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Found 16 papers, 2 papers with code
SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models
Causal information extraction is an important task in natural language processing, particularly in finance domain.
IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News
In this paper, we present the event detection models and systems we have developed for Multilingual Protest News Detection - Shared Task 1 at CASE 2021.
IBM MNLP IE at CASE 2021 Task 2: NLI Reranking for Zero-Shot Text Classification
The NLI reranker uses a textual representation of target types that allows it to score the strength with which a type is implied by a text, without requiring training data for the types.
Distilling Event Sequence Knowledge From Large Language Models
In this work, we explore the use of Large Language Models (LLMs) to generate event sequences that can effectively be used for probabilistic event model construction.
Improving Neural Ranking Models with Traditional IR Methods
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions.
A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation.
A Generative Model for Relation Extraction and Classification
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering.
Cross-Lingual Relation Extraction with Transformers
Relation extraction (RE) is one of the most important tasks in information extraction, as it provides essential information for many NLP applications.
Event Presence Prediction Helps Trigger Detection Across Languages
The task of event detection and classification is central to most information retrieval applications.
Cascaded Models for Better Fine-Grained Named Entity Recognition
Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction.
Towards Lingua Franca Named Entity Recognition with BERT
In this paper we investigate a single Named Entity Recognition model, based on a multilingual BERT, that is trained jointly on many languages simultaneously, and is able to decode these languages with better accuracy than models trained only on one language.
Ranked #1 on
Cross-Lingual NER
on CoNLL Dutch
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications.
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning
In particular, the primal GAN learns to synthesize inter-class discriminative and semantics-preserving visual features from both the semantic representations of seen/unseen classes and the ones reconstructed by the dual GAN.
Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
Experimental results show that the proposed approaches are effective in improving the accuracy of such systems on unseen entities, especially when a system is applied to a new domain or it is trained with little training data (up to 18. 3 F1 score improvement).
Multilingual Named Entity Recognition
named-entity-recognition
+3
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection
However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language.
Learning Loosely Connected Markov Random Fields
We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm for learning the underlying graph structure.
