Part of Speech Tagging

We report here on our experiments with POST (Part of Speech Tagger) to address problems of ambiguity and of understanding unknown words. Part of speech tagging, per se, is a …...

Bootstrap methods (unsupervised classification) that generate word classes without requiring pretagging have had notable success in the last few years. The methods described here strengthen these approaches and produce excellent …...

We present a novel method for segmenting the input sentence into words and assigning parts of speech to the words. It consists of a statistical language model and an efficient …...

Labeling of sentence boundaries is a nec- essary prerequisite for many natural lan- guage processing tasks, including part-of- speech tagging and sentence alignment. End-of-sentence punctuation marks are ambiguous; to disambiguate …...

The purpose of this paper is to describe the functions and procedures of the eight components of the Panglyzer Spanish analyzer of the knowledge-based engine of the Pangloss machine translation …...

Text corpora which are tagged with part-of-speech in- formation are useful in many areas of linguistic re- search. In this paper, a new part-of-speech tagging method hased on neural networks …...

Probabilistic models have been widely used for natural language processing. Part-of-speech tagging, which assigns the most likely tag to each word in a given sentence, is one. of tire problems …...

Lexical attributes, like syntactic (part-of-speech) and semantic (semantic category) attributes, are in most cases, ambiguous in every languages. Automatic resolution of ambiguity of these attributes can be achieved using different …...

The Academia Sinica Balanced Corpus (Sinica Corpus) is the first balanced Chinese corpus with part-of-speech tagging. The corpus (Sinica 2.0) is open to the research community through the WWW (http://www.sinica.edu.twiftms- …...

Palmer ([4]) demonstrated how Brill's Transformation-based Error-Driven Learning can be applied to word segmentation in various languages. We present experimental results which show that such al- gorithms can achieve satisfactory …...

This article attempts to determine what ele- ments of linguistic theory are used in statisti- cal language learning, and why the extracted language models look like they do. The study …...

The EU Copernicus project Multext-East has created a multi-lingual corpus of text and speech data, covering the six languages of the project: Bulgarian, Czech, Estonian, Hungarian, Romanian, and Slovene. In …...

For languages whose character set is very large and whose orthography does not require spac- ing between words, such as Japanese, tokenizing and part-of-speech tagging are often the diffi- cult …...

Boosting is a machine learning algorithm that is not well known in computational linguistics. We ap- ply it to part-of-speech tagging and prepositional phrase attachment. Performance is very encourag- ing. …...

This paper separates conditional parameter estima- tion, which consistently raises test set accuracy on statistical NLP tasks, from conditional model struc- tures, such as the conditional Markov model used for …...

This paper presents a revision learn- ing method that achieves high per- formance with small computational cost by combining a model with high generalization capacity and a model with small …...

This paper investigates booststrapping part-of- speech taggers using co-training, in which two taggers are iteratively re-trained on each other’s output. Since the output of the taggers is noisy, there is …...

In order to achieve fast and high quality Part-of-speech (PoS) tagging, algorithms should be high accuracy and require less manually proofreading. To evaluate a tagging system, we proposed a new …...

Today, corpus plays an important role in development and evaluation language and speech technologies, such as part of speech tagging, parsing, word sense disambiguation, text categorization, named entity classification, information …...

We present a new HMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case. Along the …...

In order to achieve fast, high quality Part-of-speech (pos) tagging, algorithms should achieve high accuracy and require less manually proofreading. This study aimed to achieve these goals by defining a …...

. This paper proposes a lexicon-constrained character model that com- bines both word and character features to solve complicated issues in Chinese morphological analysis. A Chinese character-based model constrained by …...

We present an approach to using a mor- phological analyzer for tokenizing and morphologically tagging (including part- of-speech tagging) Arabic words in one process. We learn classifiers for individual morphological …...

With performance above 97% accuracy for newspaper text, part of speech (POS) tag- ging might be considered a solved prob- lem. Previous studies have shown that allowing the parser to …...

Unsupervised learning of linguistic structure is a difficult problem. A common approach is to define a generative model and max- imize the probability of the hidden struc- ture given the …...

We propose a cascaded linear model for joint Chinese word segmentation and part- of-speech tagging. With a character-based perceptron as the core, combined with real- valued features such as language …...

AbstractIn many applications of natural language processing (NLP) grammatically tagged corpora are needed. Thus Part of Speech (POS) Tagging is of high importance in the domain of NLP. Many taggers …...

We present a HMM part-of-speech tag- ging method which is particularly suited for POS tagsets with a large number of fine-grained tags. It is based on three ideas: (1) splitting …...

Code-switching is an interesting linguistic phenomenon commonly observed in highly bilingual communities. It consists of mixing languages in the same conversational event. This paper presents results on Part-of-Speech tagging Spanish-English …...

Web-search queries are known to be short, but little else is known about their structure. In this paper we investigate the applicability of part-of-speech tagging to typical English- language web …...

We address corpus building situations, where complete annotations to the whole corpus is time consuming and unrealistic. Thus, annotation is done only on crucial part of sentences, or contains unresolved …...

We demonstrate the effectiveness of multilin- gual learning for unsupervised part-of-speech tagging. The key hypothesis of multilin- gual learning is that by combining cues from multiple languages, the structure of …...

Much previous work has investigated weak supervision with HMMs and tag dictionar- ies for part-of-speech tagging, but there have been no similar investigations for the harder problem of supertagging. Here, …...

. This paper investigates how to best couple hand-annotated data with information extracted from an external lexical resource to improve POS tagging performance. Focusing on French tagging, we introduce a …...

The (batch) EM algorithm plays an important role in unsupervised induction, but it some- times suffers from slow convergence. In this paper, we show that online variants (1) provide significant …...

AbstractArabic is a morphologically rich language, which presents a challenge for part of speech tagging. In this paper, we compare two novel methods for POS tagging of Arabic without the …...

AbstractLarge corpora are essential to modern methods of computational linguistics and natural language processing. In this paper, we describe an ongoing project whose aim is to build a largest corpus …...

We define the crouching Dirichlet, hidden Markov model (CDHMM), an HMM for part- of-speech tagging which draws state prior dis- tributions for each local document context. This simple modification of …...

. We introduce a method for learning to find the representative syntax-based context of a given collocation/phrase. In our approach, grammatical patterns are extracted for query terms aimed at accelerating …...

AbstractThis paper reports on the annotation of a corpus of 1 million words with four semantic annotation layers, including named entities, co- reference relations, semantic roles and spatial and temporal …...

AbstractThis paper describes the first phase of building a lexicon of Egyptian Cairene Arabic (ECA) ― one of the most widely understood dialects in the Arab World ― and Modern …...

This paper shows that the performance of history-based models can be significantly im- proved by performing lookahead in the state space when making each classification deci- sion. Instead of simply …...

AbstractPersian with its about 100,000,000 speakers in the world belongs to the group of languages with less developed linguistically annotated resources and tools. The few existing resources and tools are …...

Started from the very beginning, Stemming has been playing significant roles in several Natural Language Processing Applications such as information retrieval (IR), machine translation (MT), morph analysis and deciding the …...

Recent study shows that parsing accuracy can be largely improved by the joint optimization of part-of-speech (POS) tagging and dependency parsing. However, the POS tagging task does not benefit much …...

This paper presents the results of main part-of-speech tagging of Turkish sentences using Conditional Ran- dom Fields (CRFs). Although CRFs are applied to many different languages for part-of-speech (POS) tagging, …...

We propose an approach to correcting spelling errors and assigning part-of-speech (POS) tags simultaneously for sentences written by learners of English as a second language (ESL). In ESL writing, there …...

Recently, various unsupervised representation learning approaches have been investigated to produce augmenting features for natural language processing systems in the open-domain learning scenarios. In this paper, we propose a dynamic …...

Despite significant recent work, purely unsu- pervised techniques for part-of-speech (POS) tagging have not achieved useful accuracies required by many language processing tasks. Use of parallel text between resource-rich and …...

The purpose of part-of-speech tagging is to automatically tag the words of a text, written in a certain language, with labels that usually take the form of acronyms that designate …...

Construction of a Free Large Part-of-Speech Annotated Corpus in French This paper studies the possibility of creating a new part-of-speech annotated corpus in French from an existing one. The objectives …...

We present a framework for cross-lingual transfer of sequence information from a resource-rich source language to a resource- impoverished target language that incorporates soft constraints via posterior regularization. To this …...

AbstractDue to the nature of complex NLP problems, structured prediction algorithms have been important modeling tools for a wide range of tasks. While there exists evidence showing that linear Structural …...

Chinese word segmentation and part-of- speech tagging (S&T) are fundamental steps for more advanced Chinese language processing tasks. Recently, it has at- tracted more and more research interests to exploit …...

The many differences between Dialectal Ara- bic and Modern Standard Arabic (MSA) pose a challenge to the majority of Arabic natural language processing tools, which are designed for MSA. In …...

We experiment with using different sources of distant supervision to guide unsupervised and semi-supervised adaptation of part-of-speech (POS) and named entity taggers (NER) to Twitter. We show that a particularly …...

Active learning (AL) consists of asking human annotators to annotate automatically selected data that are assumed to bring the most bene- fit in the creation of a classifier. AL allows …...

. This work is motivated by the will of creating a new part-of-speech annotated corpus in French from an existing one. We propose a general and operational definition of the …...

. When Part-of-Speech annotated data is scarce, e.g. for under resourced languages, one can turn to cross- lingual transfer and crawled dictionaries to collect partially supervised data. We cast this …...

Part-of-speech (POS) taggers can be quite accurate, but for practical use, accuracy often has to be sacrificed for speed. For example, the maintainers of the Stanford tagger (Toutanova et al., …...

In the context of Social Media Analytics, Natural Language Processing tools face new chal- lenges on on-line conversational text, such as microblogs, chat, or text messages, because of the specificity …...

Structured perceptron becomes popular for various NLP tasks such as tagging and parsing. Practical studies on NLP did not pay much attention to its regularization. In this paper, we study …...

Word embeddings have been found useful for many NLP tasks, including part-of-speech tagging, named entity recognition, and pars- ing. Adding multilingual context when learn- ing embeddings can improve their quality, …...

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained mod- els of vector-space representations. Yet while ‘multi-sense’ methods have been proposed …...

We present two simple modifications to the models in the popular Word2Vec tool, in or- der to generate embeddings more suited to tasks involving syntax. The main issue with the …...

AbstractPrevious studies on Thai Sentence Boundary Detection (SBD) mostly assumed sentence ends at a space disambiguation problem, which classified space either as an indicator for Sentence Boundary (SB) or non-Sentence …...

AbstractSwiss dialects of German are, unlike most dialects of well standardised languages, widely used in everyday communication. Despite this fact, automatic processing of Swiss German is still a considerable challenge …...

We present CHARAGRAM embeddings, a sim- ple approach for learning character-based compositional models to embed textual se- quences. A word or sentence is represented using a character n-gram count vector, …...

We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of- speech tagging, dependency parsing and sentence compression results. Our model is a simple feed-forward neural network that …...

We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads text as bytes and outputs span annotations of the form [start, length, label] where start posi- tions, lengths, …...

AbstractThis work presents parallel corpora automatically annotated with several NLP tools, including lemma and part-of-speech tagging, named-entity recognition and classification, named-entity disambiguation, word-sense disambiguation, and coreference. The corpora comprise both …...

AbstractWe propose a novel semantic tagging task, semtagging, tailored for the purpose of multilingual semantic parsing, and present the first tagger using deep residual networks (ResNets). Our tagger uses both …...

AbstractAutomatic natural language processing of large texts often presents recurring challenges in multiple languages: even for most advanced tasks, the texts are first processed by basic processing steps – from …...

AbstractLanguage resources, such as corpora, are important for various natural language processing tasks. Urdu has millions of speakers around the world but it is under-resourced in terms of standard evaluation …...

AbstractIn this paper, we investigate a covert labeling cue, namely the probability that a title (by example of the Wikipedia titles) is a noun. If this probability is very large, …...

AbstractDomain similarity measures can be used to gauge adaptability and select suitable data for transfer learning, but existing approaches define ad hoc measures that are deemed suitable for respective tasks. …...

AbstractIn this work, we study the problem of part-of-speech tagging for Tweets. In contrast to newswire articles, Tweets are usually informal and contain numerous out-of-vocabulary words. Moreover, there is a …...

We present an ongoing effort on the first large-scale morphologically manually annotated corpus of Emirati Arabic. This corpus includes about 200,000 words selected from eight Gumar corpus novels in the …...

In this paper, we present a gold standard corpus for Albanian part-of-speech tagging and perform evaluation experiments with different statistical taggers. The corpus consists of more than 31,000 tokens and …...

AbstractMost work on part-of-speech (POS) tagging is focused on high resource languages, or examines low-resource and active learning settings through simulated studies. We evaluate POS tagging techniques on an actual …...

AbstractDue to the fact that Korean is a highly agglutinative, character-rich language, previous work on Korean morphological analysis typically employs the use of sub-character features known as graphemes or otherwise …...

AbstractK-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning. However, the very process of CV requires …...

AbstractRecently, pre-trained language models have achieved remarkable success in a broad range of natural language processing tasks. However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language …...

AbstractFor those languages which use it, capitalization is an important signal for the fundamental NLP tasks of Named Entity Recognition (NER) and Part of Speech (POS) tagging. In fact, it …...

AbstractWe propose a practical scheme to train a single multilingual sequence labeling model that yields state of the art results and is small and fast enough to run on a …...

AbstractMultilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable effective cross-lingual zero-shot transfer. However, their performance on Arabic …...

AbstractWe set out to uncover the unique grammatical properties of an important yet so far under-researched type of natural language text: that of short labels typically found within structured datasets. …...

AbstractChinese word segmentation (CWS) and part-of-speech (POS) tagging are two fundamental tasks for Chinese language processing. Previous studies have demonstrated that jointly performing them can be an effective one-step solution …...

AbstractChinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks. Previous …...

AbstractRecent trends in NLP research have raised an interest in linguistic code-switching (CS); modern approaches have been proposed to solve a wide range of NLP tasks on multiple language pairs. …...

AbstractIn this paper we describe our work on the development and enrichment of OFrLex, a freely available, large-coverage morphological and syntactic Old French lexicon. We rely on several heterogeneous language …...

AbstractWe present a multi-task learning framework to enable the training of one universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation …...

AbstractZero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of …...

AbstractPretrained multilingual models (PMMs) enable zero-shot learning via cross-lingual transfer, performing best for languages seen during pretraining. While methods exist to improve performance for unseen languages, they have almost exclusively …...

Chinese word segmentation and part-of- speech tagging are necessary tasks in terms of computational linguistics and application of natural language processing. Many re- searchers still debate the demand for Chinese …...

AbstractLexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labeling tasks due to their respective strengths. However, existing methods solely fuse lexicon features via …...

Model-agnostic meta-learning (MAML) has been recently put forth as a strategy to learn resource-poor languages in a sample-efficient fashion. Nevertheless, the properties of these languages are often not well represented …...

Recent years have seen a rise in interest for cross-lingual transfer between languages with similar typology, and between languages of various scripts. However, the interplay be- tween language similarity and …...

AbstractRunning large-scale pre-trained language models in computationally constrained environments remains a challenging problem yet to be addressed, while transfer learning from these models has become prevalent in Natural Language Processing …...

AbstractThe pre-trained language model has recently dominated most downstream tasks in the NLP area. Particularly, bidirectional Encoder Representations from Transformers (BERT) is the most iconic pre-trained language model among the …...

AbstractTrained on the large corpus, pre-trained language models (PLMs) can capture different levels of concepts in context and hence generate universal language representations. They can benefit from multiple downstream natural …...

AbstractThis study aims to evaluate three most popular word segmentation tool for a large Traditional Chinese corpus in terms of their efficiency, resource consumption, and cost. Specifically, we compare the …...

AbstractIn this paper, we evaluate several Transformer-based language models for Icelandic on four downstream tasks: Part-of-Speech tagging, Named Entity Recognition. Dependency Parsing, and Automatic Text Summarization. We pre-train four types …...

AbstractData-hungry deep neural networks have established themselves as the de facto standard for many NLP tasks, including the traditional sequence tagging ones. Despite their state-of-the-art performance on high-resource languages, they …...

AbstractBoundary information is critical for various Chinese language processing tasks, such as word segmentation, part-of-speech tagging, and named entity recognition. Previous studies usually resorted to the use of a high-quality …...

AbstractLarge multilingual models have inspired a new class of word alignment methods, which work well for the model’s pretraining languages. However, the languages most in need of automatic alignment are …...

AbstractWith the prominence of large pretrained language models, low-resource languages are rarely modelled monolingually and become victims of the “curse of multilinguality” in massively multilingual models. Recently, AfriBERTa showed that …...

AbstractAlthough pretrained language models (PLMs) can be prompted to perform a wide range of language tasks, it remains an open question how much this ability comes from generalizable linguistic understanding …...