Standardizing Language with Word Embeddings and Language Modeling in Reports of Near Misses in Seveso Industries
Authors: Simone Bruno, Silvia Maria Ansaldi, Patrizia Agnello, Fabio Massimo Zanzotto (Università di Roma Tor Vergata, INAIL, 2019)
Topic: In-domain IR and IE

Abstract
Standardizing technical language has always been a strong necessity of the technological society. Today, Natural Language Processing as well as the widespread use of computerized document writing can give a tremendous boost in reaching the goal of standardizing technical language. In this paper, we propose two methods for standardizing language. These methods have been applied to the dataset of near misses, collected during the inspections at Major-Accident Hazard (MAH) Industries.

Introduction
Standardizing technical language has always been a strong necessity of the technological society. Artifacts, objects, measures and so on should have a clear name and a clear description in order to assure mutual understanding, which leads to the reach of important goals in building and controlling machines. However, language standardization has always the same problem: language is a social phenomenon (de Saussure, 1916). Hence, whenever a group gathers for designing or using a technical object, this group can develop a specific sub-language or just adapt the shared technical language. This adapted sub-language can then be effectively used to refer to parts of this technical object. It is sufficient that group members agree upon this language and mutual understanding occurs. Yet, the language used by the specific group may prevent others from understanding what is written. Nowadays, Natural Language Processing as well as the widespread use of computerized document writing can give a tremendous boost in reaching the goal of standardizing technical language. Language in use can be captured and then analyzed. Technical people can be invited to use a standardized dictionary with writing suggestions. This paper discusses two different methods of standardizing technical languages, which have been applied to a dataset of near misses coming from the inspections at Major-Accident Hazard (MAH) industries, also known as “Seveso” industries. The first method aims to help a standardization agency to propose the standard language for writing these reports. We proposed to analyze language in use by word embedding similarity, such that the standardization agency can propose a language that is close to the one used. The second method aims to reduce the use of unnecessary synonyms in compiling reports of near misses. In fact, using unnecessary synonyms may result in confusing the report. For this problem, we propose to use a combination of language modeling derived from the CBOW model of word2vec (Mikolov et al., 2013) along with a classical cosine similarity using word embeddings. We experimented with a dataset of anonymized reports of near misses from Seveso Industries, which INAIL has institutionally collected. The rest of the paper is organized as follows. Section 2 describes the application scenario and the dataset. Section 3 shortly reports on the models used in this study and proposes the two tasks. Section 4 reports on a preliminary analysis of the possible results of the system. Finally, Section 5 draws some conclusions and proposes further investigations.

Conclusion
Standardizing language is a need of our technological society. In this paper, we investigated the possibility of using modern NLP techniques to achieve this goal in the specific scenario of near misses in Seveso Industries. Initial results on the corpus provided by Inail are promising and leave room for further improvement. Future models should include the treatment of multi-word expressions by using compositional distributional semantic models (Zesch et al., 2013; Zanzotto et al., 2015), merge distributional and ontological models, and incorporate a clear model for compensating knowledge producers (Zanzotto, 2019).