A Joint Neural Model for Information Extraction with Global Features

Abstract

Most existing joint neural models for Information Extraction (IE) use local task-specific classifiers to predict labels for individual instances (e.g., trigger, relation) regardless of their interactions. For example, a VICTIM of a DIE event is likely to be a VICTIM of an AT-TACK event in the same sentence. In order to capture such cross-subtask and cross-instance inter-dependencies, we propose a joint neural framework, ONEIE, that aims to extract the globally optimal IE result as a graph from an input sentence. ONEIE performs end-to-end IE in four stages: (1) Encoding a given sentence as contextualized word representations;(2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder. At the decoding stage, we incorporate global features to capture the cross-subtask and cross-instance interactions. Experiments show that adding global features improves the performance of our model and achieves new state-of-the-art on all subtasks. As ONEIE does not use any language-specific feature, we prove it can be easily applied to new languages or trained in a multilingual manner. Our code and models for English, Spanish and Chinese are publicly available for research purpose 1 . PER Erdogan PER Abdullah Gul End-Position resigned Elect won person personExample: Prime Minister Abdullah Gul resigned earlier Tuesday to make way for Erdogan, who won a parliamentary seat in by-elections Sunday.

Event Extraction by Answering (Almost) Natural Questions

“…In particular, event argument extraction generally consists of two steps -first identifying entities and their general semantic class with trained models (Wadden et al, 2019) or a parser (Sha et al, 2018), then assigning argument roles (or no role) to each entity. Although joint models (Yang and Mitchell, 2016;Nguyen and Nguyen, 2019;Zhang et al, 2019a;Lin et al, 2020) have been proposed to mitigate this issue, error propagation (Li et al, 2013) still occurs during event argument extraction.…”

“…DYGIE++ (Wadden et al, 2019) is a BERT-based framework that models text spans and captures within-sentence and cross-sentence context. OneIE (Lin et al, 2020) is a joint neural model for extraction with global features. 2 In Table 2, we present the comparison of models' performance on trigger detection.…”

Connecting the Dots: Event Graph Schema Induction with Path Language Modeling

“…We use Automatic Content Extraction (ACE) 2005 dataset 2 , the widely used dataset with annotated instances of 7 entity types, 6 relation types, 33 event types, and 22 argument roles. We follow our recent work on ACE IE (Lin et al, 2020) to split the data. We consider the training set as historical data to train the LM, and the test set as our target data to induce schema for target scenarios.…”

“…The instance graphs of the target data set are constructed from manual annotations. For historical data, we construct event instance graphs from both manual annotations (Historical ann ) and system extraction results (Historical sys ) from the state-ofthe-art IE model (Lin et al, 2020). We perform cross-document entity coreference resolution by applying an entity linker (Pan et al, 2017) for both annotated and system generated instance graphs.…”

“…Such event graph schemas can also be exploited to enhance the performance of Information Extraction (IE) tasks, such as entity extraction, relation extraction, event extraction and argument role labeling, because most of the existing methods ignore such inter-event dependencies. For example, from the following sentence "Following the trail of Mohammed A. Salameh, the first suspect arrested in the bombing, investigators discovered a jumble of chemicals, chemistry implements and detonating materials...", the state-of-the-art IE system (Lin et al, 2020) successfully extracts the ARRESTJAIL event but fails to extract the INVESTIGATECRIME triggered by "discovered" and its DEFENDANT argument "Mohammed A. Salameh". Event graph scehmas can inform the model that a person who is arrested was usually investigated, our IE system can fix this missing error.…”