Search (12 results, page 1 of 1)

0.39343736 = product of:
  0.5508123 = sum of:
    0.018316243 = product of:
      0.045790605 = sum of:
        0.02197135 = weight(_text_:retrieval in 563) [ClassicSimilarity], result of:
          0.02197135 = score(doc=563,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.20052543 = fieldWeight in 563, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=563)
        0.023819257 = weight(_text_:system in 563) [ClassicSimilarity], result of:
          0.023819257 = score(doc=563,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.20878783 = fieldWeight in 563, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=563)
      0.4 = coord(2/5)
    0.17259108 = weight(_text_:2f in 563) [ClassicSimilarity], result of:
      0.17259108 = score(doc=563,freq=2.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.56201804 = fieldWeight in 563, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=563)
    0.17259108 = weight(_text_:2f in 563) [ClassicSimilarity], result of:
      0.17259108 = score(doc=563,freq=2.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.56201804 = fieldWeight in 563, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=563)
    0.17259108 = weight(_text_:2f in 563) [ClassicSimilarity], result of:
      0.17259108 = score(doc=563,freq=2.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.56201804 = fieldWeight in 563, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=563)
    0.0147228 = product of:
      0.0294456 = sum of:
        0.0294456 = weight(_text_:22 in 563) [ClassicSimilarity], result of:
          0.0294456 = score(doc=563,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.23214069 = fieldWeight in 563, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=563)
      0.5 = coord(1/2)
  0.71428573 = coord(5/7)

Abstract

In this thesis we propose three new word association measures for multi-word term extraction. We combine these association measures with LocalMaxs algorithm in our extraction model and compare the results of different multi-word term extraction methods. Our approach is language and domain independent and requires no training data. It can be applied to such tasks as text summarization, information retrieval, and document classification. We further explore the potential of using multi-word terms as an effective representation for general web-page summarization. We extract multi-word terms from human written summaries in a large collection of web-pages, and generate the summaries by aligning document words with these multi-word terms. Our system applies machine translation technology to learn the aligning process from a training set and focuses on selecting high quality multi-word terms from human written summaries to generate suitable results for web-page summarization.

Content

A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Computer Science. Vgl. Unter: http://www.inf.ufrgs.br%2F~ceramisch%2Fdownload_files%2Fpublications%2F2009%2Fp01.pdf.

Date

10. 1.2013 19:22:47

0.002507761 = product of:
  0.017554326 = sum of:
    0.017554326 = product of:
      0.043885816 = sum of:
        0.012816621 = weight(_text_:retrieval in 572) [ClassicSimilarity], result of:
          0.012816621 = score(doc=572,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.11697317 = fieldWeight in 572, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02734375 = fieldNorm(doc=572)
        0.031069195 = weight(_text_:system in 572) [ClassicSimilarity], result of:
          0.031069195 = score(doc=572,freq=10.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.2723372 = fieldWeight in 572, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02734375 = fieldNorm(doc=572)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

Identifikation der Sprache bzw. Sprachen elektronischer Textdokumente ist einer der wichtigsten Schritte in vielen Prozessen maschineller Textverarbeitung. Die vorliegende Arbeit stellt LangIdent, ein System zur Sprachidentifikation von mono- und multilingualen elektronischen Textdokumenten vor. Das System bietet sowohl eine Auswahl von gängigen Algorithmen für die Sprachidentifikation monolingualer Textdokumente als auch einen neuen Algorithmus für die Sprachidentifikation multilingualer Textdokumente.
Mit der Verbreitung des Internets vermehrt sich die Menge der im World Wide Web verfügbaren Dokumente. Die Gewährleistung eines effizienten Zugangs zu gewünschten Informationen für die Internetbenutzer wird zu einer großen Herausforderung an die moderne Informationsgesellschaft. Eine Vielzahl von Werkzeugen wird bereits eingesetzt, um den Nutzern die Orientierung in der wachsenden Informationsflut zu erleichtern. Allerdings stellt die enorme Menge an unstrukturierten und verteilten Informationen nicht die einzige Schwierigkeit dar, die bei der Entwicklung von Werkzeugen dieser Art zu bewältigen ist. Die zunehmende Vielsprachigkeit von Web-Inhalten resultiert in dem Bedarf an Sprachidentifikations-Software, die Sprache/en von elektronischen Dokumenten zwecks gezielter Weiterverarbeitung identifiziert. Solche Sprachidentifizierer können beispielsweise effektiv im Bereich des Multilingualen Information Retrieval eingesetzt werden, da auf den Sprachidentifikationsergebnissen Prozesse der automatischen Indexbildung wie Stemming, Stoppwörterextraktion etc. aufbauen. In der vorliegenden Arbeit wird das neue System "LangIdent" zur Sprachidentifikation von elektronischen Textdokumenten vorgestellt, das in erster Linie für Lehre und Forschung an der Universität Hildesheim verwendet werden soll. "LangIdent" enthält eine Auswahl von gängigen Algorithmen zu der monolingualen Sprachidentifikation, die durch den Benutzer interaktiv ausgewählt und eingestellt werden können. Zusätzlich wurde im System ein neuer Algorithmus implementiert, der die Identifikation von Sprachen, in denen ein multilinguales Dokument verfasst ist, ermöglicht. Die Identifikation beschränkt sich nicht nur auf eine Aufzählung von gefundenen Sprachen, vielmehr wird der Text in monolinguale Abschnitte aufgeteilt, jeweils mit der Angabe der identifizierten Sprache.
Die Arbeit wird in zwei Hauptteile gegliedert. Der erste Teil besteht aus Kapiteln 1-5, in denen theoretische Grundlagen zum Thema Sprachidentifikation dargelegt werden. Das erste Kapitel beschreibt den Sprachidentifikationsprozess und definiert grundlegende Begriffe. Im zweiten und dritten Kapitel werden vorherrschende Ansätze zur Sprachidentifikation von monolingualen Dokumenten dargestellt und miteinander verglichen, indem deren Vor- und Nachteile diskutiert werden. Das vierte Kapitel stellt einige Arbeiten vor, die sich mit der Sprachidentifikation von multilingualen Texten befasst haben. Der erste Teil der Arbeit wird mit einem Überblick über die bereits entwickelten und im Internet verfügbaren Sprachidentifikationswerkzeuge abgeschlossen. Der zweite Teil der Arbeit stellt die Entwicklung des Sprachidentifikationssystems LangIdent dar. In den Kapiteln 6 und 7 werden die an das System gestellten Anforderungen zusammengefasst und die wichtigsten Phasen des Projekts definiert. In den weiterführenden Kapiteln 8 und 9 werden die Systemarchitektur und eine detaillierte Beschreibung ihrer Kernkomponenten gegeben. Das Kapitel 10 liefert ein statisches UML-Klassendiagramm mit einer ausführlichen Erklärung von Attributen und Methoden der im Diagramm vorgestellten Klassen. Das nächste Kapitel befasst sich mit den im Prozess der Systementwicklung aufgetretenen Problemen. Die Bedienung des Programms wird im Kapitel 12 beschrieben. Im letzten Kapitel der Arbeit wird die Systemevaluierung vorgestellt, in der der Aufbau und Umfang von Trainingskorpora sowie die wichtigsten Ergebnisse mit der anschließenden Diskussion präsentiert werden.

0.0024669599 = product of:
  0.01726872 = sum of:
    0.01726872 = product of:
      0.043171797 = sum of:
        0.020714786 = weight(_text_:retrieval in 548) [ClassicSimilarity], result of:
          0.020714786 = score(doc=548,freq=4.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.18905719 = fieldWeight in 548, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=548)
        0.022457011 = weight(_text_:system in 548) [ClassicSimilarity], result of:
          0.022457011 = score(doc=548,freq=4.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.19684705 = fieldWeight in 548, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03125 = fieldNorm(doc=548)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Content

Enthält die Kapitel: 2: Wissensrepräsentation 2.1 Deklarative Wissensrepräsentation 2.2 Klassifikationen des BMM 2.3 Thesauri und Ontologien: existierende kommerzielle Software 2.4 Erstellung eines Thesaurus im Rahmen des LeWi-Projektes 3: Analysekomponenten 3.1 Sprachliche Phänomene in der maschinellen Textanalyse 3.2 Analysekomponenten: Lösungen und Forschungsansätze 3.3 Die Analysekomponenten im LeWi-Projekt 4: Information Retrieval 4.1 Grundlagen des Information Retrieval 4.2 Automatische Indexierungsmethoden und -verfahren 4.3 Automatische Indexierung des BMM im Rahmen des LeWi-Projektes 4.4 Suchstrategien und Suchablauf im LeWi-Kontext

RSWK

Volltextdatenbank / Natürlichsprachiges System

Subject

Volltextdatenbank / Natürlichsprachiges System

0.0021032572 = product of:
  0.0147228 = sum of:
    0.0147228 = product of:
      0.0294456 = sum of:
        0.0294456 = weight(_text_:22 in 1746) [ClassicSimilarity], result of:
          0.0294456 = score(doc=1746,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.23214069 = fieldWeight in 1746, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=1746)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

22. 3.2015 9:17:30

0.0018121665 = product of:
  0.012685165 = sum of:
    0.012685165 = product of:
      0.063425824 = sum of:
        0.063425824 = weight(_text_:retrieval in 1172) [ClassicSimilarity], result of:
          0.063425824 = score(doc=1172,freq=6.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.5788671 = fieldWeight in 1172, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.078125 = fieldNorm(doc=1172)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

RSWK

Englisch / Anapher <Syntax> / Hypertext / Information Retrieval / Korpus <Linguistik>

Subject

Englisch / Anapher <Syntax> / Hypertext / Information Retrieval / Korpus <Linguistik>

0.0014796278 = product of:
  0.010357394 = sum of:
    0.010357394 = product of:
      0.051786967 = sum of:
        0.051786967 = weight(_text_:retrieval in 1810) [ClassicSimilarity], result of:
          0.051786967 = score(doc=1810,freq=4.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.47264296 = fieldWeight in 1810, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.078125 = fieldNorm(doc=1810)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Content

Trägerin des VFI-Dissertationspreises 2014: "Überzeugende gründliche linguistische und quantitative Analyse eines im Information Retrieval bisher wenig beachteten Textelementes anhand eines eigens erstellten grossen Hypertextkorpus, einschliesslich der Evaluation selbsterstellter Auflösungsregeln für die Nutzung in künftigen IR-Systemen.".

0.0010357393 = product of:
  0.007250175 = sum of:
    0.007250175 = product of:
      0.036250874 = sum of:
        0.036250874 = weight(_text_:retrieval in 127) [ClassicSimilarity], result of:
          0.036250874 = score(doc=127,freq=16.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.33085006 = fieldWeight in 127, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02734375 = fieldNorm(doc=127)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Abstract

This thesis is organised as follows: Chapter 2 gives a general introduction to the field of information retrieval, covering its most important aspects. Further, the tasks of distributed and peer-to-peer information retrieval (P2PIR) are introduced, motivating their application and characterising the special challenges that they involve, including a review of existing architectures and search protocols in P2PIR. Finally, chapter 2 presents approaches to evaluating the e ectiveness of both traditional and peer-to-peer IR systems. Chapter 3 contains a detailed account of state-of-the-art information retrieval models and algorithms. This encompasses models for matching queries against document representations, term weighting algorithms, approaches to feedback and associative retrieval as well as distributed retrieval. It thus defines important terminology for the following chapters. The notion of "multi-level association graphs" (MLAGs) is introduced in chapter 4. An MLAG is a simple, graph-based framework that allows to model most of the theoretical and practical approaches to IR presented in chapter 3. Moreover, it provides an easy-to-grasp way of defining and including new entities into IR modeling, such as paragraphs or peers, dividing them conceptually while at the same time connecting them to each other in a meaningful way. This allows for a unified view on many IR tasks, including that of distributed and peer-to-peer search. Starting from related work and a formal defiition of the framework, the possibilities of modeling that it provides are discussed in detail, followed by an experimental section that shows how new insights gained from modeling inside the framework can lead to novel combinations of principles and eventually to improved retrieval effectiveness.
Chapter 5 empirically tackles the first of the two research questions formulated above, namely the question of global collection statistics. More precisely, it studies possibilities of radically simplified results merging. The simplification comes from the attempt - without having knowledge of the complete collection - to equip all peers with the same global statistics, making document scores comparable across peers. Chapter 5 empirically tackles the first of the two research questions formulated above, namely the question of global collection statistics. More precisely, it studies possibilities of radically simplified results merging. The simplification comes from the attempt - without having knowledge of the complete collection - to equip all peers with the same global statistics, making document scores comparable across peers. What is examined, is the question of how we can obtain such global statistics and to what extent their use will lead to a drop in retrieval effectiveness. In chapter 6, the second research question is tackled, namely that of making forwarding decisions for queries, based on profiles of other peers. After a review of related work in that area, the chapter first defines the approaches that will be compared against each other. Then, a novel evaluation framework is introduced, including a new measure for comparing results of a distributed search engine against those of a centralised one. Finally, the actual evaluation is performed using the new framework.

6.8055023E-4 = product of:
  0.0047638514 = sum of:
    0.0047638514 = product of:
      0.023819257 = sum of:
        0.023819257 = weight(_text_:system in 2849) [ClassicSimilarity], result of:
          0.023819257 = score(doc=2849,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.20878783 = fieldWeight in 2849, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=2849)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Abstract

Natural Language Classifier helfen Unternehmen zunehmend dabei die Flut von Textdaten zu überwinden. Aber diese Classifier, einmal trainiert, verlieren mit der Zeit ihre Nützlichkeit. Sie bleiben statisch, aber die zugrundeliegende Domäne der Textdaten verändert sich: Ihre Genauigkeit nimmt aufgrund eines Phänomens ab, das als Konzeptdrift bekannt ist. Die Frage ist ob Konzeptdrift durch die Ausgabe eines Classifiers zuverlässig erkannt werden kann, und falls ja: ist es möglich dem durch nachtrainieren des Classifiers entgegenzuwirken. Es wird eine System-Implementierung mittels Proof-of-Concept vorgestellt, bei der das Konfidenzmass des Classifiers zur Erkennung von Konzeptdrift verwendet wird. Der Classifier wird dann iterativ neu trainiert, indem er Stichproben mit niedrigem Konfidenzmass auswählt, sie korrigiert und im Trainingsset der nächsten Iteration verwendet. Die Leistung des Classifiers wird über die Zeit gemessen, und die Leistung des Systems beobachtet. Basierend darauf werden schließlich Empfehlungen gegeben, die sich bei der Implementierung solcher Systeme als nützlich erweisen können.

5.6712516E-4 = product of:
  0.003969876 = sum of:
    0.003969876 = product of:
      0.01984938 = sum of:
        0.01984938 = weight(_text_:system in 3194) [ClassicSimilarity], result of:
          0.01984938 = score(doc=3194,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.17398985 = fieldWeight in 3194, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3194)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Abstract

Als Extraktionssysteme wurden der TreeTagger und die Indexierungssoftware Lingo verwendet. Der TreeTagger basiert auf einem statistischen Tagging- und Chunking- Algorithmus, mit dessen Hilfe NPs automatisch identifiziert und extrahiert werden. Er kann für verschiedene Anwendungsszenarien der natürlichen Sprachverarbeitung eingesetzt werden, in erster Linie als POS-Tagger für unterschiedliche Sprachen. Das Indexierungssystem Lingo arbeitet im Gegensatz zum TreeTagger mit elektronischen Wörterbüchern und einem musterbasierten Abgleich. Lingo ist ein auf automatische Indexierung ausgerichtetes System, was eine Vielzahl von Modulen mitliefert, die individuell auf eine bestimmte Aufgabenstellung angepasst und aufeinander abgestimmt werden können. Die unterschiedlichen Verarbeitungsweisen haben sich in den Ergebnismengen beider Systeme deutlich gezeigt. Die gering ausfallenden Übereinstimmungen der Ergebnismengen verdeutlichen die abweichende Funktionsweise und konnte mit einer qualitativen Analyse beispielhaft beschrieben werden. In der vorliegenden Arbeit kann abschließend nicht geklärt werden, welches der beiden Systeme bevorzugt für die Generierung von Indextermen eingesetzt werden sollte.

5.2312744E-4 = product of:
  0.003661892 = sum of:
    0.003661892 = product of:
      0.01830946 = sum of:
        0.01830946 = weight(_text_:retrieval in 2380) [ClassicSimilarity], result of:
          0.01830946 = score(doc=2380,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.16710453 = fieldWeight in 2380, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2380)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Theme

Semantisches Umfeld in Indexierung u. Retrieval

4.5370017E-4 = product of:
  0.003175901 = sum of:
    0.003175901 = product of:
      0.015879504 = sum of:
        0.015879504 = weight(_text_:system in 104) [ClassicSimilarity], result of:
          0.015879504 = score(doc=104,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.13919188 = fieldWeight in 104, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03125 = fieldNorm(doc=104)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Abstract

The dissertation establishes 'binominal lexeme' as a comparative concept and discusses its cross-linguistic typology and semantics. Informally, a binominal lexeme is a noun-noun compound or functional equivalent; more precisely, it is a lexical item that consists primarily of two thing-morphs between which there exists an unstated semantic relation. Examples of binominals include Mandarin Chinese ?? (tielù) [iron road], French chemin de fer [way of iron] and Russian ???????? ?????? (zeleznaja doroga) [iron:adjz road]. All of these combine a word denoting 'iron' and a word denoting 'road' or 'way' to denote the meaning railway. In each case, the unstated semantic relation is one of composition: a railway is conceptualized as a road that is composed (or made) of iron. However, three different morphosyntactic strategies are employed: compounding, prepositional phrase and relational adjective. This study explores the range of such strategies used by a worldwide sample of 106 languages to express a set of 100 meanings from various semantic domains, resulting in a classification consisting of nine different morphosyntactic types. The semantic relations found in the data are also explored and a classification called the Hatcher-Bourque system is developed that operates at two levels of granularity, together with a tool for classifying binominals, the Bourquifier. The classification is extended to other subfields of language, including metonymy and lexical semantics, and beyond language to the domain of knowledge representation, resulting in a proposal for a general model of associative relations called the PHAB model. The many findings of the research include universals concerning the recruitment of anchoring nominal modification strategies, a method for comparing non-binary typologies, the non-universality (despite its predominance) of compounding, and a scale of frequencies for semantic relations which may provide insights into the associative nature of human thought.

3.661892E-4 = product of:
  0.0025633243 = sum of:
    0.0025633243 = product of:
      0.012816621 = sum of:
        0.012816621 = weight(_text_:retrieval in 1536) [ClassicSimilarity], result of:
          0.012816621 = score(doc=1536,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.11697317 = fieldWeight in 1536, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1536)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Abstract

Multiword expressions (MWEs) are lexical items that can be decomposed into single words and display lexical, syntactic, semantic, pragmatic and/or statistical idiosyncrasy (Sag et al., 2002; Kim, 2008; Calzolari et al., 2002). The proper treatment of multiword expressions such as rock 'n' roll and make a decision is essential for many natural language processing (NLP) applications like information extraction and retrieval, terminology extraction and machine translation, and it is important to identify multiword expressions in context. For example, in machine translation we must know that MWEs form one semantic unit, hence their parts should not be translated separately. For this, multiword expressions should be identified first in the text to be translated. The chief aim of this thesis is to develop machine learning-based approaches for the automatic detection of different types of multiword expressions in English and Hungarian natural language texts. In our investigations, we pay attention to the characteristics of different types of multiword expressions such as nominal compounds, multiword named entities and light verb constructions, and we apply novel methods to identify MWEs in raw texts. In the thesis it will be demonstrated that nominal compounds and multiword amed entities may require a similar approach for their automatic detection as they behave in the same way from a linguistic point of view. Furthermore, it will be shown that the automatic detection of light verb constructions can be carried out using two effective machine learning-based approaches.