A Data-Driven Semi-Automatic Framenet Development Methodology (2021)

Abstract: FrameNet is a lexical semantic resource based on the linguistic theory of frame semantics. A number of framenet development strategies have been reported previously and all of them involve exploration of corpora and a fair amount of manual work. Despite previous efforts, there does not exist a well-thought-out automatic/semi-automatic methodology for frame construction. In this paper we propose a data-driven methodology for identification and semi-automatic construction of frames. As a proof of concept, we report on our initial attempts to build a wider-scale framenet for the legal domain (LawFN) using the proposed methodology. The constructed frames are stored in a lexical database and together with the annotated example sentences they have been made available through a web interface.

## introduction frame semantics is a theory of meaning in natural languages proposed by charles fillmore @xcite . the theory stipulates that meanings of words can be best understood with reference to the situations they invoke in the minds of the speakers. the concrete manifestation of frame semantics is the computational lexical resource framenet, first constructed within the english berkeley framenet (bnf) project @xcite . the resource has inspired work on framenets for many other languages and domains, including chinese @xcite , french @xcite , german @xcite , @xcite , korean @xcite , italian @xcite , japanese @xcite , @xcite , spanish @xcite , and swedish @xcite . framenet is built around the notion of frames. a frame in this context is a schematic representations of events, objects, situations, institutions, etc. the primary components of a frame are the frame definition along with the frame elements and the lexical units documented with corpus evidence @xcite . there are at least three major framenet development strategies that have been reported in the literature @xcite namely: (1) lexicographic frame-by-frame (2) corpus driven lemmaby-lemma (3) the full-text strategy. of these three, the most commonly applied strategy is frame-byframe, where the frame is defined along with its frame-elements first. example sentences are then chosen from a corpus for a disambiguated annotation of lexical units and frame-elements. in the lemma-by-lemma strategy, a set of lemmas (lexical units) are chosen first and then all their occurrences are annotated in a given corpus together with the annotation of frame-elements. in the full-text strategy, all content words in a given text are annotated. the latter two strategies pre-suppose that frames already exist, although new frames can also be developed as new senses of lexical units are encountered in the text. all three strategies involve exploration of corpora and a fair amount of manual work for construction of semantic frames, and for searching suitable example sentences to be annotated. various framenet development projects have reported the use of (or developed) tools to assist in the corpus exploration and frames construction parts (e.g. the swedish framenet++ project; @xcite , and there also exist guide-lines for frame construction @xcite . however, there is no well-thought-out automatic/semi-automatic procedure for frame development from scratch. in this study, we propose a data-driven method for the identification and semi-automatic construction of domain-specific semantic frames. the identification involves recognizing the domain-specific events, entities, relations, procedures, etc. for which the semantic frames can be constructed. the development part involves discovering various semantic roles of a given frame, spotting and linking lexical units to the target frame, and annotating example sentences. ## data and pre-processing as a proof of concept of our methodology, we prepared a small data-set by collecting a set of documents from the legal domain and applied ocr processing to produce a machine readable version. the data was then enriched with metadata and linguistic annotations. it was then stored and made accessible through a corpus infrastructure tool for easy excess and exploration. the data used in this study was downloaded from web repository of the united nations high commissioner for refugees. 1 a subset of 900 pdf documents resulting from the hits for the search string "well-founded fear of being persecuted" 2 were downloaded and then ocred using abbyy finereader software. 3 each document was processed for the following document, token, and structure level attributes. document level attributes were preserved while downloading the documents, and for various token and structure level annotations we used sparv @xcite , which is an annotation pipeline 1 www.refworld.org 2 this search string was chosen in connection to another part of the project with its own objectives of analyzing the ill-treatment of refugees. ## frame development methodology a step-wise description of the frame development methodology is given below. this is followed by construction of an example frame using the described methodology. the developed frame is a part of the legal domain framenet (more details about the legal domain framenet in section 4). 1. as a first step, the word segmented data reported in section 2 is sorted in the descending order of term foot_1 frequencies after removing the function words. the purpose is to identify the most frequently used terms and cover them first while designing the frames. lets now walk through the construction of an example frame article using the above described methodology. in our data collection, the term 'article' occurred 29 times, and it is easy to recognize that this term has a legal domain sense in addition to four others as per wordnet. considering that no frames have been designed previously, we need to construct a frame for which the word 'article' will be a lexical unit. this is step 2 of our methodology. we chose the name article for the frame, and move towards construction of its structure i.e. step 3. a manual inspection of the text segments under the 'dependent' for the 'dobj' relation reveals that the word 'article' whenever used in the legal domain is often followed 6 this is basically a linguistic decision which requires both experience of framenet development and training in grammatical and lexical-semantic analysis for the language in question ([legal] english in our case). see @xcite for a more detailed discussion of the considerations and reasoning involved. 7 the constituents in question are not direct objects; rather, the relation should be labeled 'flat' or possibly 'appos' under the ud scheme. this does not matter to our example, but it will of course become important when we will be looking to automate the discovery of frame elements further. by a figure or figures that refers to the article number of the constitution/agreement. this means we need a frame-element to capture and represent this usage. it can perhaps be argued that it is so obvious that the term 'article' will be followed by an article number so why we need to follow the described procedure. it may appear obvious in this case, but it can not be generalized across frames and frame-elements (imagine the construction of the frame injunction which refers to a judicial order restraining a person from beginning or continuing an action). our experience suggests that this usage based methodology is very helpful in identifying the frames and frame-elements. similarly, a manual inspection of the text segments under the 'dependent' for 'nmod' relation revealed that there is often a mention of a law/protocol/constitution to which the article belongs e.g 'of the turkish constitution'. this means we need a frame-element to capture that information, hence, the frame-element constitution. the same procedure was followed to design the frame-elements inter-pretation, and date while inspecting the 'acl:relcl' (i.e. relative clause modifier) and 'nmod:tmod' (i.e. temporal modifier) relations respectively. for the actual frame construction and storage, we used karp, which is another lexical infrastructure tool developed and maintained at språkbanken. figure 2 shows a screenshot of the tool and the structure of the article frame. it also shows a set of annotated examples and the lexical units which can trigger this frame. ## conclusions and future work our contribution is twofold. first, we have reported a semi-automatic frame-development methodology, which can help speed up the frame development process. second, we have reported initial attempts in building a framenet for the legal domain and annotated data as lexico-semantic resources. in the future, we have plans to continue constructing more frames. our dataset is quite limited in scope and size, which we plan to extend in the near future. with the bigger data set, we will be able to explore more syntactic patterns. this means capturing more possible use cases, and hence being in a position to better design the frames. also, we have plans to annotate data with the developed frames, and build a frame-semantic parser exploiting machine learning. the developed parser then can be used for automatic semantic analysis of legal domain documents, which then can be used to extract particular types of information from those document. as an example, suppose one is interested to extract all judgements made by a particular judge under a particular law/protocol/agreement from a corpus of court decisions. parsing the corpus for judgement, law, and article frames can help extract that information automatically. evaluation of the extracted information for accuracy is another direction that we plan to explore in the future.