Semantic parsing with fuzzy meaning representations (2020)

Abstract: We propose an approach and a software framework for semantic parsing of natural language sentences to discourse representation structures with use of fuzzy meaning representations such as fuzzy sets and compatibility intervals. We explain the motivation for using fuzzy meaning representations in semantic parsing and describe the design of the proposed approach and the software framework, discussing various examples. We argue that the use of fuzzy meaning representations have potential to improve understanding and reasoning capabilities of systems working with natural language.

## introduction the meaning representation based on fuzzy sets was first proposed @xcite . one of the very interesting properties of this representation is allowing to quantitatively describe relations between different concepts (e.g. "young"/"age", "common"/"surprisingness", "seldom"/"frequency"), as well as represent vagueness and imprecision that are so common to natural language. we recently proposed a related meaning representation, compatibility intervals, that, instead of using membership functions, describes similar relations using several intervals on a certain scale @xcite . fuzzy meaning representations are relatively little known among linguists, and little used in natural language processing @xcite . we believe that this is well explained by the fact that these rich interpretations are generally not easy to learn from data, compared to the representations like word embeddings that can be derived from text. however, we believe that due to their expressiveness these representations have a huge potential when it comes to understanding and reasoning capabilities of the systems working with natural language. they allow to quantitatively express differences between similar language constructs (synonyms/antonyms, stronger/weaker constructs, wider/narrower constructs, etc.), making this knowledge available to the systems. in addition, compared to more opaque representations like word embeddings, they are also linguistically interpretable, potentially allowing to build the systems whose behaviour can be easier analyzed and explained. we would like to contribute to the knowledge of using fuzzy meaning representations in natural language understanding, in particular, in semantic parsing. in this introductory paper we describe an approach and a software framework for semantic parsing of natural language sentences to discourse representation structures (drs) with the use of fuzzy meaning representations. first, we use these representations to enrich the resulting drs with certain types of semantic information that can be helpful for downstream applications. in particular, both meaning similarity and vagueness can in many cases be directly represented in the drs with the use of fuzzy meaning representations. for example, in the suggested approach, the constructs "this was not completely expected" and "this was fairly surprising" should lead to similar parses, expressing the meaning of the words "expected" and "surprising" in terms of the same underlying properties. constructs "not completely" and "fairly" are modeled as modifiers acting on the words "expected" and "surprising" and transforming their underlying representations (either the fuzzy sets or the compatibility intervals). this allows the systems to analyze the similarity of the constructs computationally. second, fuzzy meaning representations also play a key role during the semantic parsing, as they are used for the assessment of the understanding level and scoring of the different interpretations. ## approach the presented approach and the framework are largely based on the idea of exploring what various language constructs (e.g. "often") tell us about possible values of different properties (e.g. "frequency"). we don't know about a general way of deriving such knowledge from texts, however, see e.g. runkler (2016). for this reason, our approach is dependent on having a predefined lexicon containing rather rich semantic information. currently, the lexicon is defined manually. while we are planning to work on obtaining some parts of this lexicon in an automated way and making the framework work with a broader lexicon, we consider it to be a different research topic. our main interest is studying whether such information may improve understanding and reasoning capabilities of natural language understanding systems in general and semantic parsing systems in particular. relying on a rich semantic lexicon has other important implications on the design of the system. we are trying to make use of this information in the best possible way, as it may help to guide all the tasks that are part of the semantic parsing (e.g. syntactic parsing, handling multi-word expressions, choosing relevant word meaning, etc.). also, we believe that any decision made during the natural language understanding process may need to be reversed as a result of the information obtained later in the process. for example, first we may parse "a few" as a multi-word expression, but later we may have to reconsider it, because we cannot arrive at any satisfactory interpretations that are based on that decision. for these reasons, rather than viewing the system as a pipeline that makes use of standard components working with different tasks, we design it as a single process that may take input from other specialized systems. currently, the framework does not yet integrate with other systems, handling main tasks of the semantic parsing internally. given a lexicon, the framework parses natural language sentences to a variant of scoped drs representation, loosely following @xcite . we extend the drs to represent fuzziness by using regions, see also @xcite . regions define which values of a certain property (e.g. "age", "surprisingness", "frequency") are compatible with a certain word or utterance by the means of either fuzzy sets or compatibility intervals. composition of meaning in a sentence is modeled by the application of word-operators to words-operands, and whether a word-operator may accept a particular word as an operand is based on various syntactic and semantic tags. the framework processes words in the order they appear in the input sentence, performing a series of transitions corresponding to the decisions made during the semantic parsing. as several alternative transitions are often possible, transitions form a search tree. paths in this tree correspond to different interpretations of the sentence, and each parse is assigned a score. finally, to find the best interpretation of the sentence, we traverse the tree, taking the scores into account. the framework is written in haskell and is under active development. while it is not open source software at the moment, we encourage everyone interested in the framework to contact the authors. in this paper we describe our approach in general, and the framework in its current state. first, we briefly describe the main components of the framework, and then discuss the role that fuzzy meaning representations play in the framework. ## discussion one of the primary design goals for the approach and the framework is extensibility, as only a little part of the lexicon can be defined manually. short-term, we are looking at using external sources and machine learning techniques to obtain semantic tags for the words not present in the lexicon. long-term, we are planning to use various knowledge bases, synonym dictionaries and machine learning techniques to let the system learn more words. at the same time, we would like to emphasize that our main research goal is exploring how natural language understanding systems in general and semantic parsers in particular may benefit from using rich quantitative meaning representations, focusing somewhat less on obtaining these representations automatically at this point. we know how to automatically learn some fuzzy sets, see e.g. runkler (2016), and maybe we can find out how to learn more. we would like to study whether we should, and what understanding and reasoning capabilities this may facilitate, compared to what is supported by easily learnable but not easily linguistically interpretable representations like word embeddings. as a reviewer pointed out, it is also interesting to compare fuzzy meaning representations to universal decompositional semantics @xcite in terms of the aspects of meaning they can express. universal decompositional semantics is another quantitative representation and annotation scheme that uses continuous scales and is based on linguistic theory. while it is very different from fuzzy sets and compatibility intervals, we think it is worthwhile to explore how these representations could be used together to model different aspects of semantics. we believe that fuzzy meaning representations may improve capabilities of the systems working with natural language as they provide linguistically interpretable projections @xcite of the construct meanings onto certain properties. these may allow natural language understanding systems to interpret meanings of the words in a quantitative way, allowing to analyze and compare them with higher "precision". for example, instead of just knowing that the words are similar, the system may analyze in details how they are similar or different, e.g which word is wider, which one is more polar, which situations can be described by both words and which -only by one of them. seeing whether natural language understanding systems can actually use some of the described ideas and techniques, and being able to evaluate this in practice will require more research, and we hope that our approach and the software framework can be a useful contribution to the study of this complex field.