Search Results for author: Ralph Grishman

Found 35 papers, 6 papers with code

Learning Relatedness between Types with Prototypes for Relation Extraction

1 code implementation EACL 2021 Lisheng Fu, Ralph Grishman

We propose to use prototypical examples to represent each relation type and use these examples to augment related types from a different dataset.

Multi-Task Learning Relation Extraction

Entity Linking with a Paraphrase Flavor

1 code implementation LREC 2016 Maria Pershina, Yifan He, Ralph Grishman

The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions.

Entity Disambiguation Entity Linking

Combining Neural Networks and Log-linear Models to Improve Relation Extraction

no code implementations18 Nov 2015 Thien Huu Nguyen, Ralph Grishman

The last decade has witnessed the success of the traditional feature-based method on exploiting the discrete structures such as words or lexical patterns to extract relations from text.

 Ranked #1 on Relation Extraction on ACE 2005 (Cross Sentence metric)

Relation Extraction

Probabilistic Belief Embedding for Knowledge Base Completion

no code implementations10 May 2015 Miao Fan, Qiang Zhou, Andrew Abel, Thomas Fang Zheng, Ralph Grishman

This paper contributes a novel embedding model which measures the probability of each belief $\langle h, r, t, m\rangle$ in a large-scale knowledge repository via simultaneously learning distributed representations for entities ($h$ and $t$), relations ($r$), and the words in relation mentions ($m$).

Knowledge Base Completion

Jointly Embedding Relations and Mentions for Knowledge Population

no code implementations RANLP 2015 Miao Fan, Kai Cao, Yifan He, Ralph Grishman

This paper contributes a joint embedding model for predicting relations between a pair of entities in the scenario of relation inference.

Relation Extraction

Large Margin Nearest Neighbor Embedding for Knowledge Representation

no code implementations7 Apr 2015 Miao Fan, Qiang Zhou, Thomas Fang Zheng, Ralph Grishman

Traditional way of storing facts in triplets ({\it head\_entity, relation, tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively displayed and easily acquired by mankind, but hardly computed or even reasoned by AI machines.

Link Prediction

Challenges in the Knowledge Base Population Slot Filling Task

no code implementations LREC 2012 Bonan Min, Ralph Grishman

The Knowledge Based Population (KBP) evaluation track of the Text Analysis Conferences (TAC) has been held for the past 3 years.

Entity Linking Knowledge Base Population +2

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