. Our models obtain state-of-the-art performance in NER in four languages without resorting to any language-specific knowledge or resources such as gazetteers. 1