Author Extraction: A Test Experience for Flexible Information Extraction

The lack of data for information extraction (IE) from semi-structured business documents is a real problem for the IE community. Publications relying on large-scale datasets use only proprietary, unpublished data due to the sensitive nature of such documents. Publicly available datasets are mostly small and domain-specific. The absence of a large-scale public dataset or benchmark hinders the reproducibility and cross-evaluation of published methods. The DocILE 2023 competition, hosted as a lab at the CLEF 2023 conference and as an ICDAR 2023 competition, will run the first major benchmark for the tasks of Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR) from business documents. With thousands of annotated real documents from open sources, a hundred thousand of generated synthetic documents, and nearly a million unlabeled documents, the DocILE lab comes with the largest publicly available dataset for KILE and LIR. We are looking forward to contributions from the Computer Vision, Natural Language Processing, Information Retrieval, and other communities. The data, baselines, code and up-to-date information about the lab and competition are available at https://docile.rossum.ai/ .

There is large and growing amounts of textual data that contains information about human activities. Mining interesting knowledge from this textual data is a challenging task because it consists of unstructured or semistructured text that are written in natural language. In the field of artificial intelligence, event-oriented techniques are helpful in addressing this problem, where information retrieval (IR), information extraction (IE) and graph methods (GMs) are three of the most important paradigms in supporting event-oriented processing. In recent years, due to information explosions, textual event detection and recognition have received extensive research attention and achieved great success. Many surveys have been conducted to retrospectively assess the development of event detection. However, until now, all of these surveys have focused on only a single aspect of IR, IE or GMs. There is no research that provides a complete introduction or a comparison of IR, IE, and GMs. In this article, a survey about these techniques is provided from a broader perspective, and a convenient and comprehensive comparison of these techniques is given. The hallmark of this article is that it is the first survey that combines IR, IE and GMs in a single frame and will therefore benefit researchers by acting as a reference in this field.

Although Large language Model (LLM)-powered information extraction (IE) systems have shown impressive capabilities, current fine-tuning paradigms face two major limitations: high training costs and difficulties in aligning with LLM preferences. To address these issues, we propose a novel universal IE paradigm—the Self-Correcting Iterative Refinement (SCIR) framework—along with a Multi-task Bilingual (Chinese-English) Self-Correcting (MBSC) dataset containing over 100,000 entries. The SCIR framework achieves plug-and-play compatibility with existing LLMs and IE systems through its Dual-Path Self-Correcting module and feedback-driven optimization, thereby significantly reducing training costs. Concurrently, the MBSC dataset tackles the challenge of preference alignment by indirectly distilling GPT-4's capabilities into IE result detection models. Experimental results demonstrate that SCIR outperforms state-of-the-art IE methods across three key tasks— named entity recognition, relation extraction, and event extraction—achieving a 5.27 percent average improvement in span-based Micro-F1 while reducing training costs by 87 percent compared to baseline approaches. These advancements not only enhance the flexibility and accuracy of IE systems but also pave the way for lightweight and efficient IE paradigms.

RINX: A system for information and knowledge extraction from resumes

Domain knowledge is essential resource in Information Extraction (IE) from free text since it supports the decisions about structuring the extracted text objects into domain statements. Thus manually-created conceptual structures enable the semantic representation of textual information. This paper discusses the role of domain knowledge in information extraction of structured data from patient-related texts. The article shows that domain knowledge is encoded not only in the conceptual structures, which provide the ontological framework for the IE task, but also in the IE templates that are designed to capture domain semantics. A prototype system and IE examples of domain knowledge usage are considered together with results of the current prototype evaluation.KeywordsThyroid GlandDomain KnowledgeNatural Language ProcessingInformation ExtractionEntity RecognitionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An increasing amount of electronically available information is stored in Asian language documents, which makes Information Retrieval (IR) and Information Extraction (IE) for these languages important for a large number of users. Analysis and extraction of information in these languages presents several interesting problems not seen in Western European languages; these are interesting in their own right and for the insights they can give into more general IR and IE techniques. We describe these problems and our system for Thai language IE One of the main concerns when working with Thai natural language is that the structure of the language itself is highly ambiguous. The analyser therefore requires more sophisticated techniques and large amounts of domain knowledge to cope with these ambiguities. We describe our approach to a natural language analysis system that performs preprocessing for the Thai language and the extraction module to retrieve specific information according to the predefined concept definitions.

Memory-Based Learning (MBL), one of the most influential machine learning paradigms, has been applied with great success to a variety of NLP tasks. This monograph describes the application of MBL to robust parsing. Robust parsing using MBL can provide added functionality for key NLP applications, such as Information Retrieval, Information Extraction, and Question Answering, by facilitating more complex syntactic analysis than is currently available. The text presupposes no prior knowledge of MBL. It provides a comprehensive introduction to the framework and goes on to describe and compare applications of MBL to parsing. Since parsing is not easily characterizable as a classification task, adaptations of standard MBL are necessary. These adaptations can either take the form of a cascade of local classifiers or of a holistic approach for selecting a complete tree. The text provides excellent course material on MBL. It is equally relevant for any researcher concerned with symbolic machine learning, Information Retrieval, Information Extraction, and Question Answering.

This paper presents software development for searching and extracting temporal information from text to help users access and understand content from electronic documents stored on organizational computer systems and websites using the HeidelTime tool. HeidelTime is a natural language processing tool customized to analyze temporal elements in Vietnamese contexts. The research methodology includes the following key steps: Surveying systems and user needs for time-based text search; analyzing and selecting natural language processing techniques, where HeidelTime is applied to identify and extract temporal information from Vietnamese text. The research results demonstrate that the time-based search software achieves high accuracy when deployed on the organization's document management system. It effectively supports time-based information retrieval and extraction, meeting users' practical needs. This study highlights the potential application of natural language processing technology in Vietnamese document management, contributing to improved storage and search efficiency within organizational information systems. Keywords: Text extraction; natural language processing; information extraction; Vietnamese text; HeidelTime

This paper presents the Virtual Itineraries in Pyrenees (PIV) project. Spatial and temporal unified models are proposed to give a formal representation to geographical information. The aim is to improve the access to local cultural and heritage document collections. The models take into account characteristics of heterogeneous human expression modes: written language and captures of drawings, maps, pictures, etc. Semantic treatments have been built to automatically manage spatial and temporal information from non-structured data. These treatments are added to classical information extraction (IE) approaches. Then, geographical information retrieval processing is based on geographical information systems (GIS) algorithms. These algorithms look for any relations between formal representations of geographic information in documents collections and similar representations in a user query. Finally we propose a prototype implementing such geographic IE and geographic Information Retrieval (IR).

The construction and real estate industry features long product life cycles, a wide range of stakeholders and a high information density. Information is not only available in large quantities, but also in a very heterogeneous and user-specific manner. Digital building documentation is partially available in some companies and even non-existent in others. The result is often analogue, unstructured building documentation, which makes the processing of data and information considerably more difficult and, in the worst case, leads to media disruptions between those involved. However, the benefits of a lean, complete and targeted digital building documentation can be manifold. In particular, automated information extraction and further information retrieval are seen as having great potential. Information extraction as an ultimate aim, requires a defined handling of analogue documents, transparent criteria regarding data quality and machine readability as well as a clear classification system. The research project ML-BAU-DOK (funded by The Federal Office for Building and Regional Planning BBSR, SWD-10.08.18.7-20.26) presents the necessary preparatory processes for advanced digital use of building documentation. First, a set of rules is created to digitize paper-based documentation in a targeted manner. The automated separation of mass documentation into individual documents, as well as the classification of documents into selected document classes, is mapped using machine-learning. The document classes are consolidated from the current worldwide class standards and prioritized according to their information content. The project includes the evaluation of 600,000 document pages, which are analysed class-specifically with regard to two use cases, energy efficiency and life cycle analysis. The methodology ensures transferability of the results to other use cases. The key result of the ML-BAU-DOK is an algorithm that automatically separates individual documents from a mass scan, assigns the individual documents to defined document classes, and thus reduces the amount of scanning and filing required. This leads to a classification system that enables information extraction as a subsequent goal and brings the construction and real estate industry closer to a Common Data Environment.

Named Entity Recognition is a subtask in Information Extraction. In Named Entity Recognition (NER) we try to identify each of the words provided into some categories predefined by us. These categories or classes can be organization, name, time, place etc. Named Entity Recognition is a part of Natural Language Processing which is aims at making human and computer interactions more meaningful and efficient. NLP is nowadays being effectively being used in the area of automatic text summarization, cross language information retrieval, speech recognition and query named entity recognition. This paper focuses on Named entity recognition, how it is performed and a how apply NER to web search queries so that correct and more user personalized results are displayed by the web search engines..

The paper introduces an instructive scheme to establish an enterprise-wide management information system with high flexibility. Key concepts, business logic, process unit partition, information architecture configuration, information extraction and decision support, are respectively defined and explained to improve its flexibility and expansibility Moreover, Microsoft@ Net solution, combined with SQL Server 2000 and OLE DB, provides strong and reasonable IT support to implement the introduced MIS design method.

The development of digital and online information repositories is creating many opportunities and also new challenges in information retrieval. The availability of online documents in many different languages makes it possible for users around the world to directly access previously unimagined sources of information. However in conventional information retrieval systems the user must enter a search query in the language of the documents in order to retrieve it. This requires that users can express their queries in those languages in which the information is available and can understand the documents returned by the retrieval process. This restriction clearly limits the amount and type of information that an individual user really has access to. Cross Lingual Information Access is concerned with technologies that let users express their query in their native language, and irrespective of the language in which the information is available, present their information in the user-preferred language or set of languages, in a manner that satisfies the user's information needs. The additional processing may take the form of machine translation of snippets, summarization and subsequent translation of summaries and/or information extraction. In recent times, research in Cross Lingual Information Access has been vigorously pursued through several international fora, such as, the Cross-Language Evaluation Forum (CLEF), NTCIR Asian Language Retrieval, Question-answering Workshop and such other fora. A workshop geared towards cross language information retrieval in Indian languages (FIRE) was organized in December 2008. In addition to CLIR, significant results have been obtained in multilingual summarization workshops and cross-language named entity extraction challenges by the ACL (Association for Computational Linguistics) and the Geographic Information retrieval (GeoCLEF) track of CLEF. The previous two issues of this workshop were held in January 2007, during IJCAI 2007 in Hyderabad, India (http://search.iiit.ac.in/CLIA2007/) and subsequently during IJCNLP 2008 in Hyderabad, India (http://search.iiit.ac.in/CLIA2008/). Both the previous workshops attracted an encouraging number of submissions, and a large number of registered participants. This third international workshop on Cross Lingual Information Access aims to bring together various trends in multi-source, cross and multilingual information retrieval and access, and provide a venue for researchers and practitioners from academia, government, and industry to interact and share a broad spectrum of ideas, views and applications. The present workshop includes an invited keynote talk, presentations of technical papers selected after peer review followed by a panel discussion.

Automatic information extraction from semi-structured Web pages by pattern discovery

For the actual demand on food safety emergency management, this paper researches some key techniques such as web page crawling that collects subject-related and timely information, the deceptive spam opinion identification which based on the machine learning method of “learning from positive and unlabeled examples”, the food safety incidents' information extraction and warning which based on the ontology and so on. On the basis above, this paper researches and designs the system on monitoring and analyzing the Internet information for food safety, and the system prototype has been achieved.