Q uali A ssistant: Extracting Qualia Structures from Texts (2022)

Abstract: In this paper, we present QualiAssistant, a free and open-source system written in Java for identification and extraction of Qualia structures from any natural language texts having many application scenarios such as argument mining or creating dictionaries. It answers the call for a Qualia bootstrapping tool with a ready-to-use system that can be gradually filled by the community with patterns in multiple languages. Qualia structures express the meaning of lexical items. They describe, e.g., of what kind the item is (formal role), what it includes (constitutive role), how it is brought about (agentive role), and what it is used for (telic role). They are also valuable for various Information Retrieval and NLP tasks. Our application requires search patterns for Qualia structures consisting of POS tag sequences as well as the dataset the user wants to search for Qualias. Samples for both are provided alongside this paper. While samples are in German, QualiAssistant can process all languages for which constituency trees can be generated and patterns are available. Our provided patterns follow a high-precision low-recall design aiming to generate automatic annotations for text mining but can be exchanged easily for other purposes. Our evaluation shows that QualiAssistant is a valuable and reliable tool for finding Qualia structures in unstructured texts.

## introduction in the field of natural language processing, knowledge bases and thesauri are often used to improve methods such as information extraction (stevenson and greenwood, 2006), question answering (choi et al., 2003), the validation of statements (hassan et al., 2017) or the generation of arguments (alshomary and wachsmuth, 2021; schiller et al., 2021). in the domain of argument retrieval, for example, there is usually a set of arguments prestored in an argument base to improve perfor-mance. given a query entered by a user, these arguments are returned in a ranking (stab et al., 2018; wachsmuth et al., 2017). when there are arguments suitable for the query, this ranking works well up to now (stab et al., 2018). as the arguments are pre-stored in the bases, they are consequently finite and cannot be used for arbitrary queries. with the help of thesauri such as wordnet, it is possible to modify existing arguments in the base to fit the query. @xcite for instance, if the user enters the query does every worker get a pension?, and the most appropriate argument in the base is each employee is eligible for pension, a system applying a thesaurus should be able to recognize, for example, that the category of employees is wider and includes workers. hence, given the appropriate context, the argument could be evolved to each worker is eligible for a pension. however, thesauri and knowledge bases are also limited because they also have only a finite number of entries, although there could be many more arguments. while they only contain given truths such as that a worker can be seen as employee, this approach could not straightforwardly determine more nuanced facts, like they occur in topics that are discussed in parliaments. for instance, given a query should i rely on state pension?, one result could be that pensions are no insurance sum that is simply paid out once foot_1 , containing valuable though very nuanced knowledge dealing with common misconceptions. in this paper we present a free and open-source tool called qualiassistant which is written in java and helps to create dictionaries, e.g., to mitigate the aforementioned problem of thesauri and knowledge bases. @xcite it is based on the work of saint-dizier (2017), who uses qualia roles -multidimensional aspects of meaning -for automatized argument mining (more on qualia roles in section 2) and it answers his call for a "bootstrapping method". one issue with his approach is that it takes a lot of time to acquire a high-quality corpus of such roles (his estimate: several months). while this manual approach facilitates quality control, it is also laborious and should be supported with automatized methods where possible. one such method has been introduced by cimiano and wenderoth (2007) in their paper on automatized creation of qualia roles, using a set of clues, corresponding syntactic patterns (see section 3), and a large corpus of web documents in english language. in this paper, we not only enhance their approach so that it becomes applicable to other languages than english (german for instance), we also provide a more extensive list consisting of 142 patterns, and a tool that allows to query arbitrary words such as pension in user provided texts in order to extract and return their qualia roles. @xcite the contributions of this paper are the following: ## foundations and related work in this section, we start by defining qualia structures and discuss related work and how it differs from our work. qualia structures were first introduced by pustejovsky (pustejovsky, 1991; pustejovsky and jezek, 2015) in 1991 as part of his 'generative lexicon' (gl) which "emerged from aristotle's notion of modes of explanation" and structures "lexical semantics knowledge in conjunction with domain knowledge" (saint-dizier, 2016). qualia roles structure multiple layers of meaning around an entity and form a knowledge repository which can be put to good use in many relevant areas in information retrieval (cimiano and wenderoth, 2007), including for reference resolution (bos et al., 1995) and query expansions (voorhees, 1994). they are also used in areas of natural language processing like argument mining, most prominently by saint-dizier (2017). pustejovsky's work is based on moravcsiks's re-interpretation of aristotle's "doctrine of four causes" as four viewpoints for understanding the meaning of lexical items (aitia) (cimiano and wenderoth, 2007; pustejovsky and jezek, 2015). in qualia structures, the meaning of a lexical item is thus divided into four roles: formal, constitutive, agentive, and telic as shown and described in more detail in ## evaluation in this section, we outline our evaluation, where we measured the performance of qualiassistant in comparison to a baseline on two datasets quantitatively and qualitatively. to this end, we involved a human annotator. ## conclusion and future work in this paper, we presented qualiassistant for finding qualia structures in texts. in our evaluation utilizing two different datasets in german and one in english, we showed that our approach works reasonably well. however, in this research, the most challenging part remains the gathering of patterns in the form of pos sequences for texts. this is an ongoing process that can take years of development to become sophisticated. we hope for the community to assist in this process by contributing to updating the patterns in our repository since qualia structures are important for many nlp tasks such as argument mining which is a growing area of research. in the future, we aim to identify qualia struc-tures on larger datasets such as wikipedia using our application, e.g., to set up knowledge bases with them, or for mining arguments. furthermore, we will investigate the development of further patterns in multiple languages as we believe that many fields of research benefit from having qualia structures with high-precision, e.g., the validation of statements. moreover, we want to develop machine learning methods that are able to find new qualia roles by making use of the automated annotations. we will also improve usability, for example by returning complete qualia objects as query response.