Joint entity recognition and relation extraction as a multi-head selection problem

State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers. Thus, the performance of such joint models depends on the quality of the features obtained from these NLP tools. However, these features are not always accurate for various languages and contexts. In this paper, we propose a joint neural model which performs entity recognition and relation extraction simultaneously, without the need of any manually extracted features or the use of any external tool. Specifically, we model the entity recognition task using a CRF (Conditional Random Fields) layer and the relation extraction task as a multi-head selection problem (i.e., potentially identify multiple relations for each entity). We present an extensive experimental setup, to demonstrate the effectiveness of our method using datasets from various contexts (i.e., news, biomedical, real estate) and languages (i.e., English, Dutch). Our model outperforms the previous neural models that use automatically extracted features, while it performs within a reasonable margin of feature-based neural models, or even beats them. (C) 2018 Elsevier Ltd. All rights reserved

An attentive neural architecture for joint segmentation and parsing and its application to real estate ads

In processing human produced text using natural language processing (NLP) techniques, two fundamental subtasks that arise are (i) segmentation of the plain text into meaningful subunits (e.g., entities), and (ii) dependency parsing, to establish relations between subunits. In this paper, we develop a relatively simple and effective neural joint model that performs both segmentation and dependency parsing together, instead of one after the other as in most state-of-the-art works. We will focus in particular on the real estate ad setting, aiming to convert an ad to a structured description, which we name property tree, comprising the tasks of (1) identifying important entities of a property (e.g., rooms) from classifieds and (2) structuring them into a tree format. In this work, we propose a new joint model that is able to tackle the two tasks simultaneously and construct the property tree by (i) avoiding the error propagation that would arise from the subtasks one after the other in a pipelined fashion, and (ii) exploiting the interactions between the subtasks. For this purpose, we perform an extensive comparative study of the pipeline methods and the new proposed joint model, reporting an improvement of over three percentage points in the overall edge F1 score of the property tree. Also, we propose attention methods, to encourage our model to focus on salient tokens during the construction of the property tree. Thus we experimentally demonstrate the usefulness of attentive neural architectures for the proposed joint model, showcasing a further improvement of two percentage points in edge F1 score for our application.Comment: Preprint - Accepted for publication in Expert Systems with Application

Neural approaches to sequence labeling for information extraction

Een belangrijk aspect binnen artificiële intelligentie (AI) is het interpreteren van menselijke taal uitgedrukt in tekstuele (geschreven) vorm: natural Language processing (NLP) is belangrijk gezien tekstuele informatie nuttig is voor veel toepassingen. Toch is het verstaan ervan (zogenaamde natural Language understanding, (NLU) een uitdaging, gezien de ongestructureerde vorm van tekst, waarvan de betekenis vaak dubbelzinnig en contextafhankelijk is. In dit proefschrift introduceren we oplossingen voor tekortkomingen van gerelateerd werk bij het behandelen van fundamentele taken in natuurlijke taalverwerking, zoals named entity recognition (i.e. het identificeren van de entiteiten die in een zin voorkomen) en relatie-extractie (het identificeren van relaties tussen entiteiten). Vertrekkend van een specifiek probleem (met name het identificeren van de structuur van een huis aan de hand van een tekstueel zoekertje), bouwen we stapsgewijs een complete (geautomatiseerde) oplossing voor de bovengenoemde taken, op basis van neutrale netwerkarchitecturen. Onze oplossingen zijn algemeen toepasbaar op verschillende toepassingsdomeinen en talen. We beschouwen daarnaast ook de taak van het identificeren van relevante gebeurtenissen tijdens een evenement (bv. een doelpunt tijdens een voetbalwedstrijd), in informatiestromen op Twitter. Meer bepaald formuleren we dit probleem als het labelen van woord sequenties (vergelijkbaar met named entity recognition), waarbij we de chronologische relatie tussen opeenvolgende tweets benutten