We propose a static relation extraction task to complement biomedical information extraction approaches. We argue that static relations such as part-whole are implicitly involved in many common extraction settings, define a task setting making them explicit, and discuss their integration into previously proposed tasks and extraction methods. We further identify a specific static relation extrac...

This article describes our work on the BioCreative-V chemical-disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We ...

Recent approaches to relation extraction following the distant supervision paradigm have focused on exploiting large knowledge bases, from which they extract substantial amount of supervision. However, for many relations in real-world applications, there are few instances available to seed the relation extraction process, and appropriate named entity recognizers which are necessary for pre-proc...

Relation extraction between entities from text plays an important role in information extraction and knowledge discovery related tasks. A relation validation method justifies a claimed relation based on the provided information. In this paper we propose a relation validation method with some linguistic and world knowledge-based graph features for validating a claimed relation. MOTS-CLÉS : Valid...

Entity relation extraction is mainly focused on researching extraction approaches and improving precision of the extraction results. Although many efforts have been made on this field, there still exist some problems. In order to improve the performance of extracting entity relation, we propose a tuple refinement method based on relationship keyword extension. Firstly, we utilize the diversity ...

Relation Extraction methods based on Distant Supervision rely on true tuples to retrieve noisy mentions, which are then used to train traditional supervised relation extraction methods. In this paper we analyze the sources of noise in the mentions, and explore simple methods to filter out noisy mentions. The results show that a combination of mention frequency cut-off, Pointwise Mutual Informat...

Open domain relation extraction systems identify relation and argument phrases in a sentence without relying on any underlying schema. However, current state-of-the-art relation extraction systems are available only for English because of their heavy reliance on linguistic tools such as part-of-speech taggers and dependency parsers. We present a cross-lingual annotation projection method for la...

Recent approaches to relation extraction following the distant supervision paradigm have focused on exploiting large knowledge bases, from which they extract substantial amount of supervision. However, for many relations in real-world applications, there are few instances available to seed the relation extraction process, and appropriate named entity recognizers which are necessary for pre-proc...

We present an application of kernel methods for extracting relation between an aspect of an entity and an opinion word from text. Two tree kernels based on the constituent tree and dependency tree were applied for aspect-opinion relation extraction. In addition, we developed a new kernel by combining these two tree kernels. We also proposed a new model for sentiment analysis on aspects. Our mod...

Distant supervision is an efficient approach for various tasks, such as relation extraction. Most of the recent literature on distantly supervised relation extraction generates labeled data by heuristically aligning knowledge bases with text corpora and then trains supervised relation classification models based on statistical learning. However, extracting long tail relations from the automatic...