Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNEREL: A Russian Dataset with Nested Named Entities, Relations and Events
نيريل: مجموعة بيانات روسية مع الكيانات المسماة المتداخلة والعلاقات والأحداث
181
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في هذه الورقة، نقدم نيريل، مجموعة بيانات روسية للتعرف على الكيان المسمى واستخراج العلاقة.نيريل أكبر بكثير من مجموعات البيانات الروسية القائمة: حتى الآن تحتوي على 56 كيلو كيانات المسماة المشروحة وعلاقات مشروحة 39 ألفا.الفرق المهم له من مجموعات البيانات السابقة هو شرح للكيانات المسماة المتداخلة، وكذلك العلاقات داخل الكيانات المتداخلة وفي مستوى الخطاب.يمكن أن تسهل نيريل تطوير نماذج جديدة يمكنها استخراج العلاقات بين الكيانات المسماة المتداخلة، وكذلك العلاقات في كل من المستويات والوثائق.يحتوي نيريل أيضا على شرح الأحداث التي تنطوي على الكيانات المسماة وأدوارها في الأحداث.تتوفر مجموعة Nerel عبر https://github.com/nerel-ds/nerel.
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it contains 56K annotated named entities and 39K annotated relations. Its important difference from previous datasets is annotation of nested named entities, as well as relations within nested entities and at the discourse level. NEREL can facilitate development of novel models that can extract relations between nested named entities, as well as relations on both sentence and document levels. NEREL also contains the annotation of events involving named entities and their roles in the events. The NEREL collection is available via https://github.com/nerel-ds/NEREL.
References used
https://aclanthology.org/
rate research
Read More
Pretraining-based neural network models have demonstrated state-of-the-art (SOTA) performances on natural language processing (NLP) tasks. The most frequently used sentence representation for neural-based NLP methods is a sequence of subwords that is
different from the sentence representation of non-neural methods that are created using basic NLP technologies, such as part-of-speech (POS) tagging, named entity (NE) recognition, and parsing. Most neural-based NLP models receive only vectors encoded from a sequence of subwords obtained from an input text. However, basic NLP information, such as POS tags, NEs, parsing results, etc, cannot be obtained explicitly from only the large unlabeled text used in pretraining-based models. This paper explores use of NEs on two Japanese tasks; document classification and headline generation using Transformer-based models, to reveal the effectiveness of basic NLP information. The experimental results with eight basic NEs and approximately 200 extended NEs show that NEs improve accuracy although a large pretraining-based model trained using 70 GB text data was used.
The stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the task involves reasoning of the tweet with respect to a target, we find that it i
s possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence. Specifically, a simple tweet classification model achieved human-level performance on the WT--WT dataset and more than two-third accuracy on various other datasets. We investigate the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category. Furthermore, we propose a new large dataset free of such biases and demonstrate its aptness on the existing stance detection systems. Our empirical findings show much scope for research on the stance detection task and proposes several considerations for creating future stance detection datasets.
Recognizing named entities in short search engine queries is a difficult task due to their weaker contextual information compared to long sentences. Standard named entity recognition (NER) systems that are trained on grammatically correct and long se
ntences fail to perform well on such queries. In this study, we share our efforts towards creating a cleaned and labeled dataset of real Turkish search engine queries (TR-SEQ) and introduce an extended label set to satisfy the search engine needs. A NER system is trained by applying the state-of-the-art deep learning method BERT to the collected data and its high performance on search engine queries is reported. Moreover, we compare our results with the state-of-the-art Turkish NER systems.
In this paper, we propose a controllable neural generation framework that can flexibly guide dialogue summarization with personal named entity planning. The conditional sequences are modulated to decide what types of information or what perspective t
o focus on when forming summaries to tackle the under-constrained problem in summarization tasks. This framework supports two types of use cases: (1) Comprehensive Perspective, which is a general-purpose case with no user-preference specified, considering summary points from all conversational interlocutors and all mentioned persons; (2) Focus Perspective, positioning the summary based on a user-specified personal named entity, which could be one of the interlocutors or one of the persons mentioned in the conversation. During training, we exploit occurrence planning of personal named entities and coreference information to improve temporal coherence and to minimize hallucination in neural generation. Experimental results show that our proposed framework generates fluent and factually consistent summaries under various planning controls using both objective metrics and human evaluations.
Abstract Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. Morphologically rich languages (MRLs) pose a challenge to this basic formulation, as the boundaries of named entities
do not necessarily coincide with token boundaries, rather, they respect morphological boundaries. To address NER in MRLs we then need to answer two fundamental questions, namely, what are the basic units to be labeled, and how can these units be detected and classified in realistic settings (i.e., where no gold morphology is available). We empirically investigate these questions on a novel NER benchmark, with parallel token- level and morpheme-level NER annotations, which we develop for Modern Hebrew, a morphologically rich-and-ambiguous language. Our results show that explicitly modeling morphological boundaries leads to improved NER performance, and that a novel hybrid architecture, in which NER precedes and prunes morphological decomposition, greatly outperforms the standard pipeline, where morphological decomposition strictly precedes NER, setting a new performance bar for both Hebrew NER and Hebrew morphological decomposition tasks.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
