Маркировка последовательностей

We present a two-stage multilingual de- pendency parsing system submitted to the Multilingual Track of CoNLL-2007. The parser first identifies dependencies using a deterministic parsing method and then labels those …...

Pictorial communication systems convert natural language text into pictures to as- sist people with limited literacy. We define a novel and challenging problem: picture layout optimization. Given an input sen- …...

This paper explores the use of the homo- topy method for training a semi-supervised Hidden Markov Model (HMM) used for sequence labeling. We provide a novel polynomial-time algorithm to trace …...

. In this paper, a layer-based projective dependency parsing approach is presented. This novel approach works layer by layer from the bottom up. Inside the layer the dependency graphs are …...

Many sequence labeling tasks in NLP require solving a cascade of segmentation and tag- ging subtasks, such as Chinese POS tagging, named entity recognition, and so on. Tradi- tional pipeline …...

Sequence labeling models like conditional random fields have been successfully applied in a variety of NLP tasks. However, as the size of label set and dataset grows, the learning speed …...

Error region detection and characterization in transcriptions of multimedia documents : application to person name search In this article, we propose to detect and characterize error regions in automatic transcriptions …...

There are many abbreviation and non-standard tokens in SMS and Twitter messages. Nor- malizing these non-standard tokens will ease natural language processing modules for these domains. Recently, character-level machine translation …...

A citing sentence is one that appears in a sci- entific article and cites previous work. Cit- ing sentences have been studied and used in many applications. For example, they …...

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 …...

Uncertainty detection is essential for many NLP applications. For instance, in information re- trieval, it is of primary importance to distinguish among factual, negated and uncertain informa- tion. Current research …...

AbstractSequence labeling architectures use word embeddings for capturing similarity, but suffer when handling previously unseen or rare words. We investigate character-level extensions to such models and propose a novel architecture …...

Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In the clinical domain one major ap- plication of sequence labeling …...

Supervised stance classification, in such do- mains as Congressional debates and online forums, has been a topic of interest in the past decade. Approaches have evolved from text classification to …...

. Recurrent Neural Networks have proved effective on several NLP tasks. Despite such great success, their ability to model sequence labeling is still limited. This lead research toward solutions where …...

AbstractWe propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to …...

AbstractIn this paper, we model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a new transition system …...

AbstractIn this paper, a new deep reinforcement learning based augmented general tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence labeling model …...

AbstractRecent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters. By learning to predict the next character on the basis …...

AbstractCharacter identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity. In this paper, …...

AbstractCognitive task analysis (CTA) is a type of analysis in applied psychology aimed at eliciting and representing the knowledge and thought processes of domain experts. In CTA, often heavy human …...

AbstractRetrieve-and-edit based approaches to structured prediction, where structures associated with retrieved neighbors are edited to form new structures, have recently attracted increased interest. However, much recent work merely conditions on …...

AbstractTargeted sentiment analysis is the task of jointly predicting target entities and their associated sentiment information. Existing research efforts mostly regard this joint task as a sequence labeling problem, building …...

AbstractThe phrase grounding task aims to ground each entity mention in a given caption of an image to a corresponding region in that image. Although there are clear dependencies between …...

AbstractWe use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions. To do so, we cast the problem as multitask learning (MTL). First, …...

AbstractJoint extraction of aspects and sentiments can be effectively formulated as a sequence labeling problem. However, such formulation hinders the effectiveness of supervised methods due to the lack of annotated …...

AbstractContextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a wide range of natural language processing tasks by pretraining on large corpora of unlabeled …...

AbstractWe define a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. This not only establishes a theoretical relation between …...

AbstractA major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning. Fine-tuning, the most widely used …...

AbstractMany tasks in natural language processing involve predicting structured outputs, e.g., sequence labeling, semantic role labeling, parsing, and machine translation. Researchers are increasingly applying deep representation learning to these problems, …...

AbstractProducing related words is a key concern in historical linguistics. Given an input word, the task is to automatically produce either its proto-word, a cognate pair or a modern word …...

AbstractMost pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable. However, those methods split …...

In this paper, we propose a method of ex- tracting novel character information such as characters’ names, gender, age, occupations, and a part of relationships between characters from synopses of …...

AbstractThis paper describes our approach to “DeftEval: Extracting Definitions from Free Text in Textbooks” competition held as a part of Semeval 2020. The task was devoted to finding and labeling …...

AbstractWords are properly segmented in the Persian writing system; in practice, however, these writing rules are often neglected, resulting in single words being written disjointedly and multiple words written without …...

AbstractWord embedding is an essential building block for deep learning methods for natural language processing. Although word embedding has been extensively studied over the years, the problem of how to …...

AbstractIn natural languages, words are used in association to construct sentences. It is not words in isolation, but the appropriate use of hierarchical structures that conveys the meaning of the …...

AbstractRecent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the last layer. …...

AbstractBoth performance and efficiency are crucial factors for sequence labeling tasks in many real-world scenarios. Although the pre-trained models (PTMs) have significantly improved the performance of various sequence labeling tasks, …...

AbstractArgument pair extraction (APE) is a research task for extracting arguments from two passages and identifying potential argument pairs. Prior research work treats this task as a sequence labeling problem …...

AbstractThis paper studies the relative importance of attention heads in Transformer-based models to aid their interpretability in cross-lingual and multi-lingual tasks. Prior research has found that only a few attention …...

AbstractSequence labeling aims to predict a fine-grained sequence of labels for the text. However, such formulation hinders the effectiveness of supervised methods due to the lack of token-level annotated data. …...

AbstractAlthough the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks. The sequence labeling-based NER models do not perform well in recognizing long entities …...

AbstractIn this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles. We formulate this problem as a sequence labeling task solved using …...

AbstractKnowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). The objective function of knowledge …...

AbstractIn low-resource settings, model transfer can help to overcome a lack of labeled data for many tasks and domains. However, predicting useful transfer sources is a challenging problem, as even …...

AbstractCorrect natural language understanding requires computers to distinguish the literal and metaphorical senses of a word. Recent neu- ral models achieve progress on verb metaphor detection by viewing it as …...

AbstractPrivacy protection raises great attention on both legal levels and user awareness. To protect user privacy, countries enact laws and regulations requiring software privacy policies to regulate their behavior. However, …...

AbstractTo automatically correct handwritten assignments, the traditional approach is to use an OCR model to recognize characters and compare them to answers. The OCR model easily gets confused on recognizing …...

AbstractWe offer a two-stage reranking method for grammatical error correction: the first model serves as edit generator, while the second classifies the proposed edits as correct or false. We show …...

Abstract“Medical named entity recognition (NER), a fundamental task of medical information extraction, is crucial for medical knowledge graph construction, medical question answering, and automatic medical record analysis, etc. Compared with …...

AbstractWe present bgGLUE (Bulgarian General Language Understanding Evaluation), a benchmark for evaluating language models on Natural Language Understanding (NLU) tasks in Bulgarian. Our benchmark includes NLU tasks targeting a variety …...

AbstractIXA proposes a Sequence labeling fine-tune approach, which consists of a lightweight few-shot baseline (10e), the system takes advantage of transfer learning from pre-trained Named Entity Recognition and cross-lingual knowledge …...

Medical Named Entity Recognition (NER) stands as a pivotal technique within the realm of medicine, cate sequence labeling.encompassing intri- Profound medi- cal knowledge acumen and accurate de- marcation of entity …...