Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data. While the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography. These irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small. In our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.
Max Planck Institute for Empirical Aesthetics（马克斯·普朗克经验美学研究所）；University of Stuttgart（斯图加特大学）
A prerequisite for the computational study of literature is the availability of properly digitized texts, ideally with reliable meta-data and ground-truth annotation. Poetry corpora do exist for a number of languages, but larger collections lack consistency and are encoded in various standards, while annotated corpora are typically constrained to a particular genre and/or were designed for the analysis of certain linguistic features (like rhyme). In this work, we provide large poetry corpora for English and German, and annotate prosodic features in smaller corpora to train corpus driven neural…
L3S Research Center（L3S研究中心）；Leibniz University of Hannover（莱布尼茨汉诺威大学）；Austrian National Library（奥地利国家图书馆）；Amazon（亚马逊）
Optical character recognition (OCR) is crucial for a deeper access to historical collections. OCR needs to account for orthographic variations, typefaces, or language evolution (i.e., new letters, word spellings), as the main source of character, word, or word segmentation transcription errors. For digital corpora of historical prints, the errors are further exacerbated due to low scan quality and lack of language standardization. For the task of OCR post-hoc correction, we propose a neural approach based on a combination of recurrent (RNN) and deep convolutional network (ConvNet) to correct O…
Centre Jean-Mabillon（让-马比永中心）；École nationale des chartes（法国国家文献学院）；Université Paris, Sciences & Lettres（巴黎科学与文学大学）
Old French is a typical example of an under-resourced historic languages, that furtherly displays animportant amount of linguistic variation. In this paper, we present the current results of a long going project (2015-...) and describe how we broached the difficult question of providing lemmatisation andPOS models for Old French with the help of neural taggers and the progressive constitution of dedicated corpora.
Colloquial Persian POS (CPPOS) Corpus: A Novel Corpus for Colloquial Persian Part of Speech Tagging
Iran Telecommunication Research Center (ITRC)（伊朗电信研究中心）；Faculty of New Sciences and Technologies, University of Tehran（德黑兰大学新科技学院）；Sharif University of Technology（谢里夫理工大学）
L3Cube-MahaPOS: A Marathi Part-of-Speech Tagging Dataset and BERT Models
Department of Information Technology, PICT, Pune, India（印度浦那PICT信息技术系）；Indian Institute of Technology Madras, Chennai, India（印度理工学院马德拉斯分校）；L3 Cube Labs, Pune, India（印度浦那L3 Cube实验室）
Tagged corpora play a crucial role in a wide range of Natural Language Processing. The Part of Speech Tagging (POST) is essential in developing tagged corpora. It is time-and-effort-consuming and costly, and therefore, it could be more affordable if it is automated. The Kurdish language currently lacks publicly available tagged corpora of proper sizes. Tagging the publicly available Kurdish corpora can leverage the capability of those resources to a higher level than what raw or segmented corpora can provide. Developing POS-tagged lexicons can assist the mentioned task. We use a tagged corpus …
Detecting Historical Turning Points in Italian Media: A Complex Systems Approach to a Diachronic News Corpus
Department of Physics and Chemistry, University of Palermo（帕尔米洛大学物理与化学系）；Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), UIB-CSIC（跨学科物理与复杂系统研究所（IFISC）， UIB-CSIC）
Urgench State University（乌尔根奇国立大学）；Urgench Branch of Tashkent university of Information Technologies Named After Muhammad al-Khwarizmi（穆罕默德·花剌子米塔什干信息技术大学乌尔根奇分校）
Nowadays, creation of the tagged corpora is becoming one of the most important tasks of Natural Language Processing (NLP). There are not enough tagged corpora to build machine learning models for the low-resource Uzbek language. In this paper, we tried to fill that gap by developing a novel Part Of Speech (POS) and syntactic tagset for creating the syntactic and morphologically tagged corpus of the Uzbek language. This work also includes detailed description and presentation of a web-based application to work on a tagging as well. Based on the developed annotation tool and the software, we sha…
MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）；University of Rhode Island（罗德岛大学）；Leipzig University（莱比锡大学）；Bar-Ilan University（巴伊兰大学）；Dicta: The Israel Center for Text Analysis（Dicta：以色列文本分析中心）
Contextualized embeddings, which capture appropriate word meaning depending on context, have recently been proposed. We evaluate two meth ods for precomputing such embeddings, BERT and Flair, on four Czech text processing tasks: part-of-speech (POS) tagging, lemmatization, dependency pars ing and named entity recognition (NER). The first three tasks, POS tagging, lemmatization and dependency parsing, are evaluated on two corpora: the Prague Dependency Treebank 3.5 and the Universal Dependencies 2.3. The named entity recognition (NER) is evaluated on the Czech Named Entity Corpus 1.1 and 2.0. W…
MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）；University of Rhode Island（罗德岛大学）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Bar-Ilan University（巴伊兰大学）；Dicta: The Israel Center for Text Analysis（以色列文本分析中心（Dicta））；University of Vienna（维也纳大学）
Arabic is a widely-spoken language with a long and rich history, but existing corpora and language technology focus mostly on modern Arabic and its varieties. Therefore, studying the history of the language has so far been mostly limited to manual analyses on a small scale. In this work, we present a large-scale historical corpus of the written Arabic language, spanning 1400 years. We describe our efforts to clean and process this corpus using Arabic NLP tools, including the identification of reused text. We study the history of the Arabic language using a novel automatic periodization algorit…
Unsupervised Cross-Lingual Part-of-Speech Tagging with Monolingual Corpora Only
School of Foreign Languages, University of Electronic Science and Technology of China（电子科技大学外国语言学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）
PERCEPT: A Corpus for POS Tagging and Analysis of Persian-English Code-Mixing
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（德黑兰大学工程学院电气与计算机工程学院）；School of Engineering Science, College of Engineering, University of Tehran（德黑兰大学工程学院工程科学学院）；Tehran Institute for Advanced Studies, Khatam University（哈塔米大学德黑兰高级研究所）
HistLens: Mapping Idea Change across Concepts and Corpora
Department of Computer Science and Technology, Tsinghua University, China（清华大学计算机科学与技术系）；School of History, Nanjing University, China（南京大学历史学院）；Department of Chinese Language and Literature, Tsinghua University, China（清华大学中国语言文学系）；State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University, China（北京大学计算机学院多媒体信息处理国家重点实验室）
Under-resourced studies of under-resourced languages: lemmatization and POS-tagging with LLM annotators for historical Armenian, Georgian, Greek and Syriac
LIPN, CNRS UMR 7030（法国国家科学研究中心LIPN实验室（UMR 7030））；École nationale des chartes, Université Paris-Sciences-et-Lettres (PSL)（巴黎文理研究大学（PSL）国立文献学院）；Université catholique de Louvain（鲁汶天主教大学）
University of Louvain（鲁汶大学）；University Paris Diderot（巴黎狄德罗大学）；University of Geneva（日内瓦大学）
We present DisMo, a multi-level annotator for spoken language corpora that integrates part-of-speech tagging with basic disfluency detection and annotation, and multi-word unit recognition. DisMo is a hybrid system that uses a combination of lexical resources, rules, and statistical models based on Conditional Random Fields (CRF). In this paper, we present the first public version of DisMo for French. The system is trained and its performance evaluated on a 57k-token corpus, including different varieties of French spoken in three countries (Belgium, France and Switzerland). DisMo supports a mu…
Modern Models, Medieval Texts: A POS Tagging Study of Old Occitan
Bavarian Academy of Sciences（巴伐利亚科学院）；LMU Munich（慕尼黑大学）；Albert-Ludwigs-Universität Freiburg（弗莱堡大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Language models for historical states of language are becoming increasingly important to allow the optimal digitisation and analysis of old textual sources. Because these historical states are at the same time more complex to process and more scarce in the corpora available, specific efforts are necessary to train natural language processing (NLP) tools adapted to the data. In this paper, we present our efforts to develop NLP tools for Early Modern French (historical French from the 16$^\text{th}$ to the 18$^\text{th}$ centuries). We present the $\text{FreEM}_{\text{max}}$ corpus of Early Mode…
Dialogue Act (DA) tagging is crucial for spoken language understanding systems, as it provides a general representation of speakers' intents, not bound to a particular dialogue system. Unfortunately, publicly available data sets with DA annotation are all based on different annotation schemes and thus incompatible with each other. Moreover, their schemes often do not cover all aspects necessary for open-domain human-machine interaction. In this paper, we propose a methodology to map several publicly available corpora to a subset of the ISO standard, in order to create a large task-independent …
In natural language processing, the deep learning revolution has shifted the focus from conventional hand-crafted symbolic representations to dense inputs, which are adequate representations learned automatically from corpora. However, particularly when working with low-resource languages, small amounts of symbolic lexical resources such as user-generated lexicons are often available even when gold-standard corpora are not. Such additional linguistic information is though often neglected, and recent neural approaches to cross-lingual tagging typically rely only on word and subword embeddings. …
This study improves the performance of neural named entity recognition by a margin of up to 11% in F-score on the example of a low-resource language like German, thereby outperforming existing baselines and establishing a new state-of-the-art on each single open-source dataset. Rather than designing deeper and wider hybrid neural architectures, we gather all available resources and perform a detailed optimization and grammar-dependent morphological processing consisting of lemmatization and part-of-speech tagging prior to exposing the raw data to any training process. We test our approach in a…
Hanoi Univ of Science and Technology（河内科技大学）；VCCorp（VCCorp公司）
This paper presents a neural architecture for Vietnamese sequence labeling tasks including part-of-speech (POS) tagging and named entity recognition (NER). We applied the model described in \cite{lample-EtAl:2016:N16-1} that is a combination of bidirectional Long-Short Term Memory and Conditional Random Fields, which rely on two sources of information about words: character-based word representations learned from the supervised corpus and pre-trained word embeddings learned from other unannotated corpora. Experiments on benchmark datasets show that this work achieves state-of-the-art performan…
Shallow parsing is an essential task for many NLP applications like machine translation, summarization, sentiment analysis, aspect identification and many more. Quality annotated corpora is critical for building accurate shallow parsers. Many Indian languages are resource poor with respect to the availability of corpora in general. So, this paper is an attempt towards creating quality corpora for shallow parsers. The contribution of this paper is two folds: creation pos and chunk annotated corpora for Odia and development of baseline systems for pos tagging and chunking in Odia.
Background: Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of approaches to this information extraction task on syntactic information, it is valuable to understand which approaches to syntactic processing of biomedical text have the highest performance. Results: We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two…
Bayerische Staatsbibliothek München（慕尼黑巴伐利亚州立图书馆）；University of Vienna（维也纳大学）；Volkswagen AG（大众汽车集团）
Compared to standard Named Entity Recognition (NER), identifying persons, locations, and organizations in historical texts constitutes a big challenge. To obtain machine-readable corpora, the historical text is usually scanned and Optical Character Recognition (OCR) needs to be performed. As a result, the historical corpora contain errors. Also, entities like location or organization can change over time, which poses another challenge. Overall, historical texts come with several peculiarities that differ greatly from modern texts and large labeled corpora for training a neural tagger are hardl…
Charles University（查理大学）；Faculty of Mathematics and Physics（数学与物理学院）；Institute of Formal and Applied Linguistics（形式与应用语言学研究所）
We present an extensive evaluation of three recently proposed methods for contextualized embeddings on 89 corpora in 54 languages of the Universal Dependencies 2.3 in three tasks: POS tagging, lemmatization, and dependency parsing. Employing the BERT, Flair and ELMo as pretrained embedding inputs in a strong baseline of UDPipe 2.0, one of the best-performing systems of the CoNLL 2018 Shared Task and an overall winner of the EPE 2018, we present a one-to-one comparison of the three contextualized word embedding methods, as well as a comparison with word2vec-like pretrained embeddings and with e…
This paper outlines the creation of three corpora for multilingual comparison and analysis of the Voynich manuscript: a corpus of Voynich texts partitioned by Currier language, scribal hand, and transcription system, a corpus of 294 language samples compiled from Wikipedia, and a corpus of eighteen transcribed historical texts in eight languages. These corpora will be utilized in subsequent work by the Voynich Working Group at Yale University. We demonstrate the utility of these corpora for studying characteristics of the Voynich script and language, with an analysis of conditional character e…
From Traditional Taggers to LLMs: A Comparative Study of POS Tagging for Medieval Romance Languages
Bavarian Academy of Sciences (BAdW)（巴伐利亚科学院）；Department of Statistics, LMU Munich, Germany（慕尼黑大学统计系）；Munich Center for Machine Learning (MCML), LMU Munich, Germany（慕尼黑机器学习中心）
Exploring the Statistical Derivation of Transformational Rule Sequences for Part-of-Speech Tagging
Eric Brill最近提出了一种简单而强大的基于语料库的语言建模方法，该方法可以应用于各种任务，包括词性标注和构建短语结构树。该方法学习一系列符号转换规则，然后可以将其依次应用于测试语料库以产生预测。学习过程只需要计算一组给定规则模板的匹配，允许该方法调查非常大的可能的上下文因素空间。本文分析了布里尔的方法作为一个有趣的变化，现有的决策树方法，实验的基础上，涉及英语和古希腊语料库的词性标注。特别是，分析揭示了为什么新机制似乎令人惊讶地抵抗过度训练。一个快速，增量的实现和机制，用于记录的依赖关系，所产生的规则序列。
We present the first multi-task learning model -- named PhoNLP -- for joint Vietnamese part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing. Experiments on Vietnamese benchmark datasets show that PhoNLP produces state-of-the-art results, outperforming a single-task learning approach that fine-tunes the pre-trained Vietnamese language model PhoBERT (Nguyen and Nguyen, 2020) for each task independently. We publicly release PhoNLP as an open-source toolkit under the Apache License 2.0. Although we specify PhoNLP for Vietnamese, our PhoNLP training and evaluation com…
Part of speech tagging is a fundamental NLP task often regarded as solved for high-resource languages such as English. Current state-of-the-art models have achieved high accuracy, especially on the news domain. However, when these models are applied to other corpora with different genres, and especially user-generated data from the Web, we see substantial drops in performance. In this work, we study how a state-of-the-art tagging model trained on different genres performs on Web content from unfiltered Reddit forum discussions. More specifically, we use data from multiple sources: OntoNotes, a…
University of Mannheim（曼海姆大学）；Technische Universität Darmstadt（达姆施塔特工业大学）
We analyze bias in historical corpora as encoded in diachronic distributional semantic models by focusing on two specific forms of bias, namely a political (i.e., anti-communism) and racist (i.e., antisemitism) one. For this, we use a new corpus of German parliamentary proceedings, DeuPARL, spanning the period 1867--2020. We complement this analysis of historical biases in diachronic word embeddings with a novel measure of bias on the basis of term co-occurrences and graph-based label propagation. The results of our bias measurements align with commonly perceived historical trends of antisemit…
