Focusing on the task of identifying event temporal status, we find that events directly or indirectly governing the target event in a dependency tree are most important contexts. Therefore, we extract dependency chains containing context events and use them as input in neural network models, which consistently outperform previous models using local context words as input. Visualization verifies that the dependency chain representation can effectively capture the context events which are closely related to the target event and play key roles in predicting event temporal status.
Similar to our dependency chains, dependency paths between two nodes in a dependency tree have been widely used as features for various NLP tasks and applications, including relation extraction @cite_13 , temporal relation identification @cite_16 semantic parsing @cite_9 and question answering @cite_11 . Differently, our dependency chains are generated with respect to an event word and include words that govern or depend on the event, which therefore are not bounded by two pre-identified nodes in a dependency tree.
@cite_9 @cite_16 @cite_13 @cite_11
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We propose a new approach to characterizing the timeline of a text: temporal dependency structures, where all the events of a narrative are linked via partial ordering relations like BEFORE, AFTER, OVERLAP and IDENTITY. We annotate a corpus of children's stories with temporal dependency trees, achieving agreement (Krippendorff's Alpha) of 0.856 on the event words, 0.822 on the links between events, and of 0.700 on the ordering relation labels. We compare two parsing models for temporal dependency structures, and show that a deterministic non-projective dependency parser outperforms a graph-based maximum spanning tree parser, achieving labeled attachment accuracy of 0.647 and labeled tree edit distance of 0.596. Our analysis of the dependency parser errors gives some insights into future research directions. We present a novel approach to relation extraction, based on the observation that the information required to assert a relationship between two named entities in the same sentence is typically captured by the shortest path between the two entities in the dependency graph. Experiments on extracting top-level relations from the ACE (Automated Content Extraction) newspaper corpus show that the new shortest path dependency kernel outperforms a recent approach based on dependency tree kernels. The goal of relation extraction is to detect relations between two entities in free text. In a sentence, a relation instance usually comprises a small number of words, which yields a sparse feature representation. To make better use of limited information in a relation instance, parsing trees and combined features are employed widely to capture the local dependencies of relation instances. However, the performance of parsing tree-based systems is often degraded by chunking or parsing errors. Combined features are used widely, but few studies have addressed how features can be combined to achieve optimal performance. Thus, in this study, we propose a feature assembly method for relation extraction. Six types of candidate features (head noun, POS tag, n-gram, omni-word, etc.) are employed as atomic features and six constraint conditions (singleton, position, syntax, etc.) are used to combine these features in different settings. Depending on the utilization of candidate features, different constraint conditions can be explored to achieve the optimal extraction performance. Our method is effective for capturing local dependencies and it reduces the errors caused by inaccurate parsing. We tested the proposed method using the ACE 2005 Chinese and English corpora, and it achieved state-of-the-art performance, where it was significantly superior to existing methods. We extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences. Using this kernel within a Support Vector Machine, we detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles. We examine the utility of different features such as Wordnet hypernyms, parts of speech, and entity types, and find that the dependency tree kernel achieves a 20 F1 improvement over a "bag-of-words" kernel.