Multilingual extension of a temporal expression normalizer using annotated corpora

This paper presents the automatic extension to other languages of TERSEO, a knowledge-based system for the recognition and normalization of temporal expressions originally developed for Spanish. TERSEO was first extended to English through the automatic translation of the temporal expressions. Then, an improved porting process was applied to Italian, where the automatic translation of the temporal expressions from English and from Spanish was combined with the extraction of new expressions from an Italian annotated corpus. Experimental results demonstrate how, while still adhering to the rule-based paradigm, the development of automatic rule translation procedures allowed us to minimize the effort required for porting to new languages. Relying on such procedures, and without any manual effort or previous knowledge of the target language, TERSEO recognizes and normalizes temporal expressions in Italian with good results (72% precision and 83% recall for recognition).This research was partially funded by the Spanish Government (contract TIC2003-07158-C04-01

A geo-temporal information extraction service for processing descriptive metadata in digital libraries

In the context of digital map libraries, resources are usually described according to metadata records that define the relevant subject, location, time-span, format and keywords. On what concerns locations and time-spans, metadata records are often incomplete or they provide information in a way that is not machine-understandable (e.g. textual descriptions). This paper presents techniques for extracting geotemporal information from text, using relatively simple text mining methods that leverage on a Web gazetteer service. The idea is to go from human-made geotemporal referencing (i.e. using place and period names in textual expressions) into geo-spatial coordinates and time-spans. A prototype system, implementing the proposed methods, is described in detail. Experimental results demonstrate the efficiency and accuracy of the proposed approaches

Incorporating TimeML into a GIS

This study approaches a methodology for the integration of temporal information belonging to a historical corpus in a Geographic Information System (GIS), with the purpose of analyzing and visualizing the textual information. The selected corpus is composed of business letters of the Castilian merchant Simón Ruiz (1553-1597), in the context of the DynCoopNet Project (Dynamic Complexity of Cooperation-Based Self-Organizing Commercial Networks in the First Global Age), that aims to analyze the dynamic cooperation procedures of social networks. The integration of historical corpus into a GIS has involved the following phases: (1) recognition and normalization of temporal expressions and events in 16th century Castilian following the TimeML annotation guidelines and (2) storage of tagged expressions into a Geodatabase. The implementation of this process in a GIS would allow to later carrying out temporal queries, dynamic visualization of historical events and thus, it addresses the recognition of human activity patterns and behaviours over tim

Design of a Controlled Language for Critical Infrastructures Protection

We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen

An Urdu semantic tagger - lexicons, corpora, methods and tools

Extracting and analysing meaning-related information from natural language data has attracted the attention of researchers in various fields, such as Natural Language Processing (NLP), corpus linguistics, data sciences, etc. An important aspect of such automatic information extraction and analysis is the semantic annotation of language data using semantic annotation tool (a.k.a semantic tagger). Generally, different semantic annotation tools have been designed to carry out various levels of semantic annotations, for instance, sentiment analysis, word sense disambiguation, content analysis, semantic role labelling, etc. These semantic annotation tools identify or tag partial core semantic information of language data, moreover, they tend to be applicable only for English and other European languages. A semantic annotation tool that can annotate semantic senses of all lexical units (words) is still desirable for the Urdu language based on USAS (the UCREL Semantic Analysis System) semantic taxonomy, in order to provide comprehensive semantic analysis of Urdu language text. This research work report on the development of an Urdu semantic tagging tool and discuss challenging issues which have been faced in this Ph.D. research work. Since standard NLP pipeline tools are not widely available for Urdu, alongside the Urdu semantic tagger a suite of newly developed tools have been created: sentence tokenizer, word tokenizer and part-of-speech tagger. Results for these proposed tools are as follows: word tokenizer reports $F_1$ of 94.01\%, and accuracy of 97.21\%, sentence tokenizer shows F$_1$ of 92.59\%, and accuracy of 93.15\%, whereas, POS tagger shows an accuracy of 95.14\%. The Urdu semantic tagger incorporates semantic resources (lexicon and corpora) as well as semantic field disambiguation methods. In terms of novelty, the NLP pre-processing tools are developed either using rule-based, statistical, or hybrid techniques. Furthermore, all semantic lexicons have been developed using a novel combination of automatic or semi-automatic approaches: mapping, crowdsourcing, statistical machine translation, GIZA++, word embeddings, and named entity. A large multi-target annotated corpus is also constructed using a semi-automatic approach to test accuracy of the Urdu semantic tagger, proposed corpus is also used to train and test supervised multi-target Machine Learning classifiers. The results show that Random k-labEL Disjoint Pruned Sets and Classifier Chain multi-target classifiers outperform all other classifiers on the proposed corpus with a Hamming Loss of 0.06\% and Accuracy of 0.94\%. The best lexical coverage of 88.59\%, 99.63\%, 96.71\% and 89.63\% are obtained on several test corpora. The developed Urdu semantic tagger shows encouraging precision on the proposed test corpus of 79.47\%

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie