Maya Carrillo

Representing Context Information for Document Retrieval

The bag of words representation (BoW), which is widely used in information retrieval (IR), represents documents and queries as word lists that do not express anything about context information. When we look for information, we find that not everything is explicitly stated in a document, so context information is needed to understand its content. This paper proposes the use of bag of concepts (BoC) and Holographic reduced representation (HRR) in IR. These representations go beyond BoW by incorporating context information to document representations. Both HRR and BoC are produced using a vector space methodology known as Random Indexing, and allow expressing additional knowledge from different sources. Our experiments have shown the feasibility of the representations and improved the mean average precision by up to 7% when they are compared with the traditional vector space model.

Combining Text Vector Representations for Information Retrieval

This paper suggests a novel representation for documents that is intended to improve precision. This representation is generated by combining two central techniques: Random Indexing; and Holographic Reduced Representations (HRRs). Random indexing uses co-occurrence information among words to generate semantic context vectors that are the sum of randomly generated term identity vectors. HRRs are used to encode textual structure which can directly capture relations between words (e.g., compound terms, subject-verb, and verb-object). By using the random vectors to capture semantic information, and then employing HRRs to capture structural relations extracted from the text, document vectors are generated by summing all such representations in a document. In this paper, we show that these representations can be successfully used in information retrieval, can effectively incorporate relations, and can reduce the dimensionality of the traditional vector space model (VSM). The results of our experiments show that, when a representation that uses random index vectors is combined with different contexts, such as document occurrence representation (DOR), term co-occurrence representation (TCOR) and HRRs, the VSM representation is outperformed when employed in information retrieval tasks.

An Intelligent Tutoring System to Evaluate and Advise on Lexical Richness in Students Writings

Lexical competence, the writer ability to use properly vocabulary, becomes a basic issue of a writing instructor when reviewing drafts. Here, we present the basic part of a web-based intelligent tutoring system to provide student guidance and evaluation in structuring research proposals. We elaborate a network-based model to follow the progress of each student in the development of the project, supply assignments and personalized feedback on each evaluation. This tutor includes for now a module for assessing the lexical richness, in terms of three measures: variety, density, and sophistication, that are described. We also explain the methodology for pilot testing with undergraduate students, whose results were encouraging, indicating that the tutor indeed helps students.

Concept Based Representations as Complement of Bag of Words in Information Retrieval

Information Retrieval models, which do not represent texts merely as collections of the words they contain, but rather as collections of the concepts they contain through synonym sets or latent dimensions, are known as Bag-of-Concepts (BoC) representations. In this paper we use random indexing, which uses co-occurrence information among words to generate semantic context vectors and then represent the documents and queries as BoC. In addition, we use a novel representation, Holographic Reduced Representation, previously proposed in cognitive models, which can encode relations between words. We show that these representations can be successfully used in information retrieval, can associate terms, and when they are combined with the traditional vector space model, they improve effectiveness, in terms of mean average precision.

Development of an Ontologies System for Spatial Biomedical Applications

In this paper a process of creating ontologies system based on other existing ontologies is described, in order to response biomedical spatial queries on the Web. GeOntoMex is a Mexican spatial ontology, which is structured according to its political-administrative division, in addition, axioms are defined to represent the spatial relationships between geographic entities. Moreover, the Health Onto Mex ontology, whose structure corresponds to the INEGIs taxonomy (National Institute of Statistics and Geography) health services, is presented. Later, a system based on the aforementioned ontologies is shown. The system named Geo Health Onto Mex, could lead to more accurate user queries that requires a specific medical service in a given geographical area.

A Classification Model with Corpus Enrichment for Toponym Disambiguation

This paper presents a method based on information retrieval to enrich corpus using bootstrapping techniques. A supervised corpus manually validated is provided, and then snippets are obtained from Web in order to increase the size of the initial corpus. Although this technique has already been reported in the literature, the main objective of this work is to apply it under the specific task of GEO/NO-GEO toponym disambiguation.The disambiguation procedure is evaluated by a classification model observing favorable results.

Subjects identification using EEG-recorded imagined speech

Abstract Due to the problems presented in current traditional/biometric security systems, the interest to use new security systems, have been increasing. This paper explores the use of brain signals EEG-based during imagined speech in order to use it as a new biometric measure for Subjects identification and thus create a new biometric security system. The main contribution of this paper are two methods for feature extraction, first to improve the signal-to-noise ratio the Common Average Reference was applied. The first method was based on Discrete Wavelet Transform, and the second method was based on statistical features directly from the raw signal. The proposed methods were tested in a dataset of 27 Subjects who performed 33 repetitions of 5 imagined words in Spanish. The results show the feasibility of the task with accurate identification of the Subject, regardless of the imagined word used and using a commercial EEG system (EMOTIV EPOC). In addition, the scope of the method is displayed by decreasing the training data, as well as the number of active sensors for the identification task. Using the proposed method with future improvements and implementing it in a low-cost device can be a new and valuable biometric security system.

Concept based representations for ranking in geographic information retrieval

Geographic Information Retrieval (GIR) is a specialized Information Retrieval (IR) branch that deals with information related to geographical locations. Traditional IR engines are perfectly able to retrieve the majority of the relevant documents for most geographical queries, but they have severe difficulties generating a pertinent ranking of the retrieved results, which leads to poor performance. A key reason for this ranking problem has been a lack of information. Therefore, previous GIR research has tried to fill this gap using robust geographical resources (i.e. a geographical ontology), while other research with the same aim has used relevant feedback techniques instead. This paper explores the use of Bag of Concepts (BoC; a representation where documents are considered as the union of the meanings of its terms) and Holographic Reduced Representation (HRR; a novel representation for textual structure) as re-ranking mechanisms for GIR. Our results reveal an improvement in mean average precision (MAP) when compared to the traditional vector space model, even if Pseudo Relevance Feedback is employed.

Towards an Enhanced Vector Model to Encode Textual Relations: Experiments Retrieving Information

The constant growth of digital information, facilitated by storage technologies, imposes new challenges for information processing tasks, and maintains the need of effective search mechanisms, oriented towards improving in precision but simultaneously capable of producing useful information in a short time. Hence, this paper presents a document representation to encode textual relations. This representation does not consider each term as one entry in a vector but rather as a pattern, i.e. a set of contiguous entries. To deal with variations inherent in natural language, we plan to express textual relations (such as noun phrases, named entities, subject-verb, verb-object, adjective-noun, and adverb-verb) as composed patterns. An operator is applied to form bindings between terms encoding relations as new “terms”, thereby providing additional descriptive elements for indexing a document collection. The results of our first experiments, using the document representation to conduct information retrieval and incorporating two-word noun phrases, showed that the representation is feasible, retrieves, and improves the ranking of relevant documents, and consequently the values of mean average precision.