We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM-CNN) that exploits word embeddings and positional embeddings for cross-sentence n-ary relation extraction. The proposed model brings together the properties of both LSTMs and CNNs, to simultaneously exploit long-range sequential information and capture most informative features, essential for cross-sentence n-ary relation extraction. The LSTM-CNN model is evaluated on standard dataset on cross-sentence n-ary relation extraction, where it significantly outperforms baselines such as CNNs, LSTMs and also a combined CNN-LSTM model. The paper also shows that the LSTM-CNN model outperforms the current state-of-the-art methods on cross-sentence n-ary relation extraction.
 In natural language relationships between entities can asserted within a single sentence or over many sentences in a document. Many information extraction systems are constrained to extracting binary relations that are asserted within a single sentence (single-sentence relations) and this limits the proportion of relations they can extract since those expressed across multiple sentences (inter-sentential relations) are not considered. The analysis in this paper focuses on finding the distribution of inter-sentential and single-sentence relations in two corpora used for the evaluation of information extraction systems: the MUC6 corpus and the ACE corpus from 2003. In order to carry out this analysis we had to manually mark up all the management succession relations described in the MUC6 corpus. It was found that inter-sentential relations constitute 28.5 and 9.4 of the total number of relations in MUC6 and ACE03 respectively. This places upper bounds on the recall of information extraction systems that do not consider relations that are asserted across multiple sentences (71.5 and 90.6 respectively). Relation extraction, as an important part of information extraction, can be used for many applications such as question-answering and knowledge base population. To thoroughly comprehend relation extraction, the paper reviews it mainly concentrating on its mainstream methods. Besides, open information extraction (OIE), as a different relation extraction paradigm, is introduced as well. Also, we exploit the challenges and directions for relation extraction. We hope the paper will give the overview of relation extraction and help guide the path ahead. This paper proposes state-of-the-art models for time-event relation extraction (TERE). The models are specifically designed to work effectively with relations that span multiple sentences and paragraphs, i.e., inter-sentence TERE. Our main idea is: (i) to build a computational representation of the context of the two target relation arguments, and (ii) to encode it as structural features in Support Vector Machines using tree kernels. Results on two data sets – Machine Reading and TimeBank – with 3-fold crossvalidation show that the combination of traditional feature vectors and the new structural features improves on the state of the art for inter-sentence TERE by about 20 , achieving a 30.2 F1 score on intersentence TERE alone, and 47.2 F1 for all TERE (inter and intra sentence combined).  The growing demand for structured knowledge has led to great interest in relation extraction, especially in cases with limited supervision. However, existing distance supervision approaches only extract relations expressed in single sentences. In general, cross-sentence relation extraction is under-explored, even in the supervised-learning setting. In this paper, we propose the first approach for applying distant supervision to cross- sentence relation extraction. At the core of our approach is a graph representation that can incorporate both standard dependencies and discourse relations, thus providing a unifying way to model relations within and across sentences. We extract features from multiple paths in this graph, increasing accuracy and robustness when confronted with linguistic variation and analysis error. Experiments on an important extraction task for precision medicine show that our approach can learn an accurate cross-sentence extractor, using only a small existing knowledge base and unlabeled text from biomedical research articles. Compared to the existing distant supervision paradigm, our approach extracted twice as many relations at similar precision, thus demonstrating the prevalence of cross-sentence relations and the promise of our approach.