Cognitive modules of an NLP knowledge base for language understanding

Algunas aplicaciones del procesamiento del lenguaje natural, p.ej. la traducción automática, requieren una base de conocimiento provista de representaciones conceptuales que puedan reflejar la estructura del sistema cognitivo del ser humano. En cambio, tareas como la indización automática o la extracción de información pueden ser realizadas con una semántica superficial. De todos modos, la construcción de una base de conocimiento robusta garantiza su reutilización en la mayoría de las tareas del procesamiento del lenguaje natural. El propósito de este artículo es describir los principales módulos cognitivos de FunGramKB, una base de conocimiento léxico-conceptual multipropósito para su implementación en sistemas del procesamiento del lenguaje natural.Some natural language processing systems, e.g. machine translation, require a knowledge base with conceptual representations reflecting the structure of human beings’ cognitive system. In some other systems, e.g. automatic indexing or information extraction, surface semantics could be sufficient, but the construction of a robust knowledge base guarantees its use in most natural language processing tasks, consolidating thus the concept of resource reuse. The objective of this paper is to describe FunGramKB, a multipurpose lexicoconceptual knowledge base for natural language processing systems. Particular attention will be paid to the two main cognitive modules, i.e. the ontology and the cognicon

Cognitive modules of an NLP knowledge base for language understanding

[EN] Some natural language processing systems, e.g. machine translation, require a knowledge base with conceptual representations reflecting the structure of human beings’ cognitive system. In some other systems, e.g. automatic indexing or information extraction, surface semantics could be sufficient, but the construction of a robust knowledge base guarantees its use in most natural language processing tasks, consolidating thus the concept of resource reuse. The objective of this paper is to describe FunGramKB, a multipurpose lexicoconceptual knowledge base for natural language processing systems. Particular attention will be paid to the two main cognitive modules, i.e. the ontology and the cognicon.[ES] Algunas aplicaciones del procesamiento del lenguaje natural, p.ej. la traducción automática, requieren una base de conocimiento provista de representaciones conceptuales que puedan reflejar la estructura del sistema cognitivo del ser humano. En cambio, tareas como la indización automática o la extracción de información pueden ser realizadas con una semántica superficial. De todos modos, la construcción de una base de conocimiento robusta garantiza su reutilización en la mayoría de las tareas del procesamiento del lenguaje natural. El propósito de este artículo es describir los principales módulos cognitivos de FunGramKB, una base de conocimiento léxico-conceptual multipropósito para su implementación en sistemas del procesamiento del lenguaje natural.Periñán Pascual, JC.; Arcas Túnez, F. (2007). Cognitive modules of an NLP knowledge base for language understanding. Procesamiento del Lenguaje Natural. (39):197-204. http://hdl.handle.net/10251/52122S1972043

Mindful documentary

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (leaves 86-92).In the practice of documentary creation, a videographer performs an elaborate balancing act between observing the world, deciding what to record, and understanding the implications of the recorded material, all with respect to her primary goal of story construction. This thesis presents mindful documentary, a model of a videographer's cyclical process of thinking and constructing during a documentary production. The purpose of this model is to better support documentary creation through systems that assist the documentary videographer in discovering new methods of observation, ways of thinking, and novel stories while recording the world. Based on the mindful documentary model, a reflective partnership is established between the videographer and a camera with commonsense reasoning abilities during capture and organization of documentary video collections. Knowledge is solicited from the videographer at the point of capture; it is used to generate narrative or contextual shot suggestions, which provide alternative recording path ideas for the videographer. Thus, the system encourages the videographer to reflect on the story possibilities of a documentary collection during real-time capture. Qualitative results of studies with a group of videographers - including novices and experts - showed a willingness to take suggestions during documentary production and, in some cases, to alter the recording path after reflection on shot possibilities presented by the system. Moreover, suggestions often had increased influence on the recording path if they were not taken as directives but as catalysts, i.e., prompts to expand thinking about the documentary subject rather than explicit shot instructions.(cont.) Critical lessons were learned about methodology and system design for documentary production. As a documentary is built, evidence of what the videographer has learned is represented in the documentary. The model, methodology, and system presented in this thesis provide a basis for understanding how videographers think during documentary construction and how machines with commonsense reasoning resources can serve as creative storytelling partners.by Barbara A. Barry.Ph.D

Event structures in knowledge, pictures and text

This thesis proposes new techniques for mining scripts. Scripts are essential pieces of common sense knowledge that contain information about everyday scenarios (like going to a restaurant), namely the events that usually happen in a scenario (entering, sitting down, reading the menu...), their typical order (ordering happens before eating), and the participants of these events (customer, waiter, food...). Because many conventionalized scenarios are shared common sense knowledge and thus are usually not described in standard texts, we propose to elicit sequential descriptions of typical scenario instances via crowdsourcing over the internet. This approach overcomes the implicitness problem and, at the same time, is scalable to large data collections. To generalize over the input data, we need to mine event and participant paraphrases from the textual sequences. For this task we make use of the structural commonalities in the collected sequential descriptions, which yields much more accurate paraphrases than approaches that do not take structural constraints into account. We further apply the algorithm we developed for event paraphrasing to parallel standard texts for extracting sentential paraphrases and paraphrase fragments. In this case we consider the discourse structure in a text as a sequential event structure. As for event paraphrasing, the structure-aware paraphrasing approach clearly outperforms systems that do not consider discourse structure. As a multimodal application, we develop a new resource in which textual event descriptions are grounded in videos, which enables new investigations on action description semantics and a more accurate modeling of event description similarities. This grounding approach also opens up new possibilities for applying the computed script knowledge for automated event recognition in videos.Die vorliegende Dissertation schlägt neue Techniken zur Berechnung von Skripten vor. Skripte sind essentielle Teile des Allgemeinwissens, die Informationen über alltägliche Szenarien (wie im Restaurant essen) enthalten, nämlich die Ereignisse, die typischerweise in einem Szenario vorkommen (eintreten, sich setzen, die Karte lesen...), deren typische zeitliche Abfolge (man bestellt bevor man isst), und die Teilnehmer der Ereignisse (ein Gast, der Kellner, das Essen,...). Da viele konventionalisierte Szenarien implizit geteiltes Allgemeinwissen sind und üblicherweise nicht detailliert in Texten beschrieben werden, schlagen wir vor, Beschreibungen von typischen Szenario-Instanzen durch sog. “Crowdsourcing” über das Internet zu sammeln. Dieser Ansatz löst das Implizitheits-Problem und lässt sich gleichzeitig zu großen Daten-Sammlungen hochskalieren. Um über die Eingabe-Daten zu generalisieren, müssen wir in den Text-Sequenzen Paraphrasen für Ereignisse und Teilnehmer finden. Hierfür nutzen wir die strukturellen Gemeinsamkeiten dieser Sequenzen, was viel präzisere Paraphrasen-Information ergibt als Standard-Ansätze, die strukturelle Einschränkungen nicht beachten. Die Techniken, die wir für die Ereignis-Paraphrasierung entwickelt haben, wenden wir auch auf parallele Standard-Texte an, um Paraphrasen auf Satz-Ebene sowie Paraphrasen-Fragmente zu extrahieren. Hier betrachten wir die Diskurs-Struktur eines Textes als sequentielle Ereignis-Struktur. Auch hier liefert der strukturell informierte Ansatz klar bessere Ergebnisse als herkömmliche Systeme, die Diskurs-Struktur nicht in die Berechnung mit einbeziehen. Als multimodale Anwendung entwickeln wir eine neue Ressource, in der Text-Beschreibungen von Ereignissen mittels zeitlicher Synchronisierung in Videos verankert sind. Dies ermöglicht neue Ansätze für die Erforschung der Semantik von Ereignisbeschreibungen, und erlaubt außerdem die Modellierung treffenderer Ereignis-Ähnlichkeiten. Dieser Schritt der visuellen Verankerung von Text in Videos eröffnet auch neue Möglichkeiten für die Anwendung des berechneten Skript-Wissen bei der automatischen Ereigniserkennung in Videos

Script acquisition : a crowdsourcing and text mining approach

According to Grice’s (1975) theory of pragmatics, people tend to omit basic information when participating in a conversation (or writing a narrative) under the assumption that left out details are already known or can be inferred from commonsense knowledge by the hearer (or reader). Writing and understanding of texts makes particular use of a specific kind of common-sense knowledge, referred to as script knowledge. Schank and Abelson (1977) proposed Scripts as a model of human knowledge represented in memory that stores the frequent habitual activities, called scenarios, (e.g. eating in a fast food restaurant, etc.), and the different courses of action in those routines. This thesis addresses measures to provide a sound empirical basis for high-quality script models. We work on three key areas related to script modeling: script knowledge acquisition, script induction and script identification in text. We extend the existing repository of script knowledge bases in two different ways. First, we crowdsource a corpus of 40 scenarios with 100 event sequence descriptions (ESDs) each, thus going beyond the size of previous script collections. Second, the corpus is enriched with partial alignments of ESDs, done by human annotators. The crowdsourced partial alignments are used as prior knowledge to guide the semi-supervised script-induction algorithm proposed in this dissertation. We further present a semi-supervised clustering approach to induce script structure from crowdsourced descriptions of event sequences by grouping event descriptions into paraphrase sets and inducing their temporal order. The proposed semi-supervised clustering model better handles order variation in scripts and extends script representation formalism, Temporal Script graphs, by incorporating "arbitrary order" equivalence classes in order to allow for the flexible event order inherent in scripts. In the third part of this dissertation, we introduce the task of scenario detection, in which we identify references to scripts in narrative texts. We curate a benchmark dataset of annotated narrative texts, with segments labeled according to the scripts they instantiate. The dataset is the first of its kind. The analysis of the annotation shows that one can identify scenario references in text with reasonable reliability. Subsequently, we proposes a benchmark model that automatically segments and identifies text fragments referring to given scenarios. The proposed model achieved promising results, and therefore opens up research on script parsing and wide coverage script acquisition.Gemäß der Grice’schen (1975) Pragmatiktheorie neigen Menschen dazu, grundlegende Informationen auszulassen, wenn sie an einem Gespräch teilnehmen (oder eine Geschichte schreiben). Dies geschieht unter der Annahme, dass die ausgelassenen Details bereits bekannt sind, oder vom Hörer (oder Leser) aus Weltwissen erschlossen werden können. Besonders beim Schreiben und Verstehen von Text wird Verwendung einer spezifischen Art von solchem Weltwissen gemacht, welches auch Skriptwissen genannt wird. Schank und Abelson (1977) erdachten Skripte als ein Modell menschlichen Wissens, welches im menschlichen Gedächtnis gespeichert ist und häufige Alltags-Aktivitäten sowie deren typischen Ablauf beinhaltet. Solche Skript-Aktivitäten werden auch als Szenarios bezeichnet und umfassen zum Beispiel Im Restaurant Essen etc. Diese Dissertation widmet sich der Bereitstellung einer soliden empirischen Grundlage zur Akquisition qualitativ hochwertigen Skriptwissens. Wir betrachten drei zentrale Aspekte im Bereich der Skriptmodellierung: Akquisition ition von Skriptwissen, Skript-Induktion und Skriptidentifizierung in Text. Wir erweitern das bereits bestehende Repertoire und Skript-Datensätzen in 2 Bereichen. Erstens benutzen wir Crowdsourcing zur Erstellung eines Korpus, das 40 Szenarien mit jeweils 100 Ereignissequenzbeschreibungen (Event Sequence Descriptions, ESDs) beinhaltet, und welches somit größer als bestehende Skript- Datensätze ist. Zweitens erweitern wir das Korpus mit partiellen ESD-Alignierungen, die von Hand annotiert werden. Die partiellen Alignierungen werden dann als Vorwissen für einen halbüberwachten Algorithmus zur Skriptinduktion benutzt, der im Rahmen dieser Dissertation vorgestellt wird. Wir präsentieren außerdem einen halbüberwachten Clusteringansatz zur Induktion von Skripten, basierend auf Ereignissequenzen, die via Crowdsourcing gesammelt wurden. Hierbei werden einzelne Ereignisbeschreibungen gruppiert, um Paraphrasenmengen und der deren temporale Ordnung abzuleiten. Der vorgestellte Clusteringalgorithmus ist im Stande, Variationen in der typischen Reihenfolge in Skripte besser abzubilden und erweitert damit einen Formalismus zur Skriptrepräsentation, temporale Skriptgraphen. Dies wird dadurch bewerkstelligt, dass Equivalenzklassen von Beschreibungen mit "arbiträrer Reihenfolge" genutzt werden, die es erlauben, eine flexible Ereignisordnung abzubilden, die inhärent bei Skripten vorhanden ist. Im dritten Teil der vorliegenden Arbeit führen wir den Task der SzenarioIdentifikation ein, also der automatischen Identifikation von Skriptreferenzen in narrativen Texten. Wir erstellen einen Benchmark-Datensatz mit annotierten narrativen Texten, in denen einzelne Segmente im Bezug auf das Skript, welches sie instantiieren, markiert wurden. Dieser Datensatz ist der erste seiner Art. Eine Analyse der Annotation zeigt, dass Referenzen zu Szenarien im Text mit annehmbarer Akkuratheit vorhergesagt werden können. Zusätzlich stellen wir ein Benchmark-Modell vor, welches Textfragmente automatisch erstellt und deren Szenario identifiziert. Das vorgestellte Modell erreicht erfolgversprechende Resultate und öffnet damit einen Forschungszweig im Bereich des Skript-Parsens und der Skript-Akquisition im großen Stil

The Value of Everything: Ranking and Association with Encyclopedic Knowledge

This dissertation describes WikiRank, an unsupervised method of assigning relative values to elements of a broad coverage encyclopedic information source in order to identify those entries that may be relevant to a given piece of text. The valuation given to an entry is based not on textual similarity but instead on the links that associate entries, and an estimation of the expected frequency of visitation that would be given to each entry based on those associations in context. This estimation of relative frequency of visitation is embodied in modifications to the random walk interpretation of the PageRank algorithm. WikiRank is an effective algorithm to support natural language processing applications. It is shown to exceed the performance of previous machine learning algorithms for the task of automatic topic identification, providing results comparable to that of human annotators. Second, WikiRank is found useful for the task of recognizing text-based paraphrases on a semantic level, by comparing the distribution of attention generated by two pieces of text using the encyclopedic resource as a common reference. Finally, WikiRank is shown to have the ability to use its base of encyclopedic knowledge to recognize terms from different ontologies as describing the same thing, and thus allowing for the automatic generation of mapping links between ontologies. The conclusion of this thesis is that the "knowledge access heuristic" is valuable and that a ranking process based on a large encyclopedic resource can form the basis for an extendable general purpose mechanism capable of identifying relevant concepts by association, which in turn can be effectively utilized for enumeration and comparison at a semantic level

Using Analogy to Acquire Commonsense Knowledge from Human Contributors

The goal of the work reported here is to capture the commonsense knowledge of non-expert human contributors. Achieving this goal will enable more intelligent human-computer interfaces and pave the way for computers to reason about our world. In the domain of natural language processing, it will provide the world knowledge much needed for semantic processing of natural language. To acquire knowledge from contributors not trained in knowledge engineering, I take the following four steps: (i) develop a knowledge representation (KR) model for simple assertions in natural language, (ii) introduce cumulative analogy, a class of nearest-neighbor based analogical reasoning algorithms over this representation, (iii) argue that cumulative analogy is well suited for knowledge acquisition (KA) based on a theoretical analysis of effectiveness of KA with this approach, and (iv) test the KR model and the effectiveness of the cumulative analogy algorithms empirically. To investigate effectiveness of cumulative analogy for KA empirically, Learner, an open source system for KA by cumulative analogy has been implemented, deployed, and evaluated. (The site "1001 Questions," is available at http://teach-computers.org/learner.html). Learner acquires assertion-level knowledge by constructing shallow semantic analogies between a KA topic and its nearest neighbors and posing these analogies as natural language questions to human contributors. Suppose, for example, that based on the knowledge about "newspapers" already present in the knowledge base, Learner judges "newspaper" to be similar to "book" and "magazine." Further suppose that assertions "books contain information" and "magazines contain information" are also already in the knowledge base. Then Learner will use cumulative analogy from the similar topics to ask humans whether "newspapers contain information." Because similarity between topics is computed based on what is already known about them, Learner exhibits bootstrapping behavior --- the quality of its questions improves as it gathers more knowledge. By summing evidence for and against posing any given question, Learner also exhibits noise tolerance, limiting the effect of incorrect similarities. The KA power of shallow semantic analogy from nearest neighbors is one of the main findings of this thesis. I perform an analysis of commonsense knowledge collected by another research effort that did not rely on analogical reasoning and demonstrate that indeed there is sufficient amount of correlation in the knowledge base to motivate using cumulative analogy from nearest neighbors as a KA method. Empirically, evaluating the percentages of questions answered affirmatively, negatively and judged to be nonsensical in the cumulative analogy case compares favorably with the baseline, no-similarity case that relies on random objects rather than nearest neighbors. Of the questions generated by cumulative analogy, contributors answered 45% affirmatively, 28% negatively and marked 13% as nonsensical; in the control, no-similarity case 8% of questions were answered affirmatively, 60% negatively and 26% were marked as nonsensical

From generation to generation : family stories, computers and genealogy

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Leaf 132 blank.Includes bibliographical references (leaves 115-131).Telling stories about a family's common past solidifies its sense of community, and enriches member's sense of identity and belonging. In preindustrial times this information flowed orally thanks to continuous and prolonged cohabitation, but the dispersion of kinship in modern society has severed the ties between the generations. On-line communities can help restore these links by providing virtual spaces whose design specifically encourages storytelling. In order to arrive at this design, this thesis (1) surveys the importance and characteristics of family storytelling, (2) discusses the procedures used by oral historians and folklorists for story elicitation, and (3) analyzes a number of existing systems in terms of the above theoretical background. This thesis concludes with a series of guidelines for the design and implementation of communities for family storytelling. Different ways of indexing and accessing stories are discussed, and appropriate representations and interfaces that facilitate the storytelling process are presented.by Martin Hadis.S.M

Computing point-of-view : modeling and simulating judgments of taste

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 153-163).People have rich points-of-view that afford them the ability to judge the aesthetics of people, things, and everyday happenstance; yet viewpoint has an ineffable quality that is hard to articulate in words, let alone capture in computer models. Inspired by cultural theories of taste and identity, this thesis explores end-to-end computational modeling of people's tastes-from model acquisition, to generalization, to application- under various realms. Five aesthetical realms are considered-cultural taste, attitudes, ways of perceiving, taste for food, and sense-of-humor. A person's model is acquired by reading her personal texts, such as a weblog diary, a social network profile, or emails. To generalize a person model, methods such as spreading activation, analogy, and imprimer supplementation are applied to semantic resources and search spaces mined from cultural corpora. Once a generalized model is achieved, a person's tastes are brought to life through perspective-based applications, which afford the exploration of someone else's perspective through interactivity and play. The thesis describes model acquisition systems implemented for each of the five aesthetical realms.(cont.) The techniques of 'reading for affective themes' (RATE), and 'culture mining' are described, along with their enabling technologies, which are commonsense reasoning and textual affect analysis. Finally, six perspective-based applications were implemented to illuminate a range of real-world beneficiaries to person modeling-virtual mentoring, self-reflection, and deep customization.by Xinyu Hugo Liu.Ph.D