def test_title_word_normalisation():
tests = [
("word", "word"),
("tube", "tube"),
("light", "light"),
("sense", "sense"),
("domain", "domain"),
("jacobi", "jacobi"),
("chance", "chance"),
("12345", "12345"),
("12345-7", "12345-7"),
("123456", "123456"),
("123456-7", "123456"),
("scaling", "scaling"),
("strange", "strange"),
("semantic", "semant"),
("research", "researc"),
("citation", "citatio"),
("searching", "search"),
("retrieval", "retriev"),
("iostreams", "iostrea"),
("similarity", "similar"),
("co-occurrence", "co-occ"),
]
test_framework.test_and_compare(tests, normalise_title_word, "Title word")
def test_title_word_normalisation():
tests = [
("word", "word"),
("tube", "tube"),
("light", "light"),
("sense", "sense"),
("domain", "domain"),
("jacobi", "jacobi"),
("chance", "chance"),
("12345", "12345"),
("12345-7", "12345-7"),
("123456", "123456"),
("123456-7", "123456"),
("scaling", "scaling"),
("strange", "strange"),
("semantic", "semant"),
("research", "researc"),
("citation", "citatio"),
("searching", "search"),
("retrieval", "retriev"),
("iostreams", "iostrea"),
("similarity", "similar"),
("co-occurrence", "co-occ"),
]
test_framework.test_and_compare(tests, normalise_title_word, "Title word")
def test_normalise_first_N_words_of_title():
def normalise_first_3_title_words(title):
N = 3
return normalise_first_N_words_of_title(title, N)
tests = [
("Experiments in word domain disambiguation for parallel texts",
"experim-word-domain"),
("The role of domain information in word sense disambiguation",
"role-domain-inform"),
("Automatic retrieval and clustering of similar words",
"automat-retriev-cluster"),
("A general framework for distributional similarity",
"general-framew-distrib"),
("A maximum entropy part-of-speech tagger",
"maximum-entropy-part-of"),
("Domain-specific sense distributions and predominant sense acquisition",
"domain-sense-distrib"),
("Sussx: WSD using automatically acquired predominant senses",
"sussx-wsd-automat"),
("Scaling context space",
"scaling-context-space"),
("Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity",
"co-occ-retriev-flexibl"),
("Discovering corpus-specific word senses",
"discov-corpus-word"),
("Using domain information for word sense disambiguation",
"domain-inform-word"),
("Web-scale distributional similarity and entity set expansion",
"web-distrib-similar"),
("From predicting predominant senses to local context for word sense disambiguation",
"predict-predom-senses"),
("Large-Scale Syntactic Processing: Parsing the Web Final Report of the 2009 JHU CLSP Workshop",
"large-syntact-process"),
("Robust, applied morphological generation",
"robust-applied-morphol"),
("From distributional to semantic similarity",
"distrib-semant-similar"),
("TnT: a statistical part-of-speech tagger",
"tnt-statist-part-of"),
("A best-first probabilistic shift-reduce parser",
"best-first-probab"),
("Using automatically acquired predominant senses for word sense disambiguation",
"automat-acquir-predom"),
("Evaluating WordNet-based Measures of Lexical Semantic Relatedness",
"evaluat-wordnet-measur"),
]
test_framework.test_and_compare(tests, normalise_first_3_title_words, "First 3 title words")
def test_author_name_normalisation():
tests = [
("Kay", "kay"),
("Mr.E", "mre"),
("Wang", "wang"),
("Curran", "curran"),
("Fekete", "fekete"),
("Manning", "manning"),
("Nothman", "nothman"),
("O'Keefe", "okeefe"),
("Ringland", "ringlan"),
("Koprinska", "koprins"),
("Kummerfeld", "kummerf"),
("Balasuriya", "balasur"),
(u"M\u00FCller", u'muller'), # Contains a non-ASCII character
("Durrant-White", "durrant"),
]
test_framework.test_and_compare(tests, normalise_lastname, "Author name")
def test_author_name_normalisation():
tests = [
("Kay", "kay"),
("Mr.E", "mre"),
("Wang", "wang"),
("Curran", "curran"),
("Fekete", "fekete"),
("Manning", "manning"),
("Nothman", "nothman"),
("O'Keefe", "okeefe"),
("Ringland", "ringlan"),
("Koprinska", "koprins"),
("Kummerfeld", "kummerf"),
("Balasuriya", "balasur"),
(u"M\u00FCller", u'muller'), # Contains a non-ASCII character
("Durrant-White", "durrant"),
]
test_framework.test_and_compare(tests, normalise_lastname, "Author name")
def test_normalise_first_N_words_of_title():
def normalise_first_3_title_words(title):
N = 3
return normalise_first_N_words_of_title(title, N)
tests = [
("Experiments in word domain disambiguation for parallel texts",
"experim-word-domain"),
("The role of domain information in word sense disambiguation",
"role-domain-inform"),
("Automatic retrieval and clustering of similar words",
"automat-retriev-cluster"),
("A general framework for distributional similarity",
"general-framew-distrib"),
("A maximum entropy part-of-speech tagger", "maximum-entropy-part-of"),
("Domain-specific sense distributions and predominant sense acquisition",
"domain-sense-distrib"),
("Sussx: WSD using automatically acquired predominant senses",
"sussx-wsd-automat"),
("Scaling context space", "scaling-context-space"),
("Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity",
"co-occ-retriev-flexibl"),
("Discovering corpus-specific word senses", "discov-corpus-word"),
("Using domain information for word sense disambiguation",
"domain-inform-word"),
("Web-scale distributional similarity and entity set expansion",
"web-distrib-similar"),
("From predicting predominant senses to local context for word sense disambiguation",
"predict-predom-senses"),
("Large-Scale Syntactic Processing: Parsing the Web Final Report of the 2009 JHU CLSP Workshop",
"large-syntact-process"),
("Robust, applied morphological generation", "robust-applied-morphol"),
("From distributional to semantic similarity",
"distrib-semant-similar"),
("TnT: a statistical part-of-speech tagger", "tnt-statist-part-of"),
("A best-first probabilistic shift-reduce parser",
"best-first-probab"),
("Using automatically acquired predominant senses for word sense disambiguation",
"automat-acquir-predom"),
("Evaluating WordNet-based Measures of Lexical Semantic Relatedness",
"evaluat-wordnet-measur"),
]
test_framework.test_and_compare(tests, normalise_first_3_title_words,
"First 3 title words")