1 Boolean retrieval 1 2 The term vocabulary and postings lists 19 3 Dictionaries and tolerant retrieval 49 4 Index construction 67 5 Index compression 85 6 Scoring, term weighting and the vector space model 109 7 Computing scores in a complete search system 135 8 Evaluation in information retrieval 151 9 Relevance feedback and query expansion 177 10 XML retrieval 195 11 Probabilistic information retrieval 219 12 Language models for information retrieval 237 13 Text classification and Naive Bayes 253 (...) 14 Vector space classification 289 15 Support vector machines and machine learning on documents 319 16 Flat clustering 349 17 Hierarchical clustering 377 18 Matrix decompositions and latent semantic indexing 403 19 Web search basics 421 20 Web crawling and indexes 443 21 Link analysis 461.. (shrink)

This self-contained introduction to natural language semantics addresses the majortheoretical questions in the field. The authors introduce the systematic study of linguistic meaningthrough a sequence of formal tools and their linguistic applications. Starting with propositionalconnectives and truth conditions, the book moves to quantification and binding, intensionality andtense, and so on. To set their approach in a broader perspective, the authors also explore theinteraction of meaning with context and use (the semantics-pragmatics interface) and address some ofthe foundational questions, especially in connection (...) with cognition in general. They also introducea few of the most accessible and interesting ideas from recent research to give the reader a bit ofthe flavor of current work in semantics. The organization of this new edition is modular; after theintroductory chapters, the remaining material can be covered in flexible order. The book presupposesno background in formal logic (an appendix introduces the basic notions of set theory) and only aminimal acquaintance with linguistics. This edition includes a substantial amount of completely newmaterial and has been not only updated but redesigned throughout to enhance itsuser-friendliness. (shrink)

In this paper we take a fresh look at the information retrieval problem of balancing recall with precision in electronic document extraction. We examine the IR constructs of uncertainty, context and relevance, proposing a new process model for context learning, and introducing a new IT artifact designed to support user driven learning by leveraging explicit knowledge to discover implicit knowledge within a corpus of documents. The IT artifact is a prototype designed to present a small set of extracted documents from (...) a targeted corpus based upon user inputted criteria. The prototype provides the user with the opportunity to balance exploration and exploitation, via iterative relevance feedback to address the problem of imprecision resulting from uncertainty. We model the problem as an exploration–exploitation dilemma and apply it to a specific case of IR called eDiscovery. We conduct a series of behavioral experiments to evaluate the model and the artifact. Our initial findings indicate that the proposed model and the artifact improve performance in the IR result. (shrink)

Exploratory analysis is an area of increasing interest in the computational linguistics arena. Pragmatically speaking, exploratory analysis may be paraphrased as natural language processing by means of analyzing large corpora of text. Concerning the analysis, appropriate means are statistics, on the one hand, and artificial neural networks, on the other hand. As a challenging application area for exploratory analysis of text corpora we may certainly identify text databases, be it information retrieval or information filtering systems. With this paper we present (...) recent findings of exploratory analysis based on both statistical and neural models applied to legal text corpora. Concerning the artificial neural networks, we rely on a model adhering to the unsupervised learning paradigm. This choice appears naturally when taking into account the specific properties of large text corpora where one is faced with the fact that input-output-mappings as required by supervised learning models cannot be provided beforehand to a satisfying extent. This is due to the fact of the highly changing contents of text archives. In a nutshell, artificial neural networks count for their highly robust behavior regarding the parameters for model optimization. In particular, we found statistical classification techniques much more susceptible to minor parameter variations than unsupervised artificial neural networks. In this paper we describe two different lines of research in exploratory analysis. First, we use the classification methods for concept analysis. The general goal is to uncover different meanings of one and the same natural language concept. A task that, obviously, is of specific importance during the creation of thesauri. As a convenient environment to present the results we selected the legal term of neutrality, which is a perfect representative of a concept having a number of highly divergent meanings. Second, we describe the classification methods in the setting of document classification. The ultimate goal in such an application is to uncover semantic similarities of various text documents in order to increase the efficiency of an information retrieval system. In this sense, document classification has its fixed position in information retrieval research from the very beginning. Nowadays renewed massive interest in document classification may be witnessed due to the appearance of large-scale digital libraries. (shrink)

A variety of legal documents are increasingly being made available in electronic format. Automatic Information Search and Retrieval algorithms play a key role in enabling efficient access to such digitized documents. Although keyword-based search is the traditional method used for text retrieval, they perform poorly when literal term matching is done for query processing, due to synonymy and ambivalence of words. To overcome these drawbacks, an ontological framework to enhance the user’s query for retrieval of truly relevant legal judgments has (...) been proposed in this paper. Ontologies ensure efficient retrieval by enabling inferences based on domain knowledge, which is gathered during the construction of the knowledge base. Empirical results demonstrate that ontology-based searches generate significantly better results than traditional search methods. (shrink)

The BankXX system models the process of perusing and gathering information for argument as a heuristic best-first search for relevant cases, theories, and other domain-specific information. As BankXX searches its heterogeneous and highly interconnected network of domain knowledge, information is incrementally analyzed and amalgamated into a dozen desirable ingredients for argument (called argument pieces), such as citations to cases, applications of legal theories, and references to prototypical factual scenarios. At the conclusion of the search, BankXX outputs the set of argument (...) pieces filled with harvested material relevant to the input problem situation.This research explores the appropriateness of the search paradigm as a framework for harvesting and mining information needed to make legal arguments. In this article, we describe how legal research fits the heuristic search framework and detail how this model is used in BankXX. We describe the BankXX program with emphasis on its representation of legal knowledge and legal argument. We describe the heuristic search mechanism and evaluation functions that drive the program. We give an extended example of the processing of BankXX on the facts of an actual legal case in BankXX's application domain — the good faith question of Chapter 13 personal bankruptcy law. We discuss closely related research on legal knowledge representation and retrieval and the use of search for case retrieval or tasks related to argument creation. Finally we review what we believe are the contributions of this research to the understanding of the diverse disciplines it addresses. (shrink)

Legal judgments are complex in nature and hence a brief summary of the judgment, known as a headnote , is generated by experts to enable quick perusal. Headnote generation is a time consuming process and there have been attempts made at automating the process. The difficulty in interpreting such automatically generated summaries is that they are not coherent and do not convey the relative relevance of the various components of the judgment. A legal judgment can be segmented into coherent chunks (...) based on the rhetorical roles played by the sentences. In this paper, a comprehensive system is proposed for labeling sentences with their rhetorical roles and extracting structured head notes automatically from legal judgments. An annotated data set was created with the help of legal experts and used as training data. A machine learning technique, Conditional Random Field, is applied to perform document segmentation by identifying the rhetorical roles. The present work also describes the application of probabilistic models for the extraction of key sentences and composing the relevant chunks in the form of a headnote. The understanding of basic structures and distinct segments is shown to improve the final presentation of the summary. Moreover, by adding simple additional features the system can be extended to other legal sub-domains. The proposed system has been empirically evaluated and found to be highly effective on both the segmentation and summarization tasks. The final summary generated with underlying rhetorical roles improves the readability and efficiency of the system. (shrink)