Lazy and Eager Relational Learning Using Graph-Kernels View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Mathias Verbeke , Vincent Van Asch , Walter Daelemans , Luc De Raedt

ABSTRACT

Machine learning systems can be distinguished along two dimensions. The first is concerned with whether they deal with a feature based (propositional) or a relational representation; the second with the use of eager or lazy learning techniques. The advantage of relational learning is that it can capture structural information. We compare several machine learning techniques along these two dimensions on a binary sentence classification task (hedge cue detection). In particular, we use SVMs for eager learning, and \(k\)NN for lazy learning. Furthermore, we employ kLog, a kernel-based statistical relational learning framework as the relational framework. Within this framework we also contribute a novel lazy relational learning system. Our experiments show that relational learners are particularly good at handling long sentences, because of long distance dependencies. More... »

PAGES

171-184

References to SciGraph publications

  • 2003-10. Relational Case-based Reasoning for Carcinogenic Activity Prediction in ARTIFICIAL INTELLIGENCE REVIEW
  • 2012. Kernel-Based Logical and Relational Learning with kLog for Hedge Cue Detection in INDUCTIVE LOGIC PROGRAMMING
  • 2001. Propositionalization Approaches to Relational Data Mining in RELATIONAL DATA MINING
  • 2001-07-12. Similarity Assessment for Relational CBR in CASE-BASED REASONING RESEARCH AND DEVELOPMENT
  • 2012. Relational Learning for Spatial Relation Extraction from Natural Language in INDUCTIVE LOGIC PROGRAMMING
  • 1995-09. Support-vector networks in MACHINE LEARNING
  • 2012. A Relational Kernel-Based Framework for Hierarchical Image Understanding in STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION
  • Book

    TITLE

    Statistical Language and Speech Processing

    ISBN

    978-3-319-11396-8
    978-3-319-11397-5

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-11397-5_13

    DOI

    http://dx.doi.org/10.1007/978-3-319-11397-5_13

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1007515476


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "KU Leuven", 
              "id": "https://www.grid.ac/institutes/grid.5596.f", 
              "name": [
                "Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001\u00a0Heverlee, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Verbeke", 
            "givenName": "Mathias", 
            "id": "sg:person.014532405523.82", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014532405523.82"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Antwerp", 
              "id": "https://www.grid.ac/institutes/grid.5284.b", 
              "name": [
                "Department of Linguistics, Universiteit Antwerpen, Lange Winkelstraat 40-42, 2000\u00a0Antwerpen, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Van Asch", 
            "givenName": "Vincent", 
            "id": "sg:person.010311233515.28", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010311233515.28"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Antwerp", 
              "id": "https://www.grid.ac/institutes/grid.5284.b", 
              "name": [
                "Department of Linguistics, Universiteit Antwerpen, Lange Winkelstraat 40-42, 2000\u00a0Antwerpen, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Daelemans", 
            "givenName": "Walter", 
            "id": "sg:person.013134775677.47", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013134775677.47"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "KU Leuven", 
              "id": "https://www.grid.ac/institutes/grid.5596.f", 
              "name": [
                "Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001\u00a0Heverlee, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "De Raedt", 
            "givenName": "Luc", 
            "id": "sg:person.015333627665.77", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015333627665.77"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.eswa.2004.12.023", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011994357"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-662-04599-2_11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013074080", 
              "https://doi.org/10.1007/978-3-662-04599-2_11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.artint.2014.08.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017657546"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00994018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025150743", 
              "https://doi.org/10.1007/bf00994018"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-34166-3_19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029078308", 
              "https://doi.org/10.1007/978-3-642-34166-3_19"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/320434.320440", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030469836"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-31951-8_20", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038041734", 
              "https://doi.org/10.1007/978-3-642-31951-8_20"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-31951-8_29", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041776256", 
              "https://doi.org/10.1007/978-3-642-31951-8_29"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1026076312419", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047562750", 
              "https://doi.org/10.1023/a:1026076312419"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-44593-5_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053427744", 
              "https://doi.org/10.1007/3-540-44593-5_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-44593-5_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053427744", 
              "https://doi.org/10.1007/3-540-44593-5_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tsmc.1976.5408784", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061792985"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/1.9781611973440.75", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1088802021"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1017/cbo9780511486579", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1098739291"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3115/v1/p14-5015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099127830"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3115/v1/p14-5015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099127830"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3115/1596409.1596413", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099140356"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3115/1613715.1613796", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099150855"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2014", 
        "datePublishedReg": "2014-01-01", 
        "description": "Machine learning systems can be distinguished along two dimensions. The first is concerned with whether they deal with a feature based (propositional) or a relational representation; the second with the use of eager or lazy learning techniques. The advantage of relational learning is that it can capture structural information. We compare several machine learning techniques along these two dimensions on a binary sentence classification task (hedge cue detection). In particular, we use SVMs for eager learning, and \\(k\\)NN for lazy learning. Furthermore, we employ kLog, a kernel-based statistical relational learning framework as the relational framework. Within this framework we also contribute a novel lazy relational learning system. Our experiments show that relational learners are particularly good at handling long sentences, because of long distance dependencies.", 
        "editor": [
          {
            "familyName": "Besacier", 
            "givenName": "Laurent", 
            "type": "Person"
          }, 
          {
            "familyName": "Dediu", 
            "givenName": "Adrian-Horia", 
            "type": "Person"
          }, 
          {
            "familyName": "Mart\u00edn-Vide", 
            "givenName": "Carlos", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-319-11397-5_13", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-3-319-11396-8", 
            "978-3-319-11397-5"
          ], 
          "name": "Statistical Language and Speech Processing", 
          "type": "Book"
        }, 
        "name": "Lazy and Eager Relational Learning Using Graph-Kernels", 
        "pagination": "171-184", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-319-11397-5_13"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "23cc61e2091eddefcd4dde25017e33d484fa5805d9aff6ed6d9f1b0a71ce61ec"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1007515476"
            ]
          }
        ], 
        "publisher": {
          "location": "Cham", 
          "name": "Springer International Publishing", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-319-11397-5_13", 
          "https://app.dimensions.ai/details/publication/pub.1007515476"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-15T10:31", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8659_00000247.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/978-3-319-11397-5_13"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-11397-5_13'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-11397-5_13'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-11397-5_13'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-11397-5_13'


     

    This table displays all metadata directly associated to this object as RDF triples.

    154 TRIPLES      23 PREDICATES      43 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-319-11397-5_13 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N4d28e95e75a0494e89b196aaa7d95a55
    4 schema:citation sg:pub.10.1007/3-540-44593-5_4
    5 sg:pub.10.1007/978-3-642-31951-8_20
    6 sg:pub.10.1007/978-3-642-31951-8_29
    7 sg:pub.10.1007/978-3-642-34166-3_19
    8 sg:pub.10.1007/978-3-662-04599-2_11
    9 sg:pub.10.1007/bf00994018
    10 sg:pub.10.1023/a:1026076312419
    11 https://doi.org/10.1016/j.artint.2014.08.003
    12 https://doi.org/10.1016/j.eswa.2004.12.023
    13 https://doi.org/10.1017/cbo9780511486579
    14 https://doi.org/10.1109/tsmc.1976.5408784
    15 https://doi.org/10.1137/1.9781611973440.75
    16 https://doi.org/10.1145/320434.320440
    17 https://doi.org/10.3115/1596409.1596413
    18 https://doi.org/10.3115/1613715.1613796
    19 https://doi.org/10.3115/v1/p14-5015
    20 schema:datePublished 2014
    21 schema:datePublishedReg 2014-01-01
    22 schema:description Machine learning systems can be distinguished along two dimensions. The first is concerned with whether they deal with a feature based (propositional) or a relational representation; the second with the use of eager or lazy learning techniques. The advantage of relational learning is that it can capture structural information. We compare several machine learning techniques along these two dimensions on a binary sentence classification task (hedge cue detection). In particular, we use SVMs for eager learning, and \(k\)NN for lazy learning. Furthermore, we employ kLog, a kernel-based statistical relational learning framework as the relational framework. Within this framework we also contribute a novel lazy relational learning system. Our experiments show that relational learners are particularly good at handling long sentences, because of long distance dependencies.
    23 schema:editor Ne9529688883c444c8387fb53f069985b
    24 schema:genre chapter
    25 schema:inLanguage en
    26 schema:isAccessibleForFree false
    27 schema:isPartOf N7c4914ae22ca4309924e698e1a317835
    28 schema:name Lazy and Eager Relational Learning Using Graph-Kernels
    29 schema:pagination 171-184
    30 schema:productId N0892aab1a28840de9a5c28ec008d2e8d
    31 N173ea4a066d446d38f03f04c05b9fb7f
    32 N5764b893c8e0491fbd4d94ce0d0efb8b
    33 schema:publisher Na9dceeb0555f40f1963ea9e074739386
    34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007515476
    35 https://doi.org/10.1007/978-3-319-11397-5_13
    36 schema:sdDatePublished 2019-04-15T10:31
    37 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    38 schema:sdPublisher Nb970acbcc13d4cd3a78bc6789aff5542
    39 schema:url http://link.springer.com/10.1007/978-3-319-11397-5_13
    40 sgo:license sg:explorer/license/
    41 sgo:sdDataset chapters
    42 rdf:type schema:Chapter
    43 N0892aab1a28840de9a5c28ec008d2e8d schema:name dimensions_id
    44 schema:value pub.1007515476
    45 rdf:type schema:PropertyValue
    46 N0918bd05e7e2425a9cba9291cc94ef48 rdf:first N631f75dbff814ae3b8fa2eef6d9b47ab
    47 rdf:rest rdf:nil
    48 N0f200c40c9bb4596ac37af43fcf53145 rdf:first sg:person.013134775677.47
    49 rdf:rest N3d24b48261174ef4a8de6cf6c2d4b9a2
    50 N16e09890a1cd44cea94e099a24491215 schema:familyName Dediu
    51 schema:givenName Adrian-Horia
    52 rdf:type schema:Person
    53 N1701bce87c85404e909a5a30d3de013b rdf:first sg:person.010311233515.28
    54 rdf:rest N0f200c40c9bb4596ac37af43fcf53145
    55 N173ea4a066d446d38f03f04c05b9fb7f schema:name readcube_id
    56 schema:value 23cc61e2091eddefcd4dde25017e33d484fa5805d9aff6ed6d9f1b0a71ce61ec
    57 rdf:type schema:PropertyValue
    58 N230765fbb17e443aacb82c276f2c9656 schema:familyName Besacier
    59 schema:givenName Laurent
    60 rdf:type schema:Person
    61 N3d24b48261174ef4a8de6cf6c2d4b9a2 rdf:first sg:person.015333627665.77
    62 rdf:rest rdf:nil
    63 N4d28e95e75a0494e89b196aaa7d95a55 rdf:first sg:person.014532405523.82
    64 rdf:rest N1701bce87c85404e909a5a30d3de013b
    65 N5764b893c8e0491fbd4d94ce0d0efb8b schema:name doi
    66 schema:value 10.1007/978-3-319-11397-5_13
    67 rdf:type schema:PropertyValue
    68 N5786461d9b634804a9e259e7055b3dca rdf:first N16e09890a1cd44cea94e099a24491215
    69 rdf:rest N0918bd05e7e2425a9cba9291cc94ef48
    70 N631f75dbff814ae3b8fa2eef6d9b47ab schema:familyName Martín-Vide
    71 schema:givenName Carlos
    72 rdf:type schema:Person
    73 N7c4914ae22ca4309924e698e1a317835 schema:isbn 978-3-319-11396-8
    74 978-3-319-11397-5
    75 schema:name Statistical Language and Speech Processing
    76 rdf:type schema:Book
    77 Na9dceeb0555f40f1963ea9e074739386 schema:location Cham
    78 schema:name Springer International Publishing
    79 rdf:type schema:Organisation
    80 Nb970acbcc13d4cd3a78bc6789aff5542 schema:name Springer Nature - SN SciGraph project
    81 rdf:type schema:Organization
    82 Ne9529688883c444c8387fb53f069985b rdf:first N230765fbb17e443aacb82c276f2c9656
    83 rdf:rest N5786461d9b634804a9e259e7055b3dca
    84 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    85 schema:name Information and Computing Sciences
    86 rdf:type schema:DefinedTerm
    87 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    88 schema:name Artificial Intelligence and Image Processing
    89 rdf:type schema:DefinedTerm
    90 sg:person.010311233515.28 schema:affiliation https://www.grid.ac/institutes/grid.5284.b
    91 schema:familyName Van Asch
    92 schema:givenName Vincent
    93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010311233515.28
    94 rdf:type schema:Person
    95 sg:person.013134775677.47 schema:affiliation https://www.grid.ac/institutes/grid.5284.b
    96 schema:familyName Daelemans
    97 schema:givenName Walter
    98 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013134775677.47
    99 rdf:type schema:Person
    100 sg:person.014532405523.82 schema:affiliation https://www.grid.ac/institutes/grid.5596.f
    101 schema:familyName Verbeke
    102 schema:givenName Mathias
    103 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014532405523.82
    104 rdf:type schema:Person
    105 sg:person.015333627665.77 schema:affiliation https://www.grid.ac/institutes/grid.5596.f
    106 schema:familyName De Raedt
    107 schema:givenName Luc
    108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015333627665.77
    109 rdf:type schema:Person
    110 sg:pub.10.1007/3-540-44593-5_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053427744
    111 https://doi.org/10.1007/3-540-44593-5_4
    112 rdf:type schema:CreativeWork
    113 sg:pub.10.1007/978-3-642-31951-8_20 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038041734
    114 https://doi.org/10.1007/978-3-642-31951-8_20
    115 rdf:type schema:CreativeWork
    116 sg:pub.10.1007/978-3-642-31951-8_29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041776256
    117 https://doi.org/10.1007/978-3-642-31951-8_29
    118 rdf:type schema:CreativeWork
    119 sg:pub.10.1007/978-3-642-34166-3_19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029078308
    120 https://doi.org/10.1007/978-3-642-34166-3_19
    121 rdf:type schema:CreativeWork
    122 sg:pub.10.1007/978-3-662-04599-2_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013074080
    123 https://doi.org/10.1007/978-3-662-04599-2_11
    124 rdf:type schema:CreativeWork
    125 sg:pub.10.1007/bf00994018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025150743
    126 https://doi.org/10.1007/bf00994018
    127 rdf:type schema:CreativeWork
    128 sg:pub.10.1023/a:1026076312419 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047562750
    129 https://doi.org/10.1023/a:1026076312419
    130 rdf:type schema:CreativeWork
    131 https://doi.org/10.1016/j.artint.2014.08.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017657546
    132 rdf:type schema:CreativeWork
    133 https://doi.org/10.1016/j.eswa.2004.12.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011994357
    134 rdf:type schema:CreativeWork
    135 https://doi.org/10.1017/cbo9780511486579 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098739291
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.1109/tsmc.1976.5408784 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061792985
    138 rdf:type schema:CreativeWork
    139 https://doi.org/10.1137/1.9781611973440.75 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088802021
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1145/320434.320440 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030469836
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.3115/1596409.1596413 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099140356
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.3115/1613715.1613796 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099150855
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.3115/v1/p14-5015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099127830
    148 rdf:type schema:CreativeWork
    149 https://www.grid.ac/institutes/grid.5284.b schema:alternateName University of Antwerp
    150 schema:name Department of Linguistics, Universiteit Antwerpen, Lange Winkelstraat 40-42, 2000 Antwerpen, Belgium
    151 rdf:type schema:Organization
    152 https://www.grid.ac/institutes/grid.5596.f schema:alternateName KU Leuven
    153 schema:name Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001 Heverlee, Belgium
    154 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...