Neural Semantic Role Labeling using Verb Sense Disambiguation
Authors: Domenico Alfano, Roberto Abbruzzese, Donato Cappetta (Eustema S.p.A., 2019)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The Natural Language Processing (NLP) community has recently experienced a growing interest in Semantic Role Labeling (SRL). The increased availability of annotated resources enables the development of statistical approaches specifically for SRL. This holds potential impact in NLP applications. We examine and reproduce Marcheggiani's system and its individual components, including its annotated resources, parser, classification system, the features used and the results obtained by the system. Then, we explore different solutions in order to achieve better results by approaching Verb-Sense Disambiguation (VSD). VSD is a sub-problem of the Word Sense Disambiguation (WSD) problem, that tries to identify in which sense a polysemic word is used in a given sentence. Thus a sense inventory for each word (or lemma) must be used. Finally, we also assess the challenges in SRL and identify the opportunities for useful further research in the future.

Introduction
One of the fields where AI is gaining great importance is NLP. Nowadays, NLP has many applications: search engines (semantic/topic search rather than word matching), automated speech translation, automatic summarization, etc. Therefore, there are many sub-tasks for natural language applications that have already been studied. An example is the syntactic analysis of the words of a sentence. The object of this research study is the realization of a system able to perform SRL. A SRL system does nothing more than take a set of input phrases and, for each of them, it starts to determine the various components that could play a semantic role. A component of a proposition that plays a semantic role is defined as a constituent. Once the possible candidates are determined, Machine Learning techniques are used to label them with the right role. This task becomes important for advanced applications where it is also necessary to process the semantic meaning of a sentence. Moreover, all these applications have to deal with ambiguity. Ambiguity is the term used to describe the fact that a certain expression can be interpreted in more than one way. In NLP, ambiguity is present at several stages in the processing of a text or a sentence, such as: tokenization, sentence-splitting, part-of-speech (POS) tagging, syntactic parsing, and semantic processing. Semantic ambiguity is usually the last to be addressed by NLP systems, and it tends to be one of the hardest to solve among all types of ambiguities mentioned. For this type of ambiguity, the sentence has already been parsed and, even if its syntactic analysis (parse tree) is unique and correct, some words may feature more than one meaning for the grammatical category they were tagged with. Usually, this difference in meaning is associated with syntactic properties. In order to overcome these issues, this research study approaches the VSD task. The majority of the systems used in the VSD task are based on Machine Learning techniques (Witten, 2011). We approach both tasks by following two different solutions.

Conclusion
The realized work represents the development of a complete system for Semantic Role Labeling, an important tool to be used in advanced Natural Language Processing applications. A system of SRL alone is not very useful and it necessarily must be included in a wider application, for example a Question&Answering system or a Neural Machine Translation system. In conclusion, as all the new applications of natural language processing must be able to handle semantic information if they want to have good performances, this type of system can be considered a valuable solution to achieve such performances. The statistical analysis of the errors registered by the system, developing from this analysis new algorithms in order to correct such errors, is another aspect to be considered in the evaluation of this system.