Open Information Extraction via Chunks (2023)

Abstract: Open Information Extraction (OIE) aims to extract relational tuples from open-domain sentences. Existing OIE systems split a sentence into tokens and recognize token spans as tuple relations and arguments. We instead propose Sentence as Chunk sequence (SaC) and recognize chunk spans as tuple relations and arguments. We argue that SaC has better properties for OIE than sentence as token sequence, and evaluate four choices of chunks (i.e., CoNLL chunks, OIA simple phrases, noun phrases, and spans from SpanOIE). Also, we propose a simple end-to-end BERT-based model, Chunk-OIE, for sentence chunking and tuple extraction on top of SaC. Chunk-OIE achieves state-of-the-art results on multiple OIE datasets, showing that SaC benefits the OIE task.

## introduction open information extraction (oie) is to extract structured tuples from unstructured open-domain text @xcite . the extracted tuples are in the form of (subject, relation, object) in the case of binary relations, and (arg 0 , relation, arg 1 , . . . , arg n ) for n-ary relations. the structured relational tuples are beneficial to many downstream tasks, such as question answering @xcite and knowledge base population @xcite . when observing benchmark oie datasets, most relations and their arguments are token spans. as a domain-independent information extraction task, oie does not specify any pre-defined extraction schema. as a result, determining the granularity or length of these text spans becomes challenging. consequently, many existing oie systems adopt tagging-based methods, such as bio 1 or similar tagging schemes, to extract tuples at the token level. recently, @xcite and @xcite propose to use open information annotation (oia) as an intermediate layer between the input sentence and oie tuples. oia represents a sentence as a graph where nodes are simple phrases, and edges connect predicate nodes and their argument nodes. by employing dataset-specific rules, these oia graphs can be transformed into oie tuples. nevertheless, accurately generating the complete oia graph for a given sentence poses a challenge. inspired by oia, we propose a novel notion of sentence as chunk sequence (sac), as an alternative intermediate layer representation. chunking, a form of shallow parsing, divides a sentence into syntactically related non-overlapping phrases, known as chunks @xcite . for instance, the simple phrases in oia can be considered as chunks (figure 1a ). to justify the adaptability of sac for oie, we also employ other chunking options including noun phrase chunks (figure 1b ) and conll chunks (figure 1c ). figure 1 shows an example sentence with different chunking schemes. subsequently, we propose chunk-oie, an end-to-end tagging-based neural oie model. chunk-oie performs multi-task learning among two subtasks: (i) to represent sentence in @xcite to extract tuples based on sac. our findings reveal that sac-based oie outperforms the traditional oie approach representing sentences as token sequences, particularly when the oie tuple relations and arguments align well with the chunks, as it is often the case. our contributions are as follows. firstly, we propose a novel notion of sentence as chunk sequence (sac) for oie. on top of sac, we further propose to simplify token-level dependency structure of sentence into chunk-level dependency structure in order to also encode chunk-level syntactic information for oie. secondly, we propose chunk-oie, an end-to-end learning model that (i) represents a sentence as a sac, and (ii) extracts tuples based on the sac. finally, experimental results show the effectiveness of chunk-oie against strong baselines. through data analysis against gold tuples, we show that chunks provide a suitable granularity of token spans for oie. recently, two kinds of neural systems have been explored, generative and tagging-based systems @xcite . generative oie systems @xcite @xcite model tuple extraction as a sequenceto-sequence generation task with copying mechanism. tagging-based oie systems @xcite @xcite tag each token as a sequence labeling task. spanoie @xcite ) uses a different approach. it enumerates all possible spans (up to a predefined length) from a sentence. after rulebased filtering, the remaining candidate spans are classified to relation, argument, or not part of a tuple. however, enumerating and filtering all possible spans for scoring is computationally expensive. early neural models typically seldom utilize syntactic structure of sentence, which was required by traditional models. recently works show that encoding explicit syntactic information benefits neu-ral oie as well. rnnoie @xcite and senseoie @xcite encode pos / dependency as additional embedding features. mgd-gnn @xcite connects words, if they are in dependency relations, in an undirected graph and applies gat as its graph encoder. robustoie @xcite uses paraphrases (with various constituency form) for more syntactically robust oie training. smile-oie @xcite incorporates heterogeneous syntactic information (constituency and dependency graphs) through gcn encoders and multi-view learning. inspired by them, we design a simple strategy to model dependency relation at the chunk level. note that chunks in sac partially reflect constituency structure as words in a chunk are syntactically related, by definition. sentence chunking. our proposed notion of sac is based on the concept of chunking. chunking is to group tokens in a sentence into syntactically related non-overlapping groups of words, i.e., chunks. sentence chunking is a well studied pre-processing step for sentence parsing. we can naturally use the off-the-shelf annotations as external knowledge to enhance oie. the earliest task of chunking was to recognize non-overlapping noun phrases @xcite as exemplified in figure 1b . then conll-2000 shared task @xcite proposed to identify other types of chunks such as verb and prepositional phrases, see figure 1c . oix and oia. @xcite propose open information expression (oix) to build oie systems. oix is to represent a sentence in an intermediate layer, so that reusable oie strategies can be developed on oix. as an implementation, they propose open information annotation (oia), which is a single-rooted directed-acyclic graph (dag) of a sentence. its basic information unit, i.e., graph node, is a simple phrase. a simple phrase is either a fixed expression or a phrase. @xcite define simple phrases to be: constant (e.g., nominal phrase), predicate (e.g., verbal phrase), and functional (e.g., wh-phrase). edges in an oia graph connect the predicate/function nodes to their arguments. @xcite extend oia by defining more simple phrase types and release an updated version of the oia dataset. the authors also propose oia@oie, including oia generator to produce oia graphs of sentences, and rule-based oie adaptors to extract tuples from oia graphs.