Approaches to Automatic Text Structuring

Structured text helps readers to better understand the content of documents. In classic newspaper texts or books, some structure already exists. In the Web 2.0, the amount of textual data, especially user-generated data, has increased dramatically. As a result, there exists a large amount of textual data which lacks structure, thus making it more difficult to understand. In this thesis, we will explore techniques for automatic text structuring to help readers to fulfill their information needs. Useful techniques for automatic text structuring are keyphrase identification, table-of-contents generation, and link identification. We improve state of the art results for approaches to text structuring on several benchmark datasets. In addition, we present new representative datasets for users’ everyday tasks. We evaluate the quality of text structuring approaches with regard to these scenarios and discover that the quality of approaches highly depends on the dataset on which they are applied. In the first chapter of this thesis, we establish the theoretical foundations regarding text structuring. We describe our findings from a user survey regarding web usage from which we derive three typical scenarios of Internet users. We then proceed to the three main contributions of this thesis. We evaluate approaches to keyphrase identification both by extracting and assigning keyphrases for English and German datasets. We find that unsupervised keyphrase extraction yields stable results, but for datasets with predefined keyphrases, additional filtering of keyphrases and assignment approaches yields even higher results. We present a de- compounding extension, which further improves results for datasets with shorter texts. We construct hierarchical table-of-contents of documents for three English datasets and discover that the results for hierarchy identification are sufficient for an automatic system, but for segment title generation, user interaction based on suggestions is required. We investigate approaches to link identification, including the subtasks of identifying the mention (anchor) of the link and linking the mention to an entity (target). Approaches that make use of the Wikipedia link structure perform best, as long as there is sufficient training data available. For identifying links to sense inventories other than Wikipedia, approaches that do not make use of the link structure outperform the approaches using existing links. We further analyze the effect of senses on computing similarities. In contrast to entity linking, where most entities can be discriminated by their name, we consider cases where multiple entities with the same name exist. We discover that similarity de- pends on the selected sense inventory. To foster future evaluation of natural language processing components for text structuring, we present two prototypes of text structuring systems, which integrate techniques for automatic text structuring in a wiki setting and in an e-learning setting with eBooks

Lexical cohesion analysis for topic segmentation, summarization and keyphrase extraction

Cataloged from PDF version of article.When we express some idea or story, it is inevitable to use words that are semantically related to each other. When this phenomena is exploited from the aspect of words in the language, it is possible to infer the level of semantic relationship between words by observing their distribution and use in discourse. From the aspect of discourse it is possible to model the structure of the document by observing the changes in the lexical cohesion in order to attack high level natural language processing tasks. In this research lexical cohesion is investigated from both of these aspects by first building methods for measuring semantic relatedness of word pairs and then using these methods in the tasks of topic segmentation, summarization and keyphrase extraction. Measuring semantic relatedness of words requires prior knowledge about the words. Two different knowledge-bases are investigated in this research. The first knowledge base is a manually built network of semantic relationships, while the second relies on the distributional patterns in raw text corpora. In order to discover which method is effective in lexical cohesion analysis, a comprehensive comparison of state-of-the art methods in semantic relatedness is made. For topic segmentation different methods using some form of lexical cohesion are present in the literature. While some of these confine the relationships only to word repetition or strong semantic relationships like synonymy, no other work uses the semantic relatedness measures that can be calculated for any two word pairs in the vocabulary. Our experiments suggest that topic segmentation performance improves methods using both classical relationships and word repetition. Furthermore, the experiments compare the performance of different semantic relatedness methods in a high level task. The detected topic segments are used in summarization, and achieves better results compared to a lexical chains based method that uses WordNet. Finally, the use of lexical cohesion analysis in keyphrase extraction is investigated. Previous research shows that keyphrases are useful tools in document retrieval and navigation. While these point to a relation between keyphrases and document retrieval performance, no other work uses this relationship to identify keyphrases of a given document. We aim to establish a link between the problems of query performance prediction (QPP) and keyphrase extraction. To this end, features used in QPP are evaluated in keyphrase extraction using a Naive Bayes classifier. Our experiments indicate that these features improve the effectiveness of keyphrase extraction in documents of different length. More importantly, commonly used features of frequency and first position in text perform poorly on shorter documents, whereas QPP features are more robust and achieve better results.Ercan, GönençPh.D

Adaptive Semantic Annotation of Entity and Concept Mentions in Text

The recent years have seen an increase in interest for knowledge repositories that are useful across applications, in contrast to the creation of ad hoc or application-specific databases. These knowledge repositories figure as a central provider of unambiguous identifiers and semantic relationships between entities. As such, these shared entity descriptions serve as a common vocabulary to exchange and organize information in different formats and for different purposes. Therefore, there has been remarkable interest in systems that are able to automatically tag textual documents with identifiers from shared knowledge repositories so that the content in those documents is described in a vocabulary that is unambiguously understood across applications. Tagging textual documents according to these knowledge bases is a challenging task. It involves recognizing the entities and concepts that have been mentioned in a particular passage and attempting to resolve eventual ambiguity of language in order to choose one of many possible meanings for a phrase. There has been substantial work on recognizing and disambiguating entities for specialized applications, or constrained to limited entity types and particular types of text. In the context of shared knowledge bases, since each application has potentially very different needs, systems must have unprecedented breadth and flexibility to ensure their usefulness across applications. Documents may exhibit different language and discourse characteristics, discuss very diverse topics, or require the focus on parts of the knowledge repository that are inherently harder to disambiguate. In practice, for developers looking for a system to support their use case, is often unclear if an existing solution is applicable, leading those developers to trial-and-error and ad hoc usage of multiple systems in an attempt to achieve their objective. In this dissertation, I propose a conceptual model that unifies related techniques in this space under a common multi-dimensional framework that enables the elucidation of strengths and limitations of each technique, supporting developers in their search for a suitable tool for their needs. Moreover, the model serves as the basis for the development of flexible systems that have the ability of supporting document tagging for different use cases. I describe such an implementation, DBpedia Spotlight, along with extensions that we performed to the knowledge base DBpedia to support this implementation. I report evaluations of this tool on several well known data sets, and demonstrate applications to diverse use cases for further validation

Automatic population of knowledge bases with multimodal data about named entities

Knowledge bases are of great importance for Web search, recommendations, and many Information Retrieval tasks. However, maintaining them for not so popular entities is often a bottleneck. Typically, such entities have limited textual coverage and only a few ontological facts. Moreover, these entities are not well populated with multimodal data, such as images, videos, or audio recordings. The goals in this thesis are (1) to populate a given knowledge base with multimodal data about entities, such as images or audio recordings, and (2) to ease the task of maintaining and expanding the textual knowledge about a given entity, by recommending valuable text excerpts to the contributors of knowledge bases. The thesis makes three main contributions. The first two contributions concentrate on finding images of named entities with high precision, high recall, and high visual diversity. Our main focus are less popular entities, for which the image search engines fail to retrieve good results. Our methods utilize background knowledge about the entity, such as ontological facts or a short description, and a visual-based image similarity to rank and diversify a set of candidate images. Our third contribution is an approach for extracting text contents related to a given entity. It leverages a language-model-based similarity between a short description of the entity and the text sources, and solves a budget-constraint optimization program without any assumptions on the text structure. Moreover, our approach is also able to reliably extract entity related audio excerpts from news podcasts. We derive the time boundaries from the usually very noisy audio transcriptions.Wissensbasen wird bei der Websuche, bei Empfehlungsdiensten und vielen anderen Information Retrieval Aufgaben eine große Bedeutung zugeschrieben. Allerdings stellt sich deren Unterhalt für weniger populäre Entitäten als schwierig heraus. Üblicherweise ist die Anzahl an Texten über Entitäten dieser Art begrenzt, und es gibt nur wenige ontologische Fakten. Außerdem sind nicht viele multimediale Daten, wie zum Beispiel Bilder, Videos oder Tonaufnahmen, für diese Entitäten verfügbar. Die Ziele dieser Dissertation sind daher (1) eine gegebene Wissensbasis mit multimedialen Daten, wie Bilder oder Tonaufnahmen, über Entitäten anzureichern und (2) die Erleichterung der Aufgabe Texte über eine gegebene Entität zu verwalten und zu erweitern, indem den Beitragenden einer Wissensbasis nützliche Textausschnitte vorgeschlagen werden. Diese Dissertation leistet drei Hauptbeiträge. Die ersten zwei Beiträge sind im Gebiet des Auffindens von Bildern von benannten Entitäten mit hoher Genauigkeit, hoher Trefferquote, und hoher visueller Vielfalt. Das Hauptaugenmerk liegt auf den weniger populären Entitäten bei denen die Bildersuchmaschinen normalerweise keine guten Ergebnisse liefern. Unsere Verfahren benutzen Hintergrundwissen über die Entität, zum Beispiel ontologische Fakten oder eine Kurzbeschreibung, so wie ein visuell-basiertes Bilderähnlichkeitsmaß um die Bilder nach Rang zu ordnen und um eine Menge von Bilderkandidaten zu diversifizieren. Der dritte Beitrag ist ein Ansatz um Textinhalte, die sich auf eine gegebene Entität beziehen, zu extrahieren. Der Ansatz nutzt ein auf einem Sprachmodell basierendes Ähnlichkeitsmaß zwischen einer Kurzbeschreibung der Entität und den Textquellen und löst zudem ein Optimierungsproblem mit Budgetrestriktion, das keine Annahmen an die Textstruktur macht. Darüber hinaus ist der Ansatz in der Lage Tonaufnahmen, welche in Beziehung zu einer Entität stehen, zuverlässig aus Nachrichten-Podcasts zu extrahieren. Dafür werden zeitliche Abgrenzungen aus den normalerweise sehr verrauschten Audiotranskriptionen hergeleitet

Improving Editorial Workflow and Metadata Quality at Springer Nature

Identifying the research topics that best describe the scope of a scientific publication is a crucial task for editors, in particular because the quality of these annotations determine how effectively users are able to discover the right content in online libraries. For this reason, Springer Nature, the world's largest academic book publisher, has traditionally entrusted this task to their most expert editors. These editors manually analyse all new books, possibly including hundreds of chapters, and produce a list of the most relevant topics. Hence, this process has traditionally been very expensive, time-consuming, and confined to a few senior editors. For these reasons, back in 2016 we developed Smart Topic Miner (STM), an ontology-driven application that assists the Springer Nature editorial team in annotating the volumes of all books covering conference proceedings in Computer Science. Since then STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, for a total of about 800 volumes per year. Over the past three years the initial prototype has iteratively evolved in response to feedback from the users and evolving requirements. In this paper we present the most recent version of the tool and describe the evolution of the system over the years, the key lessons learnt, and the impact on the Springer Nature workflow. In particular, our solution has drastically reduced the time needed to annotate proceedings and significantly improved their discoverability, resulting in 9.3 million additional downloads. We also present a user study involving 9 editors, which yielded excellent results in term of usability, and report an evaluation of the new topic classifier used by STM, which outperforms previous versions in recall and F-measure

Text mining techniques for patent analysis.

Abstract Patent documents contain important research results. However, they are lengthy and rich in technical terminology such that it takes a lot of human efforts for analyses. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a series of text mining techniques that conforms to the analytical process used by patent analysts. These techniques include text segmentation, summary extraction, feature selection, term association, cluster generation, topic identification, and information mapping. The issues of efficiency and effectiveness are considered in the design of these techniques. Some important features of the proposed methodology include a rigorous approach to verify the usefulness of segment extracts as the document surrogates, a corpus-and dictionary-free algorithm for keyphrase extraction, an efficient co-word analysis method that can be applied to large volume of patents, and an automatic procedure to create generic cluster titles for ease of result interpretation. Evaluation of these techniques was conducted. The results confirm that the machine-generated summaries do preserve more important content words than some other sections for classification. To demonstrate the feasibility, the proposed methodology was applied to a realworld patent set for domain analysis and mapping, which shows that our approach is more effective than existing classification systems. The attempt in this paper to automate the whole process not only helps create final patent maps for topic analyses, but also facilitates or improves other patent analysis tasks such as patent classification, organization, knowledge sharing, and prior art searches

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

Trending topic extraction from social media

Social media has become the first source of information for many people. The amount of information posted on social media daily has become very vast that it became difficult to track. One of the most popular social media applications is Twitter. Users follow lots of news accounts, public figures, and their friends so they can be updated by the latest events around them. Since the dialect language and the style of writing differ from a region to another, our objective in this research is to extract trending topics for an Egyptian twitter user. In this way, the user can easily get at a glimpse of the trending topics discussed by the people he follows. To find the best approach achieving our objective, we investigate the document pivot and the feature pivot approaches. By applying the document pivot approach on the baseline data using tf-itf (term frequency-inverse tweet frequency) representation, repeated bisecting k-means clustering technique and extracting most frequent n-grams from each cluster we could achieve a recall value of 100% and F1 measure of 0.8. The application of the feature pivot approach on the baseline data using the content similarity algorithm to group related unigrams together, could achieve a recall value of 100% and F1 measure of 0.923. To validate our results we collected 12 different data sets of different sizes (200, 400, 600, and 1200) and from three different domains (sports, entertainment, and news) then applied both approaches to them. The average recall, precision and F1 measure values resulted from applying the feature pivot approach are larger than those achieved by applying the document pivot approach. To make sure this difference in results is statistically significant we applied the Two-sample one-tailed paired significance t-test that showed the results are significantly better at confidence interval of 90% The results showed that the document pivot approach could extract the trending topics for an Egyptian twitter user with an average recall value of 0.714, average precision value of 0.521, and average F1 measure value of 0.556 versus average recall, precision and F1 measure values of 0.981, 0.754, and 0.833 respectively, when applying the feature pivot approach. â€