A Short Note on Social-Semiotic Networks from the Point of View of Quantitative Semantics

In this extended abstract we discuss four related characteristics of semantic spaces as the standard model of meaning representation in quantitative semantics. We argue that these characteristics are challenged from the point of view of social web communities and the possibilities which they offer in terms of exploring semantic emph{and} pragmatic data. More specifically, we plead for a reconstruction of the weak contextual hypothesis in order to account for non-linguistic, pragmatic aspects of context. Finally, we mention two consequences of such a pragmatic turn, that is, in the area of named entity recognition and of language evolution

Unsupervised Natural Language Processing for Knowledge Extraction from Domain-specific Textual Resources

This thesis aims to develop a Relation Extraction algorithm to extract knowledge out of automotive data. While most approaches to Relation Extraction are only evaluated on newspaper data dealing with general relations from the business world their applicability to other data sets is not well studied. Part I of this thesis deals with theoretical foundations of Information Extraction algorithms. Text mining cannot be seen as the simple application of data mining methods to textual data. Instead, sophisticated methods have to be employed to accurately extract knowledge from text which then can be mined using statistical methods from the field of data mining. Information Extraction itself can be divided into two subtasks: Entity Detection and Relation Extraction. The detection of entities is very domain-dependent due to terminology, abbreviations and general language use within the given domain. Thus, this task has to be solved for each domain employing thesauri or another type of lexicon. Supervised approaches to Named Entity Recognition will not achieve reasonable results unless they have been trained for the given type of data. The task of Relation Extraction can be basically approached by pattern-based and kernel-based algorithms. The latter achieve state-of-the-art results on newspaper data and point out the importance of linguistic features. In order to analyze relations contained in textual data, syntactic features like part-of-speech tags and syntactic parses are essential. Chapter 4 presents machine learning approaches and linguistic foundations being essential for syntactic annotation of textual data and Relation Extraction. Chapter 6 analyzes the performance of state-of-the-art algorithms of POS tagging, syntactic parsing and Relation Extraction on automotive data. The findings are: supervised methods trained on newspaper corpora do not achieve accurate results when being applied on automotive data. This is grounded in various reasons. Besides low-quality text, the nature of automotive relations states the main challenge. Automotive relation types of interest (e. g. component – symptom) are rather arbitrary compared to well-studied relation types like is-a or is-head-of. In order to achieve acceptable results, algorithms have to be trained directly on this kind of data. As the manual annotation of data for each language and data type is too costly and inflexible, unsupervised methods are the ones to rely on. Part II deals with the development of dedicated algorithms for all three essential tasks. Unsupervised POS tagging (Chapter 7) is a well-studied task and algorithms achieving accurate tagging exist. All of them do not disambiguate high frequency words, only out-of-lexicon words are disambiguated. Most high frequency words bear syntactic information and thus, it is very important to differentiate between their different functions. Especially domain languages contain ambiguous and high frequent words bearing semantic information (e. g. pump). In order to improve POS tagging, an algorithm for disambiguation is developed and used to enhance an existing state-of-the-art tagger. This approach is based on context clustering which is used to detect a word type’s different syntactic functions. Evaluation shows that tagging accuracy is raised significantly. An approach to unsupervised syntactic parsing (Chapter 8) is developed in order to suffice the requirements of Relation Extraction. These requirements include high precision results on nominal and prepositional phrases as they contain the entities being relevant for Relation Extraction. Furthermore, accurate shallow parsing is more desirable than deep binary parsing as it facilitates Relation Extraction more than deep parsing. Endocentric and exocentric constructions can be distinguished and improve proper phrase labeling. unsuParse is based on preferred positions of word types within phrases to detect phrase candidates. Iterating the detection of simple phrases successively induces deeper structures. The proposed algorithm fulfills all demanded criteria and achieves competitive results on standard evaluation setups. Syntactic Relation Extraction (Chapter 9) is an approach exploiting syntactic statistics and text characteristics to extract relations between previously annotated entities. The approach is based on entity distributions given in a corpus and thus, provides a possibility to extend text mining processes to new data in an unsupervised manner. Evaluation on two different languages and two different text types of the automotive domain shows that it achieves accurate results on repair order data. Results are less accurate on internet data, but the task of sentiment analysis and extraction of the opinion target can be mastered. Thus, the incorporation of internet data is possible and important as it provides useful insight into the customer\''s thoughts. To conclude, this thesis presents a complete unsupervised workflow for Relation Extraction – except for the highly domain-dependent Entity Detection task – improving performance of each of the involved subtasks compared to state-of-the-art approaches. Furthermore, this work applies Natural Language Processing methods and Relation Extraction approaches to real world data unveiling challenges that do not occur in high quality newspaper corpora

Ontology learning from Italian legal texts

The paper reports on the methodology and preliminary results of a case study in automatically extracting ontological knowledge from Italian legislative texts. We use a fully-implemented ontology learning system (T2K) that includes a battery of tools for Natural Language Processing (NLP), statistical text analysis and machine language learning. Tools are dynamically integrated to provide an incremental representation of the content of vast repositories of unstructured documents. Evaluated results, however preliminary, show the great potential of NLP-powered incremental systems like T2K for accurate large-scale semi-automatic extraction of legal ontologies

Clustering of Russian Adjective-Noun Constructions using Word Embeddings

Peer reviewe

Beyond Discourse: Computational Text Analysis and Material Historical Processes

This dissertation proposes a general methodological framework for the application of computational text analysis to the study of long duration material processes of transformation, beyond their traditional application to the study of discourse and rhetorical action. Over a thin theory of the linguistic nature of social facts, the proposed methodology revolves around the compilation of term co-occurrence matrices and their projection into different representations of an hypothetical semantic space. These representations offer solutions to two problems inherent to social scientific research: that of "mapping" features in a given representation to theoretical entities and that of "alignment" of the features seen in models built from different sources in order to enable their comparison. The data requirements of the exercise are discussed through the introduction of the notion of a "narrative horizon", the extent to which a given source incorporates a narrative account in its rendering of the context that produces it. Useful primary data will consist of text with short narrative horizons, such that the ideal source will correspond to a continuous archive of institutional, ideally bureaucratic text produced as mere documentation of a definite population of more or less stable and comparable social facts across a couple of centuries. Such a primary source is available in the Proceedings of the Old Bailey (POB), a collection of transcriptions of 197,752 criminal trials seen by the Old Bailey and the Central Criminal Court of London and Middlesex between 1674 and 1913 that includes verbatim transcriptions of witness testimony. The POB is used to demonstrate the proposed framework, starting with the analysis of the evolution of an historical corpus to illustrate the procedure by which provenance data is used to construct longitudinal and cross-sectional comparisons of different corpus segments. The co-occurrence matrices obtained from the POB corpus are used to demonstrate two different projections: semantic networks that model different notions of similarity between the terms in a corpus' lexicon as an adjacency matrix describing a graph and semantic vector spaces that approximate a lower-dimensional representation of an hypothetical semantic space from its empirical effects on the co-occurrence matrix. Semantic networks are presented as discrete mathematical objects that offer a solution to the mapping problem through operation that allow for the construction of sets of terms over which an order can be induced using any measure of significance of the strength of association between a term set and its elements. Alignment can then be solved through different similarity measures computed over the intersection and union of the sets under comparison. Semantic vector spaces are presented as continuous mathematical objects that offer a solution to the mapping problem in the linear structures contained in them. This include, in all cases, a meaningful metric that makes it possible to define neighbourhoods and regions in the semantic space and, in some cases, a meaningful orientation that makes it possible to trace dimensions across them. Alignment can then proceed endogenously in the case of oriented vector spaces for relative comparisons, or through the construction of common basis sets for non-oriented semantic spaces for absolute comparisons. The dissertation concludes with the proposition of a general research program for the systematic compilation of text distributional patterns in order to facilitate a much needed process of calibration required by the techniques discussed in the previous chapters. Two specific avenues for further research are identified. First, the development of incremental methods of projection that allow a semantic model to be updated as new observations come along, an area that has received considerable attention from the field of electronic finance and the pervasive use of Gentleman's algorithm for matrix factorisation. Second, the development of additively decomposable models that may be combined or disaggregated to obtain a similar result to the one that would have been obtained had the model being computed from the union or difference of their inputs. This is established to be dependent on whether the functions that actualise a given model are associative under addition or not

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

A computer-based strategy for foreign-language vocabulary-learning

This work sets out to establish principles for the design and evaluation of a computer-based vocabulary-learning strategy for foreign language learners. The strategy is intended to assist non-beginner learners who are working on their own, to acquire new words in such a way that they will be available when needed in subsequent communicative situations.The nature of vocabulary-learning is examined from linguistic, psychological and educational perspectives, and a strategy for autonomous learning is derived which emphasizes the processes of: selection of new items from text, mental lexicon- building through the association of items on the basis of their lexical-structural features, and practising productive recall of items by activating the same associations as were used to build the mental network. This strategy is considered from the point of view of the support it would need from a computer-based interaction, and the field of Computer-Assisted Language Learning (CALL) for vocabulary is reviewed for examples of system design which meet the strategic and interactional requirements. Specifications are produced, based on general principles for the design of computer-assisted learning, and on current technological capability to integrate large text-databases and on-line lexical tools such as dictionaries etc., within an interface which facilitates learner control and exploration. Questions of evaluation are considered, in the light of the computer's ability to record interaction data, and a psycholinguistic model of word production is proposed as a basis for assessing the learner's performance in terms of processes as well as quantitative 'end product'. A general model of deep and surface approaches to learning is then adduced to provide a way of interpreting learner subjective data, and an independent means of evaluating the quality of the learning outcome.A system implementing the strategy is tested with learners of Spanish and English, and the quantitative and qualitative data on learning process and outcome is analyzed in depth. The system is shown to support the learning objectives for learners who adopt a deep approach, or whose approach complements the assumptions of the design in some way, and the general design principles are therefore considered as validated. Some aspects of the strategy related to lexicon-building, however, are shown to be inadequately supported, as is the capability of the system to help learners remediate surface approaches. The main conclusion of the study is that, whilst learner exploration of powerful lexical information resources is essential for autonomous vocabulary-learning, on-line tutorial help of the kind that will encourage deep rather than surface approaches, is needed to optimise the quality of the learning outcome

A Conceptual and Historical Analysis of Culturo-Behavior Science

An investigation of culturo-behavior science (CuBS) is introduced in three parts, and based on a naturalistic method to analyze theories and knowledge in scientific work. The method consists in two types of analysis: (a) Classification and analysis of constructs according to a structure of theories, and (b) classification and analysis of research products and modes of education according to a structure of scientific collectives. The conceptual origins of this method are outlined at the beginning of this work. The first part of this investigation addresses the logical role of the metacontingency construct in the theory. The historical development of this construct traced in order to identify the aims of this sort of analysis and the social problems it has been applied to. The second part of this investigation provides an assessment of the more general theoretical framework in CuBS. The outcomes of the second part of the investigation are specific solutions to the identified theoretical issues and a revised list of constructs. In the third and last part of the investigation, the aims and progress of the groups of researchers involved in the construction of CuBS are addressed. General conclusions and recommendations concerning the aims and progress of CuBS as a scientific collective are provided at the end of this work