Automatic Content Extraction

Entity detection and tracking is a relatively new addition to the repertoire of natural language tasks. In this paper, we present a statistical language-independent framework for identify- ing and tracking …...

We extend previous work on tree kernels to estimate the similarity between the dependency trees of sen- tences. Using this kernel within a Support Vector Machine, we detect and classify …...

In this paper, we study the impact of a group of features extracted automatically from machine-generated parse trees on coreference resolution. One focus is on designing syn- tactic features using …...

. This paper presents a supervised approach for relation extraction. We apply Support Vector Machines to detect and classify the relations in Automatic Content Extraction (ACE) corpus. We use a …...

As natural language understanding re- search advances towards deeper knowledge modeling, the tasks become more and more complex: we are interested in more nu- anced word characteristics, more linguistic properties, …...

Information Extraction (IE) is the task of extracting knowledge from unstructured text. We present a novel unsupervised approach for information extraction based on graph mutual reinforcement. The proposed approach does …...

A twin-model is proposed for coreference res- olution: a link component, modeling the coref- erential relationship between an anaphor and a candidate antecedent, and a creation com- ponent modeling the …...

We present an incremental joint frame- work to simultaneously extract entity men- tions and relations using structured per- ceptron with efficient beam-search. segment-based decoder based on the ideaA of semi-Markov …...

AbstractWe present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract …...