Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences

Selectional preferences have been used by word sense disambiguation (WSD) systems as one source of disambiguating information. We evaluate WSD using selectional preferences acquired for English adjective—noun, subject, and direct object grammatical relationships with respect to a standard test corpus. The selectional preferences are specific to verb or adjective classes, rather than individual word forms, so they can be used to disambiguate the co-occurring adjectives and verbs, rather than just the nominal argument heads. We also investigate use of the one-senseper-discourse heuristic to propagate a sense tag for a word to other occurrences of the same word within the current document in order to increase coverage. Although the preferences perform well in comparison with other unsupervised WSD systems on the same corpus, the results show that for many applications, further knowledge sources would be required to achieve an adequate level of accuracy and coverage. In addition to quantifying performance, we analyze the results to investigate the situations in which the selectional preferences achieve the best precision and in which the one-sense-per-discourse heuristic increases performance

The Acquisition Of Lexical Knowledge From The Web For Aspects Of Semantic Interpretation

This work investigates the effective acquisition of lexical knowledge from the Web to perform semantic interpretation. The Web provides an unprecedented amount of natural language from which to gain knowledge useful for semantic interpretation. The knowledge acquired is described as common sense knowledge, information one uses in his or her daily life to understand language and perception. Novel approaches are presented for both the acquisition of this knowledge and use of the knowledge in semantic interpretation algorithms. The goal is to increase accuracy over other automatic semantic interpretation systems, and in turn enable stronger real world applications such as machine translation, advanced Web search, sentiment analysis, and question answering. The major contributions of this dissertation consist of two methods of acquiring lexical knowledge from the Web, namely a database of common sense knowledge and Web selectors. The first method is a framework for acquiring a database of concept relationships. To acquire this knowledge, relationships between nouns are found on the Web and analyzed over WordNet using information-theory, producing information about concepts rather than ambiguous words. For the second contribution, words called Web selectors are retrieved which take the place of an instance of a target word in its local context. The selectors serve for the system to learn the types of concepts that the sense of a target word should be similar. Web selectors are acquired dynamically as part of a semantic interpretation algorithm, while the relationships in the database are useful to iii stand-alone programs. A final contribution of this dissertation concerns a novel semantic similarity measure and an evaluation of similarity and relatedness measures on tasks of concept similarity. Such tasks are useful when applying acquired knowledge to semantic interpretation. Applications to word sense disambiguation, an aspect of semantic interpretation, are used to evaluate the contributions. Disambiguation systems which utilize semantically annotated training data are considered supervised. The algorithms of this dissertation are considered minimallysupervised; they do not require training data created by humans, though they may use humancreated data sources. In the case of evaluating a database of common sense knowledge, integrating the knowledge into an existing minimally-supervised disambiguation system significantly improved results – a 20.5% error reduction. Similarly, the Web selectors disambiguation system, which acquires knowledge directly as part of the algorithm, achieved results comparable with top minimally-supervised systems, an F-score of 80.2% on a standard noun disambiguation task. This work enables the study of many subsequent related tasks for improving semantic interpretation and its application to real-world technologies. Other aspects of semantic interpretation, such as semantic role labeling could utilize the same methods presented here for word sense disambiguation. As the Web continues to grow, the capabilities of the systems in this dissertation are expected to increase. Although the Web selectors system achieves great results, a study in this dissertation shows likely improvements from acquiring more data. Furthermore, the methods for acquiring a database of common sense knowledge could be applied in a more exhaustive fashion for other types of common sense knowledge. Finally, perhaps the greatest benefits from this work will come from the enabling of real world technologies that utilize semantic interpretation

Finding predominant word senses in untagged text

In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The problem with using the predominant, or first sense heuristic, aside from the fact that it does not take surrounding context into account, is that it assumes some quantity of handtagged data. Whilst there are a few hand-tagged corpora available for some languages, one would expect the frequency distribution of the senses of words, particularly topical words, to depend on the genre and domain of the text under consideration. We present work on the use of a thesaurus acquired from raw textual corpora and the WordNet similarity package to find predominant noun senses automatically. The acquired predominant senses give a precision of 64% on the nouns of the SENSEVAL- 2 English all-words task. This is a very promising result given that our method does not require any hand-tagged text, such as SemCor. Furthermore, we demonstrate that our method discovers appropriate predominant senses for words from two domainspecific corpora

D6.1: Technologies and Tools for Lexical Acquisition

This report describes the technologies and tools to be used for Lexical Acquisition in PANACEA. It includes descriptions of existing technologies and tools which can be built on and improved within PANACEA, as well as of new technologies and tools to be developed and integrated in PANACEA platform. The report also specifies the Lexical Resources to be produced. Four main areas of lexical acquisition are included: Subcategorization frames (SCFs), Selectional Preferences (SPs), Lexical-semantic Classes (LCs), for both nouns and verbs, and Multi-Word Expressions (MWEs)

Automatically Acquiring A Semantic Network Of Related Concepts

We describe the automatic acquisition of a semantic network in which over 7,500 of the most frequently occurring nouns in the English language are linked to their semantically related concepts in the WordNet noun ontology. Relatedness between nouns is discovered automatically from lexical co-occurrence in Wikipedia texts using a novel adaptation of an information theoretic inspired measure. Our algorithm then capitalizes on salient sense clustering among these semantic associates to automatically disambiguate them to their corresponding WordNet noun senses (i.e., concepts). The resultant concept-to-concept associations, stemming from 7,593 target nouns, with 17,104 distinct senses among them, constitute a large-scale semantic network with 208,832 undirected edges between related concepts. Our work can thus be conceived of as augmenting the WordNet noun ontology with RelatedTo links. The network, which we refer to as the Szumlanski-Gomez Network (SGN), has been subjected to a variety of evaluative measures, including manual inspection by human judges and quantitative comparison to gold standard data for semantic relatedness measurements. We have also evaluated the network’s performance in an applied setting on a word sense disambiguation (WSD) task in which the network served as a knowledge source for established graph-based spreading activation algorithms, and have shown: a) the network is competitive with WordNet when used as a stand-alone knowledge source for WSD, b) combining our network with WordNet achieves disambiguation results that exceed the performance of either resource individually, and c) our network outperforms a similar resource, WordNet++ (Ponzetto & Navigli, 2010), that has been automatically derived from annotations in the Wikipedia corpus. iii Finally, we present a study on human perceptions of relatedness. In our study, we elicited quantitative evaluations of semantic relatedness from human subjects using a variation of the classical methodology that Rubenstein and Goodenough (1965) employed to investigate human perceptions of semantic similarity. Judgments from individual subjects in our study exhibit high average correlation to the elicited relatedness means using leave-one-out sampling (r = 0.77, σ = 0.09, N = 73), although not as high as average human correlation in previous studies of similarity judgments, for which Resnik (1995) established an upper bound of r = 0.90 (σ = 0.07, N = 10). These results suggest that human perceptions of relatedness are less strictly constrained than evaluations of similarity, and establish a clearer expectation for what constitutes human-like performance by a computational measure of semantic relatedness. We also contrast the performance of a variety of similarity and relatedness measures on our dataset to their performance on similarity norms and introduce our own dataset as a supplementary evaluative standard for relatedness measures

The integration of syntax and semantic plausibility in a wide-coverage model of human sentence processing

Models of human sentence processing have paid much attention to three key characteristics of the sentence processor: Its robust and accurate processing of unseen input (wide coverage), its immediate, incremental interpretation of partial input and its sensitivity to structural frequencies in previous language experience. In this thesis, we propose a model of human sentence processing that accounts for these three characteristics and also models a fourth key characteristic, namely the influence of semantic plausibility on sentence processing. The precondition for such a sentence processing model is a general model of human plausibility intuitions. We therefore begin by presenting a probabilistic model of the plausibility of verb-argument relations, which we estimate as the probability of encountering a verb-argument pair in the relation specified by a thematic role in a role-annotated training corpus. This model faces a significant sparse data problem, which we alleviate by combining two orthogonal smoothing methods. We show that the smoothed model\u27;s predictions are significantly correlated to human plausibility judgements for a range of test sets. We also demonstrate that our semantic plausibility model outperforms selectional preference models and a standard role labeller, which solve tasks from computational linguistics that are related to the prediction of human judgements. We then integrate this semantic plausibility model with an incremental, wide-coverage, probabilistic model of syntactic processing to form the Syntax/Semantics (SynSem) Integration model of sentence processing. The SynSem-Integration model combines preferences for candidate syntactic structures from two sources: Syntactic probability estimates from a probabilistic parser and our semantic plausibility model\u27;s estimates of the verb-argument relations in each syntactic analysis. The model uses these preferences to determine a globally preferred structure and predicts difficulty in human sentence processing either if syntactic and semantic preferences conflict, or if the interpretation of the preferred analysis changes non-monotonically. In a thorough evaluation against the patterns of processing difficulty found for four ambiguity phenomena in eight reading-time studies, we demonstrate that the SynSem-Integration model reliably predicts human reading time behaviour.Diese Dissertation behandelt die Modellierung des menschlichen Sprachverstehens auf der Ebene einzelner Sätze. Während sich bereits existierende Modelle hauptsächlich mit syntaktischen Prozessen befassen, liegt unser Schwerpunkt darauf, ein Modell für die semantische Plausibilität von Äußerungen in ein Satzverarbeitungsmodell zu integrieren. Vier wichtige Eigenschaften des Sprachverstehens bestimmen die Konstruktion unseres Modells: Inkrementelle Verarbeitung, eine erfahrungsbasierte Architektur, breite Abdeckung von Äußerungen, und die Integration von semantischer Plausibilität. Während die ersten drei Eigenschaften von vielen Modellen aufgegriffen werden, gab es bis jetzt kein Modell, das außerdem auch Plausibilität einbezieht. Wir stellen zunächst ein generelles Plausibilitätsmodell vor, um es dann mit einem inkrementellen, probabilistischen Satzverarbeitungsmodell mit breiter Abdeckung zu einem Modell mit allen vier angestrebten Eigenschaften zu integrieren. Unser Plausibilitätsmodell sagt menschliche Plausibilitätsbewertungen für Verb-Argumentpaare in verschiedenen Relationen (z.B. Agens oder Patiens) voraus. Das Modell estimiert die Plausibilität eines Verb-Argumentpaars in einer spezifischen, durch eine thematische Rolle angegebenen Relation als die Wahrscheinlichkeit, das Tripel aus Verb, Argument und Rolle in einem rollensemantisch annotierten Trainingskorpus anzutreffen. Die Vorhersagen des Plausbilitätsmodells korrelieren für eine Reihe verschiedener Testdatensätze signifikant mit menschlichen Plausibilitätsbewertungen. Ein Vergleich mit zwei computerlinguist- ischen Ansätzen, die jeweils eine verwandte Aufgabe erfüllen, nämlich die Zuweisung von thematischen Rollen und die Berechnung von Selektionspräferenzen, zeigt, daß unser Modell Plausibilitätsurteile verläßlicher vorhersagt. Unser Satzverstehensmodell, das Syntax/Semantik-Integrationsmodell, ist eine Kombination aus diesem Plausibilitätsmodell und einem inkrementellen, probabilistischen Satzverarbeitungsmodell auf der Basis eines syntaktischen Parsers mit breiter Abdeckung. Das Syntax/Semantik-Integrationsmodell interpoliert syntaktische Wahrscheinlichkeitsabschätzungen für Analysen einer Äußerung mit den semantischen Plausibilitätsabschätzungen für die Verb-Argumentpaare in jeder Analyse. Das Ergebnis ist eine global präferierte Analyse. Das Syntax/Semantik-Integrationsmodell sagt Verarbeitungsschwierigkeiten voraus, wenn entweder die syntaktisch und semantisch präferierte Analyse konfligieren oder wenn sich die semantische Interpretation der global präferierten Analyse in einem Verarbeitungsschritt nicht-monoton ändert. Die abschließende Evaluation anhand von Befunden über menschliche Verarbeitungsschwierigkeiten, wie sie experimentell in acht Studien für vier Ambiguitätsphänomene festgestellt wurden, zeigt, daß das Syntax/Semantik-Integrationsmodell die experimentellen Daten korrekt voraussagt

D7.1. Criteria for evaluation of resources, technology and integration.

This deliverable defines how evaluation is carried out at each integration cycle in the PANACEA project. As PANACEA aims at producing large scale resources, evaluation becomes a critical and challenging issue. Critical because it is important to assess the quality of the results that should be delivered to users. Challenging because we prospect rather new areas, and through a technical platform: some new methodologies will have to be explored or old ones to be adapted

A distributional investigation of German verbs

Diese Dissertation bietet eine empirische Untersuchung deutscher Verben auf der Grundlage statistischer Beschreibungen, die aus einem großen deutschen Textkorpus gewonnen wurden. In einem kurzen Überblick über linguistische Theorien zur lexikalischen Semantik von Verben skizziere ich die Idee, dass die Verbbedeutung wesentlich von seiner Argumentstruktur (der Anzahl und Art der Argumente, die zusammen mit dem Verb auftreten) und seiner Aspektstruktur (Eigenschaften, die den zeitlichen Ablauf des vom Verb denotierten Ereignisses bestimmen) abhängt. Anschließend erstelle ich statistische Beschreibungen von Verben, die auf diesen beiden unterschiedlichen Bedeutungsfacetten basieren. Insbesondere untersuche ich verbale Subkategorisierung, Selektionspräferenzen und Aspekt. Alle diese Modellierungsstrategien werden anhand einer gemeinsamen Aufgabe, der Verbklassifikation, bewertet. Ich zeige, dass im Rahmen von maschinellem Lernen erworbene Merkmale, die verbale lexikalische Aspekte erfassen, für eine Anwendung von Vorteil sind, die Argumentstrukturen betrifft, nämlich semantische Rollenkennzeichnung. Darüber hinaus zeige ich, dass Merkmale, die die verbale Argumentstruktur erfassen, bei der Aufgabe, ein Verb nach seiner Aspektklasse zu klassifizieren, gut funktionieren. Diese Ergebnisse bestätigen, dass diese beiden Facetten der Verbbedeutung auf grundsätzliche Weise zusammenhängen.This dissertation provides an empirical investigation of German verbs conducted on the basis of statistical descriptions acquired from a large corpus of German text. In a brief overview of the linguistic theory pertaining to the lexical semantics of verbs, I outline the idea that verb meaning is composed of argument structure (the number and types of arguments that co-occur with a verb) and aspectual structure (properties describing the temporal progression of an event referenced by the verb). I then produce statistical descriptions of verbs according to these two distinct facets of meaning: In particular, I examine verbal subcategorisation, selectional preferences, and aspectual type. All three of these modelling strategies are evaluated on a common task, automatic verb classification. I demonstrate that automatically acquired features capturing verbal lexical aspect are beneficial for an application that concerns argument structure, namely semantic role labelling. Furthermore, I demonstrate that features capturing verbal argument structure perform well on the task of classifying a verb for its aspectual type. These findings suggest that these two facets of verb meaning are related in an underlying way