Finding Mutual Benefit between Subjectivity Analysis and Information Extraction

Information extraction (IE) systems are costly to build because they require development texts, parsing tools, and specialized dictionaries for each application domain and each natural language that needs to be processed. We present a novel method for rapidly creating IE systems for new languages by exploiting existing IE systems via cross-language projection. Given an IE system for a source language (e.g., English), we can transfer its annotations to corresponding texts in a target language (e.g., French) and learn information extraction rules for the new language automatically. In this paper, we explore several ways of realizing both the transfer and learning processes using off-the-shelf machine translation systems, induced word alignment, attribute projection, and transformation-based learning. We present a variety of experiments that show how an English IE system for a plane crash domain can be leveraged to automatically create a French IE system for the same domain.

Information Extraction (IE) systems assist analysts to assimilate information from electronic documents. This paper focuses on IE tasks designed to support information discovery applications. Since information discovery implies examining large volumes of heterogeneous documents for situations that cannot be anticipated a priori, they require IE systems to have breadth as well as depth. This implies the need for a domain-independent IE system that can easily be customized for specific domains: end users must be given tools to customize the system on their own. It also implies the need for defining new intermediate level IE tasks that are richer than the subject-verb-object (SVO) triples produced by shallow systems, yet not as complex as the domain-specific scenarios defined by the Message Understanding Conference (MUC). This paper describes InfoXtract, a robust, scalable, intermediate-level IE engine that can be ported to various domains. It describes new IE tasks such as synthesis of entity profiles, and extraction of concept-based general events which represent realistic near-term goals focused on deriving useful, actionable information. Entity profiles consolidate information about a person/organization/location etc. within a document and across documents into a single template; this takes into account aliases and anaphoric references as well as key relationships and events pertaining to that entity. Concept-based events attempt to normalize information such as time expressions (e.g., yesterday) as well as ambiguous location references (e.g., Buffalo). These new tasks facilitate the correlation of output from an IE engine with structured data to enable text mining. InfoXtract's hybrid architecture comprised of grammatical processing and machine learning is described in detail. Benchmarking results for the core engine and applications utilizing the engine are presented.

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.

A real-world information extraction (IE) system for semi-structured document images often involves a long pipeline of multiple modules, whose complexity dramatically increases its development and maintenance cost. One can instead consider an end- to-end model that directly maps the input to the target output and simplify the entire process. However, such generation approach is known to lead to unstable performance if not designed carefully. Here we present our recent effort on transitioning from our existing pipeline-based IE system to an end-to-end system focusing on practical challenges that are associated with replacing and deploying the system in real, large-scale production. By carefully formulating document IE as a sequence generation task, we show that a single end-to-end IE system can be built and still achieve competent performance.

In this paper, we give an overview of knowledge discovery (KD) and information extraction (IE) techniques on the World Wide Web (WWW). We intend to answer the following questions: What kind of additional uncertainty challenges are introduced by the WWW setting to basic KD and IE techniques? What are the fundamental techniques that can be used to reduce such uncertainty and achieve reasonable KD and IE performance on the WWW? What is the impact of each novel method? What types of interactions can be conducted between these techniques and information networks to make them benefit from each other? In what way can we utilize the results in more interesting applications? What are the remaining challenges and what are the possible ways to address these challenges? We hope this can provide a road map to advance KD and IE on the WWW to a higher level of performance, portability and utilization.

The development of an information extraction (IE) system for Thai documents raises a number of issues which are not important for IE in English and other European languages. We describe the characteristics of written Thai and the problem statements, and our approach to the Thai IE system. The structure of written Thai is highly ambiguous, which requires more sophisticated techniques than are necessary to perform comparable IE tasks in most European languages, and large amounts of domain knowledge to cope with these ambiguities. The basic characteristic of this system is to provide different natural language components to assess the surface structure of the documents. These components include word segmentation, specific lexical structure terms identification and part-of-speech tagger. Further analysis is to perform a shallow parsing based on the relevant regions that contain the specific trigger terms or patterns specified in the extraction templates. Finally, the information of interest is extracted from the grammar trees in corresponding to predefined concept definitions and returns the users with a list of answers responding to each concept.

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.

The rapid increase in volume of protein structure literature means useful information may be hidden or lost in the published literature and the process of finding relevant material, sometimes the rate-determining factor in new research, may be arduous and slow. We describe the Protein Active Site Template Acquisition (PASTA) system, which addresses these problems by performing automatic extraction of information relating to the roles of specific amino acid residues in protein molecules from online scientific articles and abstracts. Both the terminology recognition and extraction capabilities of the system have been extensively evaluated against manually annotated data and the results compare favourably with state-of-the-art results obtained in less challenging domains. PASTA is the first information extraction (IE) system developed for the protein structure domain and one of the most thoroughly evaluated IE system operating on biological scientific text to date. PASTA makes its extraction results available via a browser-based front end: http://www.dcs.shef.ac.uk/nlp/pasta/. The evaluation resources (manually annotated corpora) are also available through the website: http://www.dcs.shef.ac.uk/nlp/pasta/results.html.

Applications in the World Wide Web aggregate vast amounts of information from different data sources. The aggregation process is often implemented with Extract, Transform and Load (ETL) processes. Usually ETL processes require information for aggregation available in structured formats, e. g. XML or JSON. In many cases the information is provided in natural language text which makes the application of ETL processes impractical. Due to the fact that information is provided in natural language, Information Extraction (IE) systems have been evolved. They make use of Natural Language Processing (NLP) tools to derive meaning from natural language text. State-of-the-art NLP tools apply Machine Learning methods. These NLP tools perform on newspapers with good quality, but they drop accuracy in other domains. However, to improve the quality for IE systems in specific domains often NLP tools are trained on domain specific text which is a time consuming process. This paper introduces an approach using a Continuous Integration pipeline for organizing and monitoring the annotation process on domain specific corpora.

One promising application of natural language processing (NLP) research is in the area of information extraction (IE). In this paper, we present work flow of our IE system for the extraction of semantically rich information from the unstructured or semi-structured Chinese web pages. Knowledge engineering approach and automatic training approach are used to extract pattern and built knowledge repository. General IE system needs to label the unlabeled training Web pages. A novel methodology that does not need to label text is developed, including hierarchy filtration pattern matching based on syntax in best distance method and maximum forward boundary recognition using organization suffix repository and part of speech tagging method. As for applications of IE, a new application system based on IE is built. It is object-level vertical search system and object here is Chinese people, so IE is concerned with extracting people's related attributes from a collection of web pages about Chinese people. The results are displayed as hierarchy directory tree according to people's attributes. The system makes user find people quickly and easily.

This article investigates the effectiveness of voting and stacked generalization -also known as stacking- in the context of information extraction (IE). A new stacking framework is proposed that accommodates well-known approaches for IE. The key idea is to perform cross-validation on the base-level data set, which consists of text documents annotated with relevant information, in order to create a meta-level data set that consists of feature vectors. A classifier is then trained using the new vectors. Therefore, base-level IE systems are combined with a common classifier at the meta-level. Various voting schemes are presented for comparing against stacking in various IE domains. Well known IE systems are employed at the base-level, together with a variety of classifiers at the meta-level. Results show that both voting and stacking work better when relying on probabilistic estimates by the base-level systems. Voting proved to be effective in most domains in the experiments. Stacking, on the other hand, proved to be consistently effective over all domains, doing comparably or better than voting and always better than the best base-level systems. Particular emphasis is also given to explaining the results obtained by voting and stacking at the meta-level, with respect to the varying degree of similarity in the output of the base-level systems.

Hidden Markov models (HMMs) are powerful statistical models that have found successful applications in Information Extraction (IE). In current approaches to applying HMMs to IE, an HMM is used to model text at the document level. This modelling might cause undesired redundancy in extraction in the sense that more than one filler is identified and extracted. We propose to use HMMs to model text at the segment level, in which the extraction process consists of two steps: a segment retrieval step followed by an extraction step. In order to retrieve extraction-relevant segments from documents, we introduce a method to use HMMs to model and retrieve segments. Our experimental results show that the resulting segment HMM IE system not only achieves near zero extraction redundancy, but also has better overall extraction performance than traditional document HMM IE systems.

This paper describes and evaluates MOP, an IE system for automatic extraction of metalinguistic information from technical and scientific documents. We claim that such a system can create special databases to bootstrap compilation and facilitate update of the huge and dynamically changing glossaries, knowledge bases and ontologies that are vital to modern-day research.

RINX: A system for information and knowledge extraction from resumes

The ubiquity of unstructured/semi-structured data in business decision-making presents a unique challenge as data management methods developed for structured data are not directly applicable. While such non-traditional data have already become part of many organizations’ product/service offerings; most data managers admit that they lack the capability to leverage such data assets to elicit meaningful information. In this context, we discuss the use of Information Extraction (IE) methodologies to aid in the decision making process that utilizes un/semi-structured data. We focus on knowledge-based IE methodologies that are particularly suitable for business domains characterized by few subject matter experts’ tacit and uncodified domain knowledge. Ontologies that encapsulate and represent domain knowledge can play a key role in enabling knowledge-based IE. In this article we conduct a comprehensive review of the extant literature on Ontology-Based Information Extraction (OBIE) and articulate four different roles ontologies play in such knowledge-based IE systems. We discuss these various roles of ontologies in relation to the various IE phases and illustrate them with a case study involving IT service contracts, which is an example of a OBIE system. Finally, we discuss open research issues related to the use of ontologies, evaluation metrics, and applications of IE in decision-making.