Automatic rule generation for time expression normalization (2021)

Abstract: The understanding of time expressions includes two sub-tasks: recognition and normalization. In recent years, significant progress has been made in the recognition of time expressions while research on normalization has lagged behind. Existing SOTA normalization methods highly rely on rules or grammars designed by experts, which limits their performance on emerging corpora, such as social media texts. In this paper, we model time expression normalization as a sequence of operations to construct the normalized temporal value, and we present a novel method called ARTime, which can automatically generate normalization rules from training data without expert interventions. Specifically, ARTime automatically captures possible operation sequences from annotated data and generates normalization rules on time expressions with common surface forms. The experimental results show that ARTime can significantly surpass SOTA methods on the Tweets benchmark, and achieves competitive results with existing expert-engineered rule methods on the TempEval-3 benchmark.

## introduction temporal information plays an important role in natural language. the research community divides the understanding of time expressions into two subtasks: recognition and normalization @xcite . the first task is to annotate time expressions from free text, and the second one is to annotate the temporal values and types of the recognized time expressions. some recent research work @xcite @xcite achieved significant improvements on the recognition task comparing with classic rule-based or semantic parsing systems, while the researches on normalization have lagged behind. normalization methods often rely on expert-designed rules or grammars to model the compositional structure of time expression, which are domain-sensitive and not sufficient enough on covering emerging corpora. to avoid the performance limitation and the labor cost of manually designing rules for different corpora, we study the problem of automatically generating normalization rules from annotated data. there are some challenges to achieve this goal. firstly, the surface text forms of natural language expressions are diverse, and the normalized value of time expressions may not directly correspond to their surface text form. (e.g., both the expression "may" and "this month" could be normalized to "2021-05".) secondly, time expressions have rich semantic structures which are not explicitly reflected in their annotations. the implicitness of semantic structure makes supervised approaches hard to apply to the task of generating normalization rules. besides, the annotations in practical datasets are noisy, which challenges the robustness of data-driven methods. to achieve the goal, we regard time expression normalization as a sequence of operations to construct the normalized temporal value of specific types. we assume that the surface form of time expressions activates the corresponding normalization sequence. the normalization rules are defined as the alignment between surface form pattern and activated operation sequences, as demonstrated in example 1. section 3 will describe operations and normalization rules in details. example 1. the time expression "last october" can be normalized by the rule (pat-@xmath1:@xmath01])), where the type "instant" indicates that the normalized value should be a date or time instant, the first operation decreases the current value on year field by 1, and the second operation modifies the value on month field by the "month" variable obtained from the expression (i.e., october). we name the method for automatically generating normalization rules as artime foot_0 . artime computes the difference between the base value and the annotated value of input time expression to capture possible operation sequences, aligning the captured sequence with the surface form of the time expression to construct candidate rules. it ranks the noisy candidates by their frequency to distinguish the good rules. when applying the rules for normalization, artime attempts to dynamically search a rule composition for unmatched expressions to improve the coverage of generated rules. the whole normalization process only relies on a small set of pre-defined lexicon of temporal values (e.g, numeric values and time units), and does not need the intervention of human experts. the rest sections are organized as follows: the second section summarizes related research work. the third section introduces the representation of temporal values and time expressions in detail. the fourth section describes the framework and main components of artime. the fifth section reports the evaluation results of artime on two benchmarks. the last section concludes this paper. ## related work understanding time expressions in natural language has long attracted the attention of researchers. the tides research program proposed timex @xcite and timex2 @xcite , which are standalone annotation schemes of time expression with detailed descriptions of temporal values. the terqas workshops conceptualized timeml @xcite based on timex and timex2. timeml became an iso standard in 2009. @xcite pointed out that the classic annotation schemes failed to show the semantic composition structure of of time expressions and proposed the semantically compositional annotation of time expressions (scate). however, applying scate to existing corpus requires to manually re-annotate the expressions in a more complex way, and many of the existing sota methods can not handle annotation in scate format directly @xcite . on the recognition of time expression, an early study shows that the complexity of time expressions is limited, and finite state automata or regex expression can be effective for recognizing those expressions @xcite . mainstream recognition methods can be roughly divided to surface-structure-based methods @xcite @xcite @xcite @xcite and sequential-tagging-model-based methods @xcite @xcite . research work in recent years achieves significant improvements on the recognition. syntime @xcite defines generic but heuristic rules on a group of time-related triggering token types. tomn @xcite uses the syntime defined token types instead of the classic bio-tagging scheme for the crf model. ptime @xcite generalizes time expressions in training data to sequential patterns and selects a subset of the patterns for recognition. however, these studies only focus on the recognition. the normalization of time expression is dominated by methods with expert designed rules or grammars. heideltime @xcite @xcite uses regex rules on time tokens and modifiers to combine recognized tokens and filter ambiguous expressions. sutime @xcite proposes a 3-layered temporal pattern language. it firstly extends recognized tokens to string, then composes and filters the strings to get temporal values. angeli and uszkoreit (2013) use an em-style bootstrapping approach to learn a pcfg parser on pre-defined preternminals. uwtime @xcite uses a combinatory categorical grammar to parse possible meanings of time expressions. it selects meanings for recognized expressions via a linear classifier with context-dependent features. cogcomptime @xcite provides a rule-based standalone normalizer conceptually built on @xcite , which achieves the sota normalization results on the (uzzaman et al., 2013) dataset. there are also some efforts on understanding event-related expressions. @xcite analyzes time expressions in clinical notes. tweettime @xcite improves existing methods by establishing an external event knowledge base. according to existing studies @xcite , rule-engineering can achieve good results on covered expressions but are hard to extend to emerging corpora. in this paper, we focus on automatically recover-ing the semantic structure of expressions without any compositional annotations. the latest work on recognition inspired our idea of using surface form patterns to activate normalization rules, and we replace the labor cost of designing rules by the automatic rule generation. ## time expression normalization as a sequence of operations we model the normalization of a time expression as a sequence of operations defined on time fields, which can construct a temporal value of specific type. the normalization rule is defined as a triplet consists of a surface form pattern, a type of temporal value, and an operation sequence. the following subsections introduce the above concepts. ## framework of artime figure 1 illustrates the normalization process of artime. the pre-processing step is adopted from the corresponding components in ptime. the rest normalization procedures can be divided into two parts, 1) generating rules (i.e., the left part of figure 1 ) and 2) applying the generated rules (i.e. the right part of figure 1 ). since the timeml standard does not annotate the base value of each time expression, we simply use the document creation time as a substitute in capturing the possible operation sequences. the following sub-sections describe the key techniques in artime. section 4.1 details how to capture possible operation sequences. section 4.2 describes how to generate rules from the noisy results. section 4.3 describes how to use the generated rules to normalize input time expressions. ## conclusion in this paper, we mainly focus on automatically generating rules for time expression normalization. the main contributions of this paper are summarized as follows: there are still some rooms to improve artime. one of the future work is to generate more highquality rules. the other is to enable artime to use the tense and event information in context.