Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network

Abstract

Relational triple extraction is a crucial task for knowledge graph construction. Existing methods mainly focused on explicit relational triples that are directly expressed, but usually suffer from ignoring implicit triples that lack explicit expressions. This will lead to serious incompleteness of the constructed knowledge graphs. Fortunately, other triples in the sentence provide supplementary information for discovering entity pairs that may have implicit relations. Also, the relation types between the implicitly connected entity pairs can be identified with relational reasoning patterns in the real world. In this paper, we propose a unified framework to jointly extract explicit and implicit relational triples. To explore entity pairs that may be implicitly connected by relations, we propose a binary pointer network to extract overlapping relational triples relevant to each word sequentially and retain the information of previously extracted triples in an external memory. To infer the relation types of implicit relational triples, we propose to introduce real-world relational reasoning patterns in our model and capture these patterns with a relation network. We conduct experiments on several benchmark datasets, and the results prove the validity of our method.

Relational Triple Extraction: One Step is Enough

“…The second category is table filling methods, which formulate the triple extraction task as a table constituted by the Cartesian product of the input sentence to itself. For example, GraphRel [Fu et al, 2019] takes the interaction between entities and relations into account via a relation-weighted Graph Convolutional Network. TPLinker converts triple extraction as a token pair linking problem and introduces a relation-specific handshaking tagging scheme to align the boundary tokens of entity pairs.…”

“…We compare our method with the following ten baselines: GraphRel [Fu et al, 2019], MHSA , RSAN [Liu et al, 2020], CopyMTL [Zeng et al, 2020], CasRel [Wei et al, 2020], TPLinker [Wang et al, 2020], CGT [Ye et al, 2021], PRGC [Zheng et al, 2021], R-BPtrNet , BiRTE [Ren et al, 2022].…”

“…Relational triple extraction, defined as the task of extracting pairs of entities and their relations in the form of (head, relation, tail) or (h, r, t) from unstructured text, is an important task in natural language processing and automatic knowledge graph construction. Traditional pipeline approaches [Zelenko et al, 2003;Chan and Roth, 2011] separate this task into two independent sub-tasks: entity recognition and relation classification while ignoring their intimate connections. Thus, they suffer from the error propagation problem.…”

PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction

“…Our innovation lies in formulating ASTE as a structured prediction problem. Taking motivation from similar sequenceto-sequence approaches proposed for joint entityrelation extraction (Nayak and Ng, 2020;Chen et al, 2021), semantic role labeling (Fei et al, 2021) etc., we propose PASTE, a Pointer Networkbased encoder-decoder architecture for the task of ASTE. The pointer network effectively captures the aspect-opinion interdependence while detecting their respective spans.…”

“…Long Short Memory Networks (or LSTMs) (Hochreiter and Schmidhuber, 1997) are known for their context modeling capabilities. Similar to (Nayak and Ng, 2020;Chen et al, 2021), we employ a Bidirectional LSTM (Bi-LSTM) to encode our input sentences. We use pre-trained word vectors of dimension d w to obtain the word-level features.…”