Text Frame Detector: Slot Filling Based On Domain Knowledge Bases
Authors: Martina Miliani, Lucia C. Passaro, Alessandro Lenci (Università di Pisa, 2019)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this paper we present a system called Text Frame Detector (TFD) which aims at populating a frame-based ontology in a graph-based structure. Our system organizes textual information into frames, according to a predefined set of semantically informed patterns linking pre-coded information such as named entities, simple and complex terms. Given the semiautomatic expansion of such information with word embeddings, the system can be easily adapted to new domains.

Introduction
Textual data are still the most widespread content around the Web (Smirnova and Cudré-Mauroux, 2018). Information Extraction (IE) is a key task to structure textual information and make it machine understandable. IE can be modelled as the process of filling semantic frames specified within a domain ontology and consisting of a collection of slots typed with their possible values (Minsky, 1974; Jurafsky and Martin, 2018). Therefore, each frame can be seen as a set of relations whose participants are the values of the slots. Following Jean-Louis et al. (2011), we refer to such relations as complex relations, namely any n-ary relation among typed entities. Relation extraction techniques have been widely applied to populate semantic frames (Surdeanu, 2013; Zhenjun et al., 2017). However, both supervised and unsupervised methods have shown their limits. On the one hand, supervised approaches (Zelenko et al., 2003; Mooney and Bunescu, 2005; Nguyen and Grishman, 2015; Zhang et al., 2017) model frame filling as a classification task, hence they require labelled data, with the consequent high cost of long annotation time. On the other hand, unsupervised approaches do not need any training data, but mapping extraction results onto predefined relations or ontologies is often quite challenging with this kind of methods (Fader et al., 2011). Moreover, semi-supervised methods exploit bootstrap learning, so that any new relation requires a small set of labelled data to be extracted (Agichtein and Gravano, 2000; Chen et al., 2006; Weld et al., 2008). Finally, another kind of approach has been proposed, which relies on knowledge bases (KBs) to produce training data. Introduced by Mintz et al. (2009), distant supervision detects relations on semantically annotated texts where entities which co-occur in the same sentence match with entitypairs contained in the KB. Then a classifier is trained using features extracted from the annotated relations (Smirnova and Cudré-Mauroux, 2018). Although this approach has been proven to be effective, the supervised step could suffer from scarce amount of data, especially if the relations occur with low frequency in small corpora. In this paper, we present a system to populate a frame-based ontology, whose values are stored in a graph-based structure. Our method exploits some aspects of distant supervision, leveraging on domain specific KB to infer the relations, and populates the frames with specific information (i.e., the participants) as well as the portions of text (i.e., the snippets) which contain them. Thus, the output of the system for a single frame is a set of snippets, one for each of its slots. Each snippet is also associated with a weight encoding how likely it is expected to contain the information about a certain relation. Such a weight is calculated with a scoring function based on similarity measures and textual distance information. The system has been tested on the administrative domain, with the goal of gathering information related to taxes and agenda events. Indeed, since the KB can be semi-automatically enriched with Named Entities (NEs) and vocabularies of simple and complex terms, our approach can be easily adapted to different domains. Furthermore, system recall can be increased by expanding the frame and attribute vocabulary by exploiting word embeddings (Mikolov et al., 2013). Our approach differs from existing systems like PIKES (Concoglioniti et al., 2016), Framester (Gangemi et al., 2016), FRED (Gangemi et al., 2017), and Framebase (Rouces et al., 2015) primarily for the notion of semantic frame we have adopted. The works above are mainly based on Fillmore’s (1976) definition of frame as encoded in FrameNet: frames and associated roles describe situations evoked by lexical expressions (i.e. Lexical Units). In our system a frame represents a domain entity (e.g. “tax”) by means of attributes and relations associated to that domain. Unlike FrameNet frames, these attributes and relations are activated by a set of distributed lexico-syntactic cues. This paper is structured as follows: in section 2 we describe the general methodology of the system, we define terminology and notation and we describe the main features of the proposed approach. The system implementation is illustrated in section 3, which shows the extraction algorithm as well as the indexing methods in the knowledge graph. Evaluation and results are reported in section 4.

Conclusion
In this paper we presented a domain independent system for slot filling that exploits a graph to populate a frame-based ontology. The Text Frame Detector extracts a relevant snippet for each frame attribute from textual information with good results in terms of F1 score (0.796). Nonetheless, the evaluation showed that there is room for improvement in some of the TFD modules. For example, the annotation of the semantic neighborhood of single and multiword terms, which are particularly relevant in technical domains, should led to further improve recall performances for complex attributes. Moreover, although we did not adopted Fillmore’s semantic frames in the present work, we would like to explore the possibility of integrating our domain frames with FrameNet ones, which might contribute to enhance the system flexibility. Finally, in the near future, we plan to finetune parameters and to implement additional features such as to associate multiple snippets to the same attribute. Furhermore, we intend to convert the binary features used in the snippet selection system into continuous weights. These weights, along with the collected data about frame population, would be also employed to train a supervised model for slot filling, in order to test TFD across new domains.