Normalization Of Temporal Expressions Research Articles

Automatic Normalization of Temporal Expressions

Dates, periods and timespans are described in archaeological datasets using a number of different textual patterns for which myriad variations exist, rendering direct automated comparison difficult. The issue can occur even within records from the same dataset and is further compounded when attempting to integrate multilingual data – particularly where dates may be expressed in words rather than numbers. The same problem can be found in temporal metadata, whether manually entered or generated via Natural Language Processing (NLP) techniques from reports and grey literature. Resolving and normalizing dates and periods to internationally agreed standard formats enables efficient data integration, interchange, search, comparison and visualization. This paper reports on the design and implementation of a tool to normalize temporal expressions to a numerical time axis and reflects on key issues. Textual patterns for seven categories of temporal expression have been normalized: Ordinal named or numbered centuries; Year spans; Single year (with tolerance); Decades; Century spans; Single year with prefix; Named periods. The following languages are currently supported: Dutch, English, French, German, Italian, Norwegian, Spanish, Swedish, Welsh. Methods are described together with an (open source) normalization tool developed in Python and four applications of the method are discussed, together with limitations and future work. Results are presented from diverse data sets and languages. The input is a temporal text string and a language code (ISO639-1). The output is a tab delimited text file with start/end years (in ISO 8601 format), relative to Common Era (CE). The normalized outputs are provided as additional attributes along with the original text expression for consuming software to employ in end-user applications.

Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts

Background and ObjectiveWith the rapid development of information dissemination technology, the amount of events information contained in massive texts now far exceeds the intuitive cognition of humans, and it is hard to understand the progress of events in order of time. Temporal information runs through the whole process of beginning, proceeding, and ending of events, and plays an important role in many natural language processing applications, such as information extraction, question answering, and text summary. Accurately extracting temporal information from Chinese texts and automatically mapping the temporal expressions in natural language to the time axis are crucial to understanding the development of events and dynamic changes in them. MethodsThis study proposes a method integrating machine learning with linguistic features (IMLLF) for extraction and normalization of temporal expressions in Chinese texts to achieve the above objectives. Linguistic features are constructed by analyzing the expression rules of temporal information, and are combined with machine learning to map the natural language form of time onto a one-dimensional timeline. The web text dataset we build is divided into five parts for five-fold cross-validation, to compare the influence of different combinations of linguistic features and different methods. In the open medical dialog dataset, based on the training model obtained from the web text dataset, 200 disease descriptions are randomly selected each time for three rounds of experiments. ResultsThe F1 of multi-feature fusion is 95.2%, which is better than the single-feature and double-feature combination. The results of experiments showed that the proposed IMLLF method can improve the accuracy of recognition of temporal information in Chinese to a greater extent than classical methods, with an F1-score of over 95% on the web text dataset and medical conversation dataset. In terms of the normalization of time expressions, the accuracy of the IMLLF method is higher than 93%. ConclusionsIMLLF has better results in extracting and normalizing time expressions on the web text dataset and the medical conversation dataset, which verifies the universality of IMLLF to identify and quantify temporal information. IMLLF method can accurately map the time information to the time axis, which is convenient for doctors to intuitively see when and what happened to the patient, and helps to make better medical decisions.

Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach.

BackgroundTemporal information frequently exists in the representation of the disease progress, prescription, medication, surgery progress, or discharge summary in narrative clinical text. The accurate extraction and normalization of temporal expressions can positively boost the analysis and understanding of narrative clinical texts to promote clinical research and practice.ObjectiveThe goal of the study was to propose a novel approach for extracting and normalizing temporal expressions from Chinese narrative clinical text.MethodsTNorm, a rule-based and pattern learning-based approach, has been developed for automatic temporal expression extraction and normalization from unstructured Chinese clinical text data. TNorm consists of three stages: extraction, classification, and normalization. It applies a set of heuristic rules and automatically generated patterns for temporal expression identification and extraction of clinical texts. Then, it collects the features of extracted temporal expressions for temporal type prediction and classification by using machine learning algorithms. Finally, the features are combined with the rule-based and a pattern learning-based approach to normalize the extracted temporal expressions.ResultsThe evaluation dataset is a set of narrative clinical texts in Chinese containing 1459 discharge summaries of a domestic Grade A Class 3 hospital. The results show that TNorm, combined with temporal expressions extraction and temporal types prediction, achieves a precision of 0.8491, a recall of 0.8328, and a F1 score of 0.8409 in temporal expressions normalization.ConclusionsThis study illustrates an automatic approach, TNorm, that extracts and normalizes temporal expression from Chinese narrative clinical texts. TNorm was evaluated on the basis of discharge summary data, and results demonstrate its effectiveness on temporal expression normalization.

Recognition and Normalization of Temporal Expressions in Serbian Medical Narratives

Recognition and Normalization of Temporal Expressions in Serbian Medical Narratives

Discovering topic time from web news

Topic time reflects the temporal feature of topics in Web news pages, which can be used to establish and analyze topic models for many time-sensitive text mining tasks. However, there are two critical challenges in discovering topic time from Web news pages. The first issue is how to normalize different kinds of temporal expressions within a Web news page, e.g., explicit and implicit temporal expressions, into a unified representation framework. The second issue is how to determine the right topic time for topics in Web news. Aiming at solving these two problems, we propose a systematic framework for discovering topic time from Web news. In particular, for the first issue, we propose a new approach that can effectively determine the appropriate referential time for implicit temporal expressions and further present an effective defuzzification algorithm to find the right explanation for a fuzzy temporal expression. For the second issue, we propose a relation model to describe the relationship between news topics and topic time. Based on this model, we design a new algorithm to extract topic time from Web news. We build a prototype system called Topic Time Parser (TTP) and conduct extensive experiments to measure the effectiveness of our proposal. The results suggest that our proposal is effective in both temporal expression normalization and topic time extraction.

Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives

Identification of clinical events (eg, problems, tests, treatments) and associated temporal expressions (eg, dates and times) are key tasks in extracting and managing data from electronic health records. As part of the i2b2 2012 Natural Language Processing for Clinical Data challenge, we developed and evaluated a system to automatically extract temporal expressions and events from clinical narratives. The extracted temporal expressions were additionally normalized by assigning type, value, and modifier. The system combines rule-based and machine learning approaches that rely on morphological, lexical, syntactic, semantic, and domain-specific features. Rule-based components were designed to handle the recognition and normalization of temporal expressions, while conditional random fields models were trained for event and temporal recognition. The system achieved micro F scores of 90% for the extraction of temporal expressions and 87% for clinical event extraction. The normalization component for temporal expressions achieved accuracies of 84.73% (expression's type), 70.44% (value), and 82.75% (modifier). Compared to the initial agreement between human annotators (87-89%), the system provided comparable performance for both event and temporal expression mining. While (lenient) identification of such mentions is achievable, finding the exact boundaries proved challenging. The system provides a state-of-the-art method that can be used to support automated identification of mentions of clinical events and temporal expressions in narratives either to support the manual review process or as a part of a large-scale processing of electronic health databases.

Multilingual and cross-domain temporal tagging

Extraction and normalization of temporal expressions from documents are important steps towards deep text understanding and a prerequisite for many NLP tasks such as information extraction, question answering, and document summarization. There are different ways to express (the same) temporal information in documents. However, after identifying temporal expressions, they can be normalized according to some standard format. This allows the usage of temporal information in a term- and language-independent way. In this paper, we describe the challenges of temporal tagging in different domains, give an overview of existing annotated corpora, and survey existing approaches for temporal tagging. Finally, we present our publicly available temporal tagger HeidelTime, which is easily extensible to further languages due to its strict separation of source code and language resources like patterns and rules. We present a broad evaluation on multiple languages and domains on existing corpora as well as on a newly created corpus for a language/domain combination for which no annotated corpus has been available so far.

Normalization of Temporal Expressions in French: A Preliminary Study

Normalization of Temporal Expressions in French: A Preliminary Study