Manuel Palomar

Text summarisation in progress: a literature review

This paper contains a large literature review in the research field of Text Summarisation (TS) based on Human Language Technologies (HLT). TS helps users manage the vast amount of information available, by condensing documents’ content and extracting the most relevant facts or topics included in them. The rapid development of emerging technologies poses new challenges to this research field, which still need to be solved. Therefore, it is essential to analyse its progress over the years, and provide an overview of the past, present and future directions, highlighting the main advances achieved and outlining remaining limitations. With this purpose, several important aspects are addressed within the scope of this survey. On the one hand, the paper aims at giving a general perspective on the state-of-the-art, describing the main concepts, as well as different summarisation approaches, and relevant international forums. Furthermore, it is important to stress upon the fact that the birth of new requirements and scenarios has led to new types of summaries with specific purposes (e.g. sentiment-based summaries), and novel domains within which TS has proven to be also suitable for (e.g. blogs). In addition, TS is successfully combined with a number of intelligent systems based on HLT (e.g. information retrieval, question answering, and text classification). On the other hand, a deep study of the evaluation of summaries is also conducted in this paper, where the existing methodologies and systems are explained, as well as new research that has emerged concerning the automatic evaluation of summaries’ quality. Finally, some thoughts about TS in general and its future will encourage the reader to think of novel approaches, applications and lines to conduct research in the next years. The analysis of these issues allows the reader to have a wide and useful background on the main important aspects of this research field.

An object oriented approach to multidimensional database conceptual modeling (OOMD)

In the recent past, there has been an increasing interest in multidimensional databases (MDB) and On-line Analytical Processing (OLAP) scenarios. Several multidimensional models have been proposed in the last days. However, very few works have been focused on the area of multidimensional database conceptual modeling. Moreover, they are either conceptual extensions to the classical multidimensional model or translations from classical database conceptual models (such as the EntityRelationship model). Nevertheless, we take the concepts and basic ideas of the classical multidimensional model (dimensions and facts) to propose a revolutionary approach based on the Object Oriented (OO) Paradigm to MDB conceptual modeling. Then, the basic elements of our Object Oriented Multidimensional Model (OOMD) such as dimension classes and fact classes are introduced. We then present cube classes as the basic structure to allow a subsequent analysis of the data stored in the system. We fairly believe that the utilization of the OO Paradigm will provide us a general conceptual model to MDB conceptual modeling in a more flexible, natural and simple way than the models proposed until now.

An algorithm for anaphora resolution in Spanish texts

This paper presents an algorithm for identifying noun phrase antecedents of third person personal pronouns, demonstrative pronouns, reflexive pronouns, and omitted pronouns (zero pronouns) in unrestricted Spanish texts. We define a list of constraints and preferences for different types of pronominal expressions, and we document in detail the importance of each kind of knowledge (lexical, morphological, syntactic, and statistical) in anaphora resolution for Spanish. The paper also provides a definition for syntactic conditions on Spanish NP-pronoun noncoreference using partial parsing. The algorithm has been evaluated on a corpus of 1,677 pronouns and achieved a success rate of 76.8. We have also implemented four competitive algorithms and tested their performance in a blind evaluation on the same test corpus. This new approach could easily be extended to other languages such as English, Portuguese, Italian, or Japanese.

Combining knowledge- and corpus-based word-sense-disambiguation methods

In this paper we concentrate on the resolution of the lexical ambiguity that arises when a given word has several different meanings. This specific task is commonly referred to as word sense disambiguation (WSD). The task of WSD consists of assigning the correct sense to words using an electronic dictionary as the source of word definitions. We present two WSD methods based on two main methodological approaches in this research area: a knowledge-based method and a corpus-based method. Our hypothesis is that word-sense disambiguation requires several knowledge sources in order to solve the semantic ambiguity of the words. These sources can be of different kinds-- for example, syntagmatic, paradigmatic or statistical information. Our approach combines various sources of knowledge, through combinations of the two WSD methods mentioned above. Mainly, the paper concentrates on how to combine these methods and sources of information in order to achieve good results in the disambiguation. Finally, this paper presents a comprehensive study and experimental work on evaluation of the methods and their combinations.

An Empirical Approach to Spanish Anaphora Resolution

This paper documents the development of an empirically-basedsystem implemented in Prolog that automatically resolves severalkinds of anaphora in Spanish texts. These are pronominalreferences, surface-count anaphora, one-anaphora and ellipticalzero-subject constructions (i.e., sentences that omit theirpronominal subject). The resolution is based onrepresentations resulting from either partial or full parsing. Thesystem developed can also work on the output of a POStagger or with different dictionaries, without changing thegrammar. This grammar represents the syntactic information of eachlanguage by means of the Slot Unification Grammar formalism. The different kinds of information used for anaphora resolution in full and partial parsing are shown, as wellas evaluation results. The system has been adapted toEnglish texts, obtaining encouraging results that prove that itcan be applied with only a very few refinements to other languagesas well as Spanish and English. In addition, the differencesbetween English and Spanish anaphora are noted.

A Perspective-Based Approach for Solving Textual Entailment Recognition

The textual entailment recognition system that we discuss in this paper represents a perspective-based approach composed of two modules that analyze text-hypothesis pairs from a strictly lexical and syntactic perspectives, respectively. We attempt to prove that the textual entailment recognition task can be overcome by performing individual analysis that acknowledges us of the maximum amount of information that each single perspective can provide. We compare this approach with the system we presented in the previous edition of PASCAL Recognising Textual Entailment Challenge, obtaining an accuracy rate 17.98% higher.

Corpus-based semantic role approach in information retrieval

In this paper, a method to determine the semantic role for the constituents of a sentence is presented. This method, named SemRol, is a corpus-based approach that uses two different statistical models, conditional Maximum Entropy (ME) Probability Models and the TiMBL program, a Memory-based Learning. It consists of three phases that make use of features using words, lemmas, PoS tags and shallow parsing information. Our method introduces a new phase in the Semantic Role Labeling task which has usually been approached as a two phase procedure consisting of recognition and labeling arguments. From our point of view, firstly the sense of the verbs in the sentences must be disambiguated. That is why depending on the sense of the verb a different set of roles must be considered. Regarding the labeling arguments phase, a tuning procedure is presented. As a result of this procedure one of the best sets of features for the labeling arguments task is detected. With this set, that is different for TiMBL and ME, precisions of 76.71% for TiMBL or 70.55% for ME, are obtained. Furthermore, the semantic role information provided by our SemRol method could be used as an extension of Information Retrieval or Question Answering systems. We propose using this semantic information as an extension of an Information Retrieval system in order to reduce the number of documents or passages retrieved by the system.

A maximum entropy-based word sense disambiguation system

In this paper, a supervised learning system of word sense disambiguation is presented. It is based on conditional maximum entropy models. This system acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. Several types of features have been analyzed using the SENSEVAL-2 data for the Spanish lexical sample task. Such analysis shows that instead of training with the same kind of information for all words, each one is more effectively learned using a different set of features. This best-feature-selection is used to build some systems based on different maximum entropy classifiers, and a voting system helped by a knowledge-based method.

Applying Object-Oriented Conceptual Modeling Techniques to the Design of Multidimensional Databases and OLAP Applications

Few works have been presented in the area of Object-Oriented (OO) Conceptual Modeling to specify the design of multidimensional databases (MDB) and OLAP applications. In this context, this paper describes an OO conceptual modeling approach (based on a subset of UML) to address the peculiarities associated to this kind of systems. The structure of the system is specified by means of a class diagram that considers the semantics of multidimensional (MD) data models with a minimal use of constraints and extensions on UML. Furthermore, users requirements are specified as object collections (cube classes) in the class diagram. To do so, new specific graphical elements are defined to represent these classes. The result is an OO conceptual modeling approach that considers the semantics of MD data models as well as users requirements in the same model in a natural way.

Anaphor Resolution In Unrestricted Texts With Partial Parsing

In this paper we deal with several kinds of anaphora in unrestricted texts. These kinds of anaphora are pronominal references, surfacecount anaphora and one-anaphora. In order to solve these anaphors we work on the output of a part-of-speech tagger, on which we automatically apply a partial parsing from the formalism: Slot Unification Grammar, which has been implemented in Prolog. We only use the following kinds of information: lexical (the lemma of each word), morphologic (person, number, gender) and syntactic. Finally we show the experimental results, and the restrictions and preferences that we have used for anaphor resolution with partial parsing.