Recognising if a relation holds between two entities in a text plays a vital role in information extraction. To address this problem, multiple models have been proposed based on fixed or contextualised word representations. In this paper, we propose a meta relation classification model that can integrate the most recent models by the use of a related task, namely relation validation. To do so, we encode the text that may contain the relation and a relation triplet candidate into a sentence-triplet representation. We grounded our strategy in recent neural architectures that allow single sentence classification as well as pair comparisons. Finally, our model is trained to determine the most relevant sentence-triplet pair from a set of candidates. Experiments on two public data sets for relation extraction show that the use of the sentence-triplet representation outperforms strong baselines and achieves comparable results when compared to larger models.
In this paper, we propose an RDF model for representing embeddings for elements in lexical knowledge graphs (e.g. words and concepts), harmonizing two major paradigms in computational semantics on the level of representation, i.e., distributional semantics (using numerical vector representations), and lexical semantics (employing lexical knowledge graphs). Our model is a part of a larger effort to extend a community standard for representing lexical resources, OntoLex-Lemon, with the means to connect it to corpus data. By allowing a conjoint modelling of embeddings and lexical knowledge graphs as its part, we hope to contribute to the further consolidation of the field, its methodologies and the accessibility of the respective resources.
In this paper, we tackle the task of Word Sense Disambiguation (WSD). We present our system submitted to the Word-in-Context Target Sense Verification challenge, part of the SemDeep workshop at IJCAI 2020 (Breit et al., 2020). That challenge asks participants to predict if a specific mention of a word in a text matches a pre-defined sense. Our approach uses pre-trained transformer models such as BERT that are fine-tuned on the task using different architecture strategies. Our model achieves the best accuracy and precision on Subtask 1 – make use of definitions for decid-ing whether the target word in context corre-sponds to the given sense or not. We believe the strategies we explored in the context of this challenge can be useful to other Natural Language Processing tasks.
This paper describes the CTRL participation in the Target Sense Verification of the Words in Context challenge (WiC-TSV) at SemDeep6. Our strategy is based on a simplistic annotation scheme of the target words to later be classified by well-known pre-trained neural models. In particular, the marker allows to include position information to help models to correctly identify the word to disambiguate. Results on the challenge show that our strategy outperforms other participants (+11, 4 Accuracy points) and strong baselines (+1, 7 Accuracy points).
Although the use of end-to-end neural architectures has been proven to be effective on several sequence labeling tasks, the use of gazetteers in these architectures is still rather unexplored. We investigate several options, aiming at exploiting gazetteers to extract relevant features, and then at integrating these features in a neural model for entity recognition. We provide experimental evidences on two datasets (named entities and nominal entities) and two languages (English and Italian), showing that extracting features from a rich model of the gazetteer and then concatenating such features with the input embeddings of a neural model is the best strategy in all our experimental settings, significantly outperforming more conventional approaches.
