Sequence Tagging

We propose a novel approach to sentiment analysis for a low resource setting. The in- tuition behind this work is that sentiment expressed towards an entity, targeted senti- ment, may …...

Verbs in Romanian sometimes manifest local irregularities in the form of alternat- ing letters. We present a sequence tag- ging based method for learning stem al- ternations and ending sequences. …...

Cross lingual projection of linguistic an- notation suffers from many sources of bias and noise, leading to unreliable annota- tions that cannot be used directly. In this paper, we introduce …...

Most of the sequence tagging tasks in nat- ural language processing require to recog- nize segments with certain syntactic role or semantic meaning in a sentence. They are usually tackled …...

AbstractIn this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate for common sequence tagging tasks that the seed value for the …...

AbstractWe study three general multi-task learning (MTL) approaches on 11 sequence tagging tasks. Our extensive empirical results show that in about 50% of the cases, jointly learning all 11 tasks …...

AbstractWe present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a …...

AbstractWord segmentation is a low-level NLP task that is non-trivial for a considerable number of languages. In this paper, we present a sequence tagging framework and apply it to word …...

AbstractSequence tagging models for constituent parsing are faster, but less accurate than other types of parsers. In this work, we address the following weaknesses of such constituent parsers: (a) high …...

AbstractModern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated …...

AbstractObtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et. al., 2018) to …...

AbstractModels for reading comprehension (RC) commonly restrict their output space to the set of all single contiguous spans from the input, in order to alleviate the learning problem and avoid …...

AbstractArgumentation mining on essays is a new challenging task in natural language processing, which aims to identify the types and locations of argumentation components. Recent research mainly models the task …...

AbstractThis paper presents experiments on sentence boundary detection in transcripts of spoken dialogues. Segmenting spoken language into sentence-like units is a challenging task, due to disfluencies, ungrammatical or fragmented structures …...

AbstractThis work revisits the task of training sequence tagging models with limited resources using transfer learning. We investigate several proposed approaches introduced in recent works and suggest a new loss …...

AbstractScientific keyphrase identification and classification is the task of detecting and classifying keyphrases from scholarly text with their types from a set of predefined classes. This task has a wide …...

AbstractLeveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general representations that can then be further …...

AbstractAnnotating training data for sequence tagging of texts is usually very time-consuming. Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to …...

AbstractUnderstanding privacy policies is crucial for users as it empowers them to learn about the information that matters to them. Sentences written in a privacy policy document explain privacy practices, …...

AbstractRecurrent Neural Networks (RNN) have been widely used in various Natural Language Processing (NLP) tasks such as text classification, sequence tagging, and machine translation. Long Short Term Memory (LSTM), a …...

AbstractJoint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as …...

AbstractThe task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this task suffer …...

AbstractSentiment analysis aims to detect the overall sentiment, i.e., the polarity of a sentence, paragraph, or text span, without considering the entities mentioned and their aspects. Aspect-based sentiment analysis aims …...

AbstractIdentification of fine-grained location mentions in crisis tweets is central in transforming situational awareness information extracted from social media into actionable information. Most prior works have focused on identifying generic …...

AbstractThis paper presents our solution for SemEval-2022 Task 10: Structured Sentiment Analysis. The solution consisted of two modules: the first for sequence tagging and the second for relation classification. In …...

AbstractNatural Language Processing algorithms have made incredible progress, but they still struggle when applied to out-of-distribution examples. We address a challenging and underexplored version of this domain adaptation problem, where …...

AbstractThe Appraisal framework in linguistics defines the framework for fine-grained evaluations and opinions and has contributed to sentiment analysis and opinion mining. As developing appraisal-annotated resources requires tagging of several …...