CLosER: Conversational Legal Longformer with Expertise-Aware Passage Response Ranker for Long Contexts

In this paper, we investigate the task of response ranking in conversational legal search. We propose a novel method for conversational passage response retrieval (ConvPR) for long conversations in domains with mixed levels of expertise. Conversational legal search is challenging because the domain includes long, multi-participant dialogues with domain-specific language. Furthermore, as opposed to other domains, there typically is a large knowledge gap between the questioner (a layperson) and the responders (lawyers), participating in the same conversation. We collect and release a large-scale real-world dataset called LegalConv with nearly one million legal conversations from a legal community question answering (CQA) platform. We address the particular challenges of processing legal conversations, with our novel Conversational Legal Longformer with Expertise-Aware Response Ranker, called CLosER. The proposed method has two main innovations compared to state-of-the-art methods for ConvPR: (i) Expertise-Aware Post-Training; a learning objective that takes into account the knowledge gap difference between participants to the conversation; and (ii) a simple but effective strategy for re-ordering the context utterances in long conversations to overcome the limitations of the sparse attention mechanism of the Longformer architecture. Evaluation on LegalConv shows that our proposed method substantially and significantly outperforms existing state-of-the-art models on the response selection task. Our analysis indicates that our Expertise-Aware Post-Training, i.e., continued pre-training or domain/task adaptation, plays an important role in the achieved effectiveness. Our proposed method is generalizable to other tasks with domain-specific challenges and can facilitate future research on conversational search in other domains.</p

A Comprehensive Review of Community Detection in Graphs

The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a crucial role in understanding the organization and functioning of complex systems. We begin by introducing the concept of community structure, which refers to the arrangement of vertices into clusters, with strong internal connections and weaker connections between clusters. Then, we provide a thorough exposition of various community detection methods, including a new method designed by us. Additionally, we explore real-world applications of community detection in diverse networks. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs. It serves as a valuable resource for researchers and practitioners in multiple disciplines, offering insights into the challenges, methodologies, and applications of community detection in complex networks

Mining Experts in Technical Online Forums

Many organizations use or host discussion lists, in the form of online forums and email lists. Analyzing the content of those discussion lists is an effective solution to the task of expert finding, since experts tend to participate often by giving advice, and receive the best feedback. We present a novel method to identify positive comments that helps to identify experts by combining author statistics with polarity mining. Our method is able to distinguish experts from flamers and other people that simply participates frequently in discussions. We demonstrate the validity of our approach by evaluating it with an online discussion forum in Spanish.Sociedad Argentina de Informática e Investigación Operativ

Expert Finding in Disparate Environments

Providing knowledge workers with access to experts and communities-of-practice is central to expertise sharing, and crucial to effective organizational performance, adaptation, and even survival. However, in complex work environments, it is difficult to know who knows what across heterogeneous groups, disparate locations, and asynchronous work. As such, where expert finding has traditionally been a manual operation there is increasing interest in policy and technical infrastructure that makes work visible and supports automated tools for locating expertise. Expert finding, is a multidisciplinary problem that cross-cuts knowledge management, organizational analysis, and information retrieval. Recently, a number of expert finders have emerged; however, many tools are limited in that they are extensions of traditional information retrieval systems and exploit artifact information primarily. This thesis explores a new class of expert finders that use organizational context as a basis for assessing expertise and for conferring trust in the system. The hypothesis here is that expertise can be inferred through assessments of work behavior and work derivatives (e.g., artifacts). The Expert Locator, developed within a live organizational environment, is a model-based prototype that exploits organizational work context. The system associates expertise ratings with expert’s signaling behavior and is extensible so that signaling behavior from multiple activity space contexts can be fused into aggregate retrieval scores. Post-retrieval analysis supports evidence review and personal network browsing, aiding users in both detection and selection. During operational evaluation, the prototype generated high-precision searches across a range of topics, and was sensitive to organizational role; ranking true experts (i.e., authorities) higher than brokers providing referrals. Precision increased with the number of activity spaces used in the model, but varied across queries. The highest performing queries are characterized by high specificity terms, and low organizational diffusion amongst retrieved experts; essentially, the highest rated experts are situated within organizational niches

Interaktive Suchprozesse in komplexen Arbeitssituationen - Ein Retrieval Framework

In recent years a steady increase of information produced in organizations can be noticed. In order to stay competitive, companies have a growing interest in reusing existing knowledge from past projects. Furthermore, a complete picture of the available information is necessary to be able to make informed decisions. The variety and complexity of information in modern organizations often exceeds the capabilities of the currently deployed enterprise search solutions. The reasons for that are manifold and range from non-linked information from multiple software systems to missing functionality to support users during search tasks. Existing search engines often do not support the search paradigms necessary in these environments. On many occasions, users are not aware of the results they will find during the formulation of the search queries. Additionally, the aspect of knowledge building and the identification of new insights into the available data is a priority for the users. Therefore, search paradigms are useful to provide users with tools that support exploratory navigation in a data set and help them to recognize relationships between search results. The goal of this publication is the introduction of a framework that supports exploratory searches in an organizational setting. The described LFRP-framework is built on top of four pillars. 1. The multi-layer functionality allows users to formulate complex search queries referring to more than one result type. Therewith, it enables search queries that - starting from a set of relevant projects - allow selections of documents that are linked to these projects. 2. The search paradigm of faceted searching supports users in formulating search queries incrementally by offering dynamic and valid filter criteria that avoid empty result sets. 3. By combining the concept of faceted search with the capability to influence the search result order based on filter criteria, users can define in a fine-grained way which criteria values shall be weighted stronger or weaker in the search results. The interaction with the ranking is conducted transparently by the so-called user preference functions. 4. The last pillar consists of the visualization type of parallel coordinates covering two tasks in the search user interface of the LFRP-Framework. On the one hand, users formulate their search queries solely graphically in the parallel coordinates and on the other hand they obtain a visual representation of the search results and are able to discover relationships between search results and their facets. The framework is introduced formally from a query model point of view as well as a prototypical implementation. It enables users to access large linked data sets by navigation and constitutes a contribution to a comprehensive information strategy for organizations.Seit einigen Jahren ist ein stetiges Ansteigen der Menge an Informationen, die in Unternehmen erzeugt werden, festzustellen. Um als Unternehmen wettbewerbsfähig zu bleiben, ist es notwendig, vorhandenes Wissen wiederzuverwenden, um aus vergangenen Projektergebnissen profitieren zu können. Weiterhin ist ein vollständiges Informationsbild unabdingbar, um informierte Entscheidungen treffen zu können. Die Informationsvielfalt in modernen Unternehmen übersteigt häufig die Fähigkeiten aktuell anzutreffender unternehmensweiter Suchlösungen. Die Gründe hierfür sind vielfältig und reichen von nicht verknüpften Informationen aus verschiedenen Softwaresystemen bis hin zu fehlenden Funktionen, um den Nutzer bei der Suche zu unterstützen. Vorhandene Suchfunktionen im Unternehmen unterstützen häufig nicht die Suchparadigmen, die in diesem Umfeld notwendig sind. Vielfach ist den Suchenden bei der Formulierung ihrer Suchanfrage nicht bekannt, welche Ergebnisse sie finden werden. Stattdessen steht der Aspekt des Wissensaufbaus und der Gewinnung neuer Einsichten in den vorhandenen Daten im Vordergrund. Hierzu werden Suchparadigmen benötigt, die dem Nutzer Werkzeuge zur Verfügung stellen, die ein exploratives Navigieren im Datenbestand erlauben und ihnen bei der Erkennung von Zusammenhängen in den Suchergebnissen unterstützen. Das Ziel dieser Arbeit ist die Vorstellung eines Rahmenwerks, dass explorative Suchvorhaben im Unternehmensumfeld unterstützt. Das beschriebene LFRP-Framework baut auf vier Säulen auf. 1. Die Multi-Layer Funktionalität erlaubt es Nutzern, komplexe Suchanfragen zu formulieren, die sich auf mehr als einen Ergebnistyp beziehen. Dies ermöglicht beispielsweise Suchabfragen, die - ausgehend von einer Menge von relevanten vergangenen Projekten - Selektionen auf den dazugehörigen Dokumenten erlauben. 2. Das Suchparadigma der facettierten Suche unterstützt Nutzer bei der inkrementellen Formulierung von Suchanfragen mithilfe von dynamisch angebotenen Filterkriterien und vermeidet leere Ergebnismengen durch die Bereitstellung gültiger Filterkriterien. 3. Die Erweiterung der facettierten Suche um die Möglichkeit, die Suchergebnisreihenfolge basierend auf Filterkriterien zu beeinflussen, erlaubt es Nutzern feingranular vorzugeben, welche Kriterienausprägungen im Suchergebnis stärker gewichtet werden sollen. Für den Nutzer geschieht die Beeinflussung des Rankings transparent über sogenannte Nutzerpräferenzfunktionen. 4. Die letzte Säule umfasst die Visualisierung der parallelen Koordinaten, die in der Suchoberfläche des LFRP-Frameworks zwei Aufgaben übernimmt. Zum einen formuliert der Nutzer damit die Suchanfrage ausschließlich grafisch über die Visualisierung und zum anderen erhält er eine grafische Repräsentation der Suchergebnisse und kann so leichter Beziehungen zwischen Suchergebnissen und deren Facetten erkennen. Das Framework, welches in dieser Arbeit formal aus Sicht des Anfragemodells sowie als prototypische Umsetzung betrachtet wird, ermöglicht Nutzern den navigierenden Zugriff auf große vernetze Datenbestände und stellt einen Baustein einer umfassenden Informationsstrategie für Unternehmen dar