Non-Compositional Term Dependence for Information Retrieval

Modelling term dependence in IR aims to identify co-occurring terms that are too heavily dependent on each other to be treated as a bag of words, and to adapt the indexing and ranking accordingly. Dependent terms are predominantly identified using lexical frequency statistics, assuming that (a) if terms co-occur often enough in some corpus, they are semantically dependent; (b) the more often they co-occur, the more semantically dependent they are. This assumption is not always correct: the frequency of co-occurring terms can be separate from the strength of their semantic dependence. E.g. "red tape" might be overall less frequent than "tape measure" in some corpus, but this does not mean that "red"+"tape" are less dependent than "tape"+"measure". This is especially the case for non-compositional phrases, i.e. phrases whose meaning cannot be composed from the individual meanings of their terms (such as the phrase "red tape" meaning bureaucracy). Motivated by this lack of distinction between the frequency and strength of term dependence in IR, we present a principled approach for handling term dependence in queries, using both lexical frequency and semantic evidence. We focus on non-compositional phrases, extending a recent unsupervised model for their detection [21] to IR. Our approach, integrated into ranking using Markov Random Fields [31], yields effectiveness gains over competitive TREC baselines, showing that there is still room for improvement in the very well-studied area of term dependence in IR

Term Clustering of Syntactic Phrases

Term clustering and syntactic phrase formation are methods for transforming natural language text. Both have had only mixed success as strategies for improving the quality of text representations for document retrieval. Since the strengths of these methods are complementary, we have explored combining them to produce superior representations. In this paper we discuss our implementation of a syntactic phrase generator, as well as our preliminary experiments with producing phrase clusters. These experiments show small improvements in retrieval effectiveness resulting from the use of phrase clusters, but it is clear that corpora much larger than standard information retrieval test collections will be required to thoroughly evaluate the use of this technique

Indexação automática de textos

Descreve um programa experimental de análise automática de textos desenvolvido na Universidade de Brasília. Os passos da análise lingüística seguidos no presente modelo são: — segmentação (de um texto em frases e palavras); — procura no dicionário e análise morfológica de palavras portuguesas; — desembiguação de homografias sintáticas; — construção de uma árvore de dependência. Com estes algoritmos, várias contribuições podem ser produzidas para ajudar o indexador ou para integrar, junto com processos estatísticos adicionais, um sistema de indexação automática; — descritores simples em forma-base; — descritores compostos (grupos nominais); — descritores com peso, baseado nas funções sintáticas dentro da frase e em traços semânticos, com listas e/ou tesauros. O programa experimental está sendo testado com várias bases de dados

A stemming algorithm for Latvian

The thesis covers construction, application and evaluation of a stemming algorithm for advanced information searching and retrieval in Latvian databases. Its aim is to examine the following two questions: Is it possible to apply for Latvian a suffix removal algorithm originally designed for English? Can stemming in Latvian produce the same or better information retrieval results than manual truncation? In order to achieve these aims, the role and importance of automatic word conflation both for document indexing and information retrieval are characterised. A review of literature, which analyzes and evaluates different types of stemming techniques and retrospective development of stemming algorithms, justifies the necessity to apply this advanced IR method also for Latvian. Comparative analysis of morphological structure both for English and Latvian language determined the selection of Porter's suffix removal algorithm as a basis for the Latvian sternmer. An extensive list of Latvian stopwords including conjunctions, particles and adverbs, was designed and added to the initial sternmer in order to eliminate insignificant words from further processing. A number of specific modifications and changes related to the Latvian language were carried out to the structure and rules of the original stemming algorithm. Analysis of word stemming based on Latvian electronic dictionary and Latvian text fragments confirmed that the suffix removal technique can be successfully applied also to Latvian language. An evaluation study of user search statements revealed that the stemming algorithm to a certain extent can improve effectiveness of information retrieval

Improving Automated Requirements Trace Retrieval Through Term-Based Enhancement Strategies

Requirements traceability is concerned with managing and documenting the life of requirements. Its primary goal is to support critical software development activities such as evaluating whether a generated software system satisfies the specified set of requirements, checking that all requirements have been implemented by the end of the lifecycle, and analyzing the impact of proposed changes on the system. Various approaches for improving requirements traceability practices have been proposed in recent years. Automated traceability methods that utilize information retrieval (IR) techniques have been recognized to effectively support the trace generation and retrieval process. IR based approaches not only significantly reduce human effort involved in manual trace generation and maintenance, but also allow the analyst to perform tracing on an “as-needed” basis. The IR-based automated traceability tools typically retrieve a large number of potentially relevant traceability links between requirements and other software artifacts in order to return to the analyst as many true links as possible. As a result, the precision of the retrieval results is generally low and the analyst often needs to manually filter out a large amount of unwanted links. The low precision among the retrieved links consequently impacts the usefulness of the IR-based tools. The analyst’s confidence in the effectiveness of the approach can be negatively affected both by the presence of a large number of incorrectly retrieved traces, and the number of true traces that are missed. In this thesis we present three enhancement strategies that aim to improve precision in trace retrieval results while still striving to retrieve a large number of traceability links. The three strategies are: 1) Query term coverage (TC) This strategy assumes that a software artifact sharing a larger proportion of distinct words with a requirement is more likely to be relevant to that requirement. This concept is defined as query term coverage (TC). A new approach is introduced to incorporate the TC factor into the basic IR model such that the relevance ranking for query-document pairs that share two or more distinct terms will be increased and the retrieval precision is improved. 2) Phrasing The standard IR models generate similarity scores for links between a query and a document based on the distribution of single terms in the document collection. Several studies in the general IR area have shown phrases can provide a more accurate description of document content and therefore lead to improvement in retrieval [21, 23, 52]. This thesis therefore presents an approach using phrase detection to enhance the basic IR model and to improve its retrieval accuracy. 3) Utilizing a project glossary Terms and phrases defined in the project glossary tend to capture the critical meaning of a project and therefore can be regarded as more meaningful for detecting relations between documents compared to other more general terms. A new enhancement technique is then introduced in this thesis that utilizes the information in the project glossary and increases the weights of terms and phrases included in the project glossary. This strategy aims at increasing the relevance ranking of documents containing glossary items and consequently at improving the retrieval precision. The incorporation of these three enhancement strategies into the basic IR model, both individually and synergistically, is presented. Extensive empirical studies have been conducted to analyze and compare the retrieval performance of the three strategies. In addition to the standard performance metrics used in IR, a new metric average precision change [80] is also introduced in this thesis to measure the accuracy of the retrieval techniques. Empirical results on datasets with various characteristics show that the three enhancement methods are generally effective in improving the retrieval results. The improvement is especially significant at the top of the retrieval results which contains the links that will be seen and inspected by the analyst first. Therefore the improvement is especially meaningful as it implies the analyst may be able to evaluate those important links earlier in the process. As the performance of these enhancement strategies varies from project to project, the thesis identifies a set of metrics as possible predictors for the effectiveness of these enhancement approaches. Two such predictors, namely average query term coverage (QTC) and average phrasal term coverage (PTC), are introduced for the TC and the phrasing approach respectively. These predictors can be employed to identify which enhancement algorithm should be used in the tracing tool to improve the retrieval performance for specific documents collections. Results of a small-scale study indicate that the predictor values can provide useful guidelines to select a specific tracing approach when there is no prior knowledge on a given project. The thesis also presents criteria for evaluating whether an existing project glossary can be used to enhance results in a given project. The project glossary approach will not be effective if the existing glossary is not being consistently followed in the software development. The thesis therefore presents a new procedure to automatically extract critical keywords and phrases from the requirements collection of a given project. The experimental results suggest that these extracted terms and phrases can be used effectively in lieu of missing or ineffective project glossary to help improve precision of the retrieval results. To summarize, the work presented in this thesis supports the development and application of automated tracing tools. The three strategies share the same goal of improving precision in the retrieval results to address the low precision problem, which is a big concern associated with the IR-based tracing methods. Furthermore, the predictors for individual enhancement strategies presented in this thesis can be utilized to identify which strategy will be effective in the specific tracing tasks. These predictors can be adopted to define intelligent tracing tools that can automatically determine which enhancement strategy should be applied in order to achieve the best retrieval results on the basis of the metrics values. A tracing tool incorporating one or more of these methods is expected to achieve higher precision in the trace retrieval results than the basic IR model. Such improvement will not only reduce the analyst’s effort of inspecting the retrieval results, but also increase his or her confidence in the accuracy of the tracing tool

The Academic Library in Society’s Knowledge System : a Case Study of Tshwane University of Technology

Academic libraries traditionally support universities in their teaching, learning, and research activities. Their support roles can be broadly defined in terms of the organization and storage of knowledge, as well as its distribution and access. This makes them important role players in South Africa’s broader library and information services ecosystem. As a result, academic libraries do not operate in a vacuum. As part of the broader society the targeting of academic libraries during student protest action represents a unique type of social conflict. This unique type of social conflict is not fully understood and this study investigated Tshwane University of Technology’s libraries using the idea of a knowledge system as a theoretical framework. The main argument of the study is that academic libraries have a historical relationship with research libraries, and have an important connection with society’s knowledge system. The oversight of the functions of academic libraries problematizes their role and response to social conflict. The study used a focused literature review and documentary evidence, and data was collected from a purposive sample using an electronic survey questionnaire, focus groups and interviews. The study found that TUT’s libraries passive response to the 2015 and 2016 student disruptions stems from a poor understanding of the theoretical context of their support roles and functions. The main value of the study is to call attention to the idea of a knowledge system and to enable TUT’s libraries to respond adequately to social conflict.Dissertation (MIS)--University of Pretoria, 2018.This work was supported by the Tshwane University of Technology [grant number 168409].Information ScienceMISUnrestricte

A Hybrid Model for Document Retrieval Systems.

A methodology for the design of document retrieval systems is presented. First, a composite index term weighting model is developed based on term frequency statistics, including document frequency, relative frequency within document and relative frequency within collection, which can be adjusted by selecting various coefficients to fit into different indexing environments. Then, a composite retrieval model is proposed to process a user\u27s information request in a weighted Phrase-Oriented Fixed-Level Expression (POFLE), which may apply more than Boolean operators, through two phases. That is, we have a search for documents which are topically relevant to the information request by means of a descriptor matching mechanism, which incorporate a partial matching facility based on a structurally-restricted relationship imposed by indexing model, and is more general than matching functions of the traditional Boolean model and vector space model, and then we have a ranking of these topically relevant documents, by means of two types of heuristic-based selection rules and a knowledge-based evaluation function, in descending order of a preference score which predicts the combined effect of user preference for quality, recency, fitness and reachability of documents

Logical-Linguistic Model and Experiments in Document Retrieval

Conventional document retrieval systems have relied on the extensive use of the keyword approach with statistical parameters in their implementations. Now, it seems that such an approach has reached its upper limit of retrieval effectiveness, and therefore, new approaches should be investigated for the development of future systems. With current advances in hardware, programming languages and techniques, natural language processing and understanding, and generally, in the field of artificial intelligence, there are now attempts being made to include linguistic processing into document retrieval systems. Few attempts have been made to include parsing or syntactic analysis into document retrieval systems, and the results reported show some improvements in the level of retrieval effectiveness. The first part of this thesis sets out to investigate further the use of linguistic processing by including translation, instead of only parsing, into a document retrieval system. The translation process implemented is based on unification categorial grammar and uses C-Prolog as the building tool. It is used as the main part of the indexing process of documents and queries into a knowledge base predicate representation. Instead of using the vector space model to represent documents and queries, we have used a kind of knowledge base model which we call logical-linguistic model. A development of a robust parser-translator to perform the translation is discussed in detail in the thesis. A method of dealing with ambiguity is also incorporated in the parser-translator implementation. The retrieval process of this model is based on a logical implication process implemented in C-Prolog. In order to handle uncertainty in evaluating similarity values between documents and queries, meta level constructs are built upon the C-Prolog system. A logical meta language, called UNIL (UNcertain Implication Language), is proposed for controlling the implication process. Using UNIL, one can write a set of implication rules and thesaurus to define the matching function of a particular retrieval strategy. Thus, we have demonstrated and implemented the matching operation between a document and a query as an inference using unification. An inference from a document to a query is done in the context of global information represented by the implication rules and the thesaurus. A set of well structured experiments is performed with various retrieval strategies on a test collection of documents and queries in order to evaluate the performance of the system. The results obtained are analysed and discussed. The second part of the thesis sets out to implement and evaluate the imaging retrieval strategy as originally defined by van Rijsbergen. The imaging retrieval is implemented as a relevance feedback retrieval with nearest neighbour information which is defined as follows. One of the best retrieval strategies from the earlier experiments is chosen to perform the initial ranking of the documents, and a few top ranked documents will be retrieved and identified as relevant or not by the user. From this set of retrieved and relevant documents, we can obtain all other unretrieved documents which have any of the retrieved and relevant documents as their nearest neighbour. These unretrieved documents have the potential of also being relevant since they are 'close' to the retrieved and relevant ones, and thus their initial similarity values to the query will be updated according to their distances from their nearest neighbours. From the updated similarity values, a new ranking of documents can be obtained and evaluated. A few sets of experiments using imaging retrieval strategy are performed for the following objectives: to search for an appropriate updating function in order to produce a new ranking of documents, to determine an appropriate nearest neighbour set, to find the relationship of the retrieval effectiveness to the size of the documents shown to the user for relevance judgement, and lastly, to find the effectiveness of a multi-stage imaging retrieval. The results obtained are analysed and discussed. Generally, the thesis sets out to define the logical-linguistic model in document retrieval and demonstrates it by building an experimental system which will be referred to as SILOL (a Simple Logical-linguistic document retrieval system). A set of retrieval strategies will be experimented with and the results obtained will be analysed and discussed