Human-Level Performance on Word Analogy Questions by Latent Relational Analysis

This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus

Enhancing factoid question answering using frame semantic-based approaches

FrameNet is used to enhance the performance of semantic QA systems. FrameNet is a linguistic resource that encapsulates Frame Semantics and provides scenario-based generalizations over lexical items that share similar semantic backgrounds.Doctor of Philosoph

Similarity of Semantic Relations

There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM

Approaches to Using in Information Word Collocation Retrieval

The thesis explores long-span collocation and its application in information retrieval. The basic research question of the thesis is whether the use of long-span collocates can improve performance of a probabilistic model of IR. The model used in the project is the Robertson & Sparck Jones probabilistic model. The basic research question was explored by investigating three different ways of integrating collocation information with the probabilistic model: 1. Global collocation analysis. The method consists in expanding the original query with long-span global collocates of query terms. Global collocates of a query term are selected from large fixed-size windows around all occurrences of a term in the corpus and ranked by statistical measures of Mutual Information (MI) and Z score. A fixed number of top-ranked collocates is used in query expansion. Query expansion with global collocates did not show to be superior to the original queries, the possible reason being the fact that query terms often have a fairly broad meaning and, hence, a rather semantically heterogeneous pattern of occurrence. 2. Local collocation analysis. This method is a form of iterative query expansion following relevance or pseudo-relevance (blind) feedback. The original query is expanded with the query terms’ collocates which are extracted from the long-span windows around all occurrences of query terms in the known relevant documents, and selected using statistical measures of MI and Z. Some parameters whose effect was systematically studied in this experiment set are: window size, measure of collocation significance for collocate ranking, number of query expansion collocates and categories of terms in the expanded queries. Some results showed a tendency towards performance gain over relevance feedback in the probabilistic model, however it was not significant enough to conclude that this method is superior to the existing relevance feedback used in the model. 3. Lexical cohesion analysis using local collocations. This experiment set aimed to explore whether the level of lexical cohesion between query terms in a document can be linked to the document’s relevance property, and if so, whether it can be used to predict documents’ relevance to the query. Lexical cohesion between different query terms is estimated from the number of collocates they have in common. The experiments proved that there exists a statistically significant association between the level of lexical cohesion of the query terms in documents and relevance. Another set of experiments, aimed at using lexical cohesion to improve probabilistic document ranking, showed that sets re-ranked by their lexical cohesion scores have similar performance as the original ranking

Event-Based Modelling in Question Answering

In der natürlichen Sprachverarbeitung haben Frage-Antwort-Systeme in der letzten Dekade stark an Bedeutung gewonnen. Vor allem durch robuste Werkzeuge wie statistische Syntax-Parser und Eigennamenerkenner ist es möglich geworden, linguistisch strukturierte Informationen aus unannotierten Textkorpora zu gewinnen. Zusätzlich werden durch die Text REtrieval Conference (TREC) jährlich Maßstäbe für allgemeine domänen-unabhängige Frage-Antwort-Szenarien definiert. In der Regel funktionieren Frage-Antwort-Systeme nur gut, wenn sie robuste Verfahren für die unterschiedlichen Fragetypen, die in einer Fragemenge vorkommen, implementieren. Ein charakteristischer Fragetyp sind die sogenannten Ereignisfragen. Obwohl Ereignisse schon seit Mitte des vorigen Jahrhunderts in der theoretischen Linguistik, vor allem in der Satzsemantik, Gegenstand intensive Forschung sind, so blieben sie bislang im Bezug auf Frage-Antwort-Systeme weitgehend unerforscht. Deshalb widmet sich diese Diplomarbeit diesem Problem. Ziel dieser Arbeit ist zum Einen eine Charakterisierung von Ereignisstruktur in Frage-Antwort Systemen, die unter Berücksichtigung der theoretischen Linguistik sowie einer Analyse der TREC 2005 Fragemenge entstehen soll. Zum Anderen soll ein Ereignis-basiertes Antwort-Extraktionsverfahren entworfen und implementiert werden, das sich auf den Ergebnissen dieser Analyse stützt. Informationen von diversen linguistischen Ebenen sollen daten-getrieben in einem uniformen Modell integriert werden. Spezielle linguistische Ressourcen, wie z.B. WordNet und Subkategorisierungslexika werden dabei eine zentrale Rolle einnehmen. Ferner soll eine Ereignisstruktur vorgestellt werden, die das Abpassen von Ereignissen unabhängig davon, ob sie von Vollverben oder Nominalisierungen evoziert werden, erlaubt. Mit der Implementierung eines Ereignis-basierten Antwort-Extraktionsmoduls soll letztendlich auch die Frage beantwortet werden, ob eine explizite Ereignismodellierung die Performanz eines Frage-Antwort-Systems verbessern kann

A text mining approach for Arabic question answering systems

As most of the electronic information available nowadays on the web is stored as text,developing Question Answering systems (QAS) has been the focus of many individualresearchers and organizations. Relatively, few studies have been produced for extractinganswers to “why” and “how to” questions. One reason for this negligence is that when goingbeyond sentence boundaries, deriving text structure is a very time-consuming and complexprocess. This thesis explores a new strategy for dealing with the exponentially large spaceissue associated with the text derivation task. To our knowledge, to date there are no systemsthat have attempted to addressing such type of questions for the Arabic language.We have proposed two analytical models; the first one is the Pattern Recognizer whichemploys a set of approximately 900 linguistic patterns targeting relationships that hold withinsentences. This model is enhanced with three independent algorithms to discover thecausal/explanatory role indicated by the justification particles. The second model is the TextParser which is approaching text from a discourse perspective in the framework of RhetoricalStructure Theory (RST). This model is meant to break away from the sentence limit. TheText Parser model is built on top of the output produced by the Pattern Recognizer andincorporates a set of heuristics scores to produce the most suitable structure representing thewhole text.The two models are combined together in a way to allow for the development of an ArabicQAS to deal with “why” and “how to” questions. The Pattern Recognizer model achieved anoverall recall of 81% and a precision of 78%. On the other hand, our question answeringsystem was able to find the correct answer for 68% of the test questions. Our results revealthat the justification particles play a key role in indicating intrasentential relations

Spoken content retrieval beyond pipeline integration of automatic speech recognition and information retrieval

The dramatic increase in the creation of multimedia content is leading to the development of large archives in which a substantial amount of the information is in spoken form. Efficient access to this information requires effective spoken content retrieval (SCR) methods. Traditionally, SCR systems have focused on a pipeline integration of two fundamental technologies: transcription using automatic speech recognition (ASR) and search supported using text-based information retrieval (IR). Existing SCR approaches estimate the relevance of a spoken retrieval item based on the lexical overlap between a user’s query and the textual transcriptions of the items. However, the speech signal contains other potentially valuable non-lexical information that remains largely unexploited by SCR approaches. Particularly, acoustic correlates of speech prosody, that have been shown useful to identify salient words and determine topic changes, have not been exploited by existing SCR approaches. In addition, the temporal nature of multimedia content means that accessing content is a user intensive, time consuming process. In order to minimise user effort in locating relevant content, SCR systems could suggest playback points in retrieved content indicating the locations where the system believes relevant information may be found. This typically requires adopting a segmentation mechanism for splitting documents into smaller “elements” to be ranked and from which suitable playback points could be selected. Existing segmentation approaches do not generalise well to every possible information need or provide robustness to ASR errors. This thesis extends SCR beyond the standard ASR and IR pipeline approach by: (i) exploring the utilisation of prosodic information as complementary evidence of topical relevance to enhance current SCR approaches; (ii) determining elements of content that, when retrieved, minimise user search effort and provide increased robustness to ASR errors; and (iii) developing enhanced evaluation measures that could better capture the factors that affect user satisfaction in SCR

Distributed Inverted Files and Performance: A Study of Parallelism and Data Distribution Methods in IR

The study investigates the performance of parallel information retrieval (IR) algorithms on different data distribution methods for Inverted files to identify which is the best for the requirements of specific IR tasks. We define a data distribution method as a way of distributing Inverted file data to local disks on a parallel machine. A data distribution method may be on-the-fly (with one copy of the index held), replication (all nodes have all of the index) or partitioning (data for index is split amongst nodes). Partitioning of inverted file data can be done in many ways but we consider only two: by term (Termld) and by document (Dodd). Termld partitioning is a type of partitioning which distributes unique word data to a single partition, while D odd partitioning distributes unique document data to a single partition. We consider the issue of improving the performance of standard IR algorithms on these data distribution methods by looking at sequential job service not concurrent job service, e.g. we consider the issue of sequential query service not concurrent query service. This methodology rules out some distribution methods for some tasks studied. We consider the following main tasks of IR: indexing, search, passage retrieval, inverted file update and query optimisation for routing /filtering. We produce a synthetic performance model for each of these tasks for the purposes of comparison. We have two subsidiary aims; one was to demonstrate portability of our implemented data structures and algorithms on different parallel machines. Secondly, we also study the possibility of increased retrieval effectiveness by examining a larger section of the search space for both passage retrieval and routing/filtering. We consider the implications of concurrency in updates on Inverted files. Our theoretical and empirical results show that in most cases the D odd partitioning method is the best data distribution method apart from routing/filtering where replication was found to be superior

A framework for enhancing the query and medical record representations for patient search

Electronic medical records (EMRs) are digital documents stored by medical institutions that detail the observed symptoms, the conducted diagnostic tests, the identified diagnoses and the prescribed treatments. These EMRs are being increasingly used worldwide to improve healthcare services. For example, when a doctor compiles the possible treatments for a patient showing some particular symptoms, it is advantageous to consult the information about patients who were previously treated for those same symptoms. However, finding patients with particular medical conditions is challenging, due to the implicit knowledge inherent within the patients' medical records and queries - such knowledge may be known by medical practitioners, but may be hidden from an information retrieval (IR) system. For instance, the mention of a treatment such as a drug may indicate to a practitioner that a particular diagnosis has been made for the patient, but this diagnosis may not be explicitly mentioned in the patient's medical records. Moreover, the use of negated language (e.g.\ `without', `no') to describe a medical condition of a patient (e.g.\ the patient has no fever) may cause a search system to erroneously retrieve that patient for a query when searching for patients with that medical condition (e.g.\ find patients with fever). This thesis focuses on enhancing the search of EMRs, with the aim of identifying patients with medical histories relevant to the medical conditions stated in a text query. During retrieval, a healthcare practitioner indicates a number of inclusion criteria describing the medical conditions of the patients of interest. To attain effective retrieval performance, we hypothesise that, in a patient search system, both the information needs and patients' histories should be represented based upon \emph{the medical decision process}. In particular, this thesis argues that since the medical decision process typically encompasses four aspects (symptom, diagnostic test, diagnosis and treatment), a patient search system should take into account these aspects and apply inferences to recover the possible implicit knowledge. We postulate that considering these aspects and their derived implicit knowledge at three different levels of the retrieval process (namely, sentence, medical record and inter-record levels) enhances the retrieval performance. Indeed, we propose a novel framework that can gain insights from EMRs and queries, by modelling and reasoning upon information during retrieval in terms of the four aforementioned aspects at the three levels of the retrieval process, and can use these insights to enhance patient search. Firstly, at the sentence level, we extract the medical conditions in the medical records and queries. In particular, we propose to represent only the medical conditions related to the four medical aspects in order to improve the accuracy of our search system. In addition, we identify the context (negative/positive) of terms, which leads to an accurate representation of the medical conditions both in the EMRs and queries. In particular, we aim to prevent patients whose EMRs state the medical conditions in the contexts different from the query from being ranked highly. For example, preventing patients whose EMRs state ``no history of dementia'' from being retrieved for a query searching for patients with dementia. Secondly, at the medical record level, using external knowledge-based resources (e.g.\ ontologies and health-related websites), we leverage the relationships between medical terms to infer the wider medical history of the patient in terms of the four medical aspects. In particular, we estimate the relevance of a patient to the query by exploiting association rules that we extract from the semantic relationships between medical terms using the four aspects of the medical process. For example, patients with a medical history involving a \emph{CABG surgery} (treatment) can be inferred as relevant to a query searching for a patient suffering from \emph{heart disease} (diagnosis), since a CABG surgery is a treatment of heart disease. Thirdly, at the inter-record level, we enhance the retrieval of patients in two different manners. First, we exploit knowledge about how the four medical aspects are handled by different hospital departments to gain a better understanding about the appropriateness of EMRs created by different departments for a given query. We propose to aggregate EMRs at the department level (i.e.\ inter-record level) to extract implicit knowledge (i.e.\ the expertise of each department) and model this department's expertise, while ranking patients. For instance, patients having EMRs from the cardiology department are likely to be relevant to a query searching for patients who suffered from a heart attack. Second, as a medical query typically contains several medical conditions that the relevant patients should satisfy, we propose to explicitly model the relevance towards multiple query medical conditions in the EMRs related to a particular patient during retrieval. In particular, we rank highly those patients that match all the stated medical conditions in the query by adapting coverage-based diversification approaches originally proposed for the web search domain. Finally, we examine the combination of our aforementioned approaches that exploit the implicit knowledge at the three levels of the retrieval process to further improve the retrieval performance by adapting techniques from the fields of data fusion and machine learning. In particular, data fusion techniques, such as CombSUM and CombMNZ, are used to combine the relevance scores computed by the different approaches of the proposed framework. On the other hand, we deploy state-of-the-art learning to rank approaches (e.g.\ LambdaMART and AdaRank) to learn from a set of training data an effective combination of the relevance scores computed by the approaches of the framework. In addition, we introduce a novel selective ranking approach that uses a classifier to effectively apply one of the approaches of the framework on a per-query basis. This thesis draws insights from a thorough evaluation and analysis of the proposed framework using a standard test collection provided by the TREC Medical Records track. The experimental results show the effectiveness of the framework. In particular, the results demonstrate the importance of dealing with the implicit knowledge in patient search by focusing on the medical decision criteria aspects at the three levels of the retrieval process