def _parse(toklist):
""" Parse a token list as a query """
# Parse with the nonterminal 'QueryRoot' as the grammar root
with Fast_Parser(verbose=False, root=_QUERY_ROOT) as bp:
sent_begin = 0
num_sent = 0
num_parsed_sent = 0
rdc = Reducer(bp.grammar)
trees = dict()
sent = []
for ix, t in enumerate(toklist):
if t[0] == TOK.S_BEGIN:
sent = []
sent_begin = ix
elif t[0] == TOK.S_END:
slen = len(sent)
if not slen:
continue
num_sent += 1
# Parse the accumulated sentence
num = 0
try:
# Parse the sentence
forest = bp.go(sent)
if forest is not None:
num = Fast_Parser.num_combinations(forest)
if num > 1:
# Reduce the resulting forest
forest = rdc.go(forest)
except ParseError as e:
forest = None
if num > 0:
num_parsed_sent += 1
# Obtain a text representation of the parse tree
trees[num_sent] = ParseForestDumper.dump_forest(forest)
#ParseForestPrinter.print_forest(forest)
elif t[0] == TOK.P_BEGIN:
pass
elif t[0] == TOK.P_END:
pass
else:
sent.append(t)
result = dict(num_sent=num_sent, num_parsed_sent=num_parsed_sent)
return result, trees
def make_tree(text: str) -> Tree:
toklist = tokenize(text)
fp = Fast_Parser(verbose=False)
ip = IncrementalParser(fp, toklist, verbose=False)
# Dict of parse trees in string dump format,
# stored by sentence index (1-based)
trees = OrderedDict()
num_sent = 0
for p in ip.paragraphs():
for sent in p.sentences():
num_sent += 1
num_tokens = len(sent)
assert sent.parse(), "Sentence does not parse: " + sent.text
# Obtain a text representation of the parse tree
token_dicts = TreeUtility.dump_tokens(sent.tokens, sent.tree)
# Create a verbose text representation of
# the highest scoring parse tree
tree = ParseForestDumper.dump_forest(sent.tree, token_dicts=token_dicts)
# Add information about the sentence tree's score
# and the number of tokens
trees[num_sent] = "\n".join(
["C{0}".format(sent.score), "L{0}".format(num_tokens), tree]
)
# Create a tree representation string out of
# all the accumulated parse trees
tree_string = "".join("S{0}\n{1}\n".format(key, val) for key, val in trees.items())
tree = Tree()
tree.load(tree_string)
return tree
def parse_text_with_full_tree(session, text, all_names=False):
""" Parse plain text, assumed to contain one sentence only, and
return its simplified form as well as its full form. """
full_tree = None
def xform(tokens, tree, err_index):
""" Transformation function that yields a simplified parse tree
with POS-tagged, normalized terminal leaves for the sentence """
if err_index is not None:
return TreeUtility.dump_tokens(tokens,
tree,
error_index=err_index)
# Successfully parsed: return a simplified tree for the sentence
nonlocal full_tree
# We are assuming that there is only one parsed sentence
if full_tree is None:
# Note the full tree of the first parsed paragraph
full_tree = tree
return TreeUtility._simplify_tree(tokens, tree)
with Fast_Parser(verbose=False) as parser:
pgs, stats, _ = TreeUtility._process_text(parser, session, text,
all_names, xform)
if (not pgs or stats["num_parsed"] == 0 or not pgs[0]
or any("err" in t for t in pgs[0][0])):
# The first sentence didn't parse: let's not beat around the bush with that fact
return (None, None, stats)
# Return the simplified tree, full tree and stats
assert full_tree is not None
return (pgs[0][0], full_tree, stats)
def tag_text(
session: Session, text: str, all_names: bool = False
) -> Tuple[PgsList, StatsDict, Optional["RegisterType"]]:
""" Parse plain text and return the parsed paragraphs as lists of sentences
where each sentence is a list of tagged tokens """
# Don't emit diagnostic messages
with Fast_Parser(verbose=False) as parser:
return TreeUtility.raw_tag_text(parser, session, text, all_names=all_names)
def tag_text(session, text, all_names=False):
""" Parse plain text and return the parsed paragraphs as lists of sentences
where each sentence is a list of tagged tokens """
# Don't emit diagnostic messages
with Fast_Parser(verbose=False) as parser:
return TreeUtility.raw_tag_text(parser,
session,
text,
all_names=all_names)
def parse_text_to_bracket_form(session: Session, text: str):
""" Parse plain text and return the parsed paragraphs as bracketed strings """
def xform(
tokens: List[Tok], tree: Optional[Node], err_index: Optional[int]
) -> str:
""" Transformation function that yields a simplified parse tree
with POS-tagged, normalized terminal leaves for the sentence """
if err_index is not None:
# Return an empty string for sentences that don't parse
return ""
# Successfully parsed: obtain a simplified tree for the sentence
result = []
def push(node: Optional[CanonicalTokenDict]) -> None:
""" Append information about a node to the result list """
if node is None:
return
nonlocal result
if node.get("k") == "NONTERMINAL":
node = cast(SimpleTreeNode, node)
result.append("(" + node.get("i", ""))
# Recursively add the children of this nonterminal
for child in node.get("p", []):
result.append(" ")
push(child)
result.append(")")
elif node.get("k") == "PUNCTUATION":
pass
# Include punctuation?
# If so, do something like:
# result.push("(PUNCT |" + node["x"] + "|)")
else:
# Terminal: append the text
result.append(node.get("x", "").replace(" ", "_"))
# This uses a custom simplification scheme
simple_tree = TreeUtility._simplify_tree(
tokens,
tree,
nt_map=_TEST_NT_MAP,
id_map=_TEST_ID_MAP,
terminal_map=_TEST_TERMINAL_MAP,
)
push(simple_tree)
return "".join(result)
with Fast_Parser(verbose=False) as parser:
# The cast(XformFunc, xform) type annotation is a hack
pgs, stats, _ = TreeUtility._process_text(
parser, session, text, all_names=False, xform=cast(XformFunc, xform)
)
# pgs is a list of paragraphs, each being a list of sentences
# To access the first parsed sentence, use pgs[0][0]
return (pgs, stats)
def raw_tag_toklist(session, toklist, root=None):
""" Parse plain text and return the parsed paragraphs as lists of sentences
where each sentence is a list of tagged tokens. The result does not
include a name register. """
def xform(tokens, tree, err_index):
""" Transformation function that simply returns a list of POS-tagged,
normalized tokens for the sentence """
return TreeUtility.dump_tokens(tokens, tree, error_index=err_index)
with Fast_Parser(verbose=False, root=root) as parser:
return TreeUtility._process_toklist(parser, session, toklist,
xform)
def _parse(toklist: Iterable[Tok]) -> Tuple[ResponseDict, Dict[int, str]]:
""" Parse a token list as a query """
bp = Query._parser
assert bp is not None
num_sent = 0
num_parsed_sent = 0
rdc = Reducer(bp.grammar)
trees: Dict[int, str] = dict()
sent: List[Tok] = []
for t in toklist:
if t[0] == TOK.S_BEGIN:
if num_sent > 0:
# A second sentence is beginning: this is not valid for a query
raise ParseError(
"A query cannot contain more than one sentence")
sent = []
elif t[0] == TOK.S_END:
slen = len(sent)
if not slen:
continue
num_sent += 1
# Parse the accumulated sentence
num = 0
try:
# Parse the sentence
forest = bp.go(sent)
if forest is not None:
num = Fast_Parser.num_combinations(forest)
if num > 1:
# Reduce the resulting forest
forest = rdc.go(forest)
except ParseError:
forest = None
num = 0
if num > 0:
num_parsed_sent += 1
# Obtain a text representation of the parse tree
assert forest is not None
trees[num_sent] = ParseForestDumper.dump_forest(forest)
elif t[0] == TOK.P_BEGIN:
pass
elif t[0] == TOK.P_END:
pass
else:
sent.append(t)
result: ResponseDict = dict(num_sent=num_sent,
num_parsed_sent=num_parsed_sent)
return result, trees
def parse_text(session, text, all_names=False):
""" Parse plain text and return the parsed paragraphs as simplified trees """
def xform(tokens, tree, err_index):
""" Transformation function that yields a simplified parse tree
with POS-tagged, normalized terminal leaves for the sentence """
if err_index is not None:
return TreeUtility.dump_tokens(tokens, tree, None, err_index)
# Successfully parsed: return a simplified tree for the sentence
return TreeUtility._simplify_tree(tokens, tree)
with Fast_Parser(
verbose=False) as parser: # Don't emit diagnostic messages
return TreeUtility._process_text(parser, session, text, all_names,
xform)
def _parse(toklist):
""" Parse a token list as a query """
# Parse with the nonterminal 'QueryRoot' as the grammar root
with Fast_Parser(verbose=False, root=_QUERY_ROOT) as bp:
sent_begin = 0
num_sent = 0
num_parsed_sent = 0
rdc = Reducer(bp.grammar)
trees = dict()
sent = []
for ix, t in enumerate(toklist):
if t[0] == TOK.S_BEGIN:
sent = []
sent_begin = ix
elif t[0] == TOK.S_END:
slen = len(sent)
if not slen:
continue
num_sent += 1
# Parse the accumulated sentence
num = 0
try:
# Parse the sentence
forest = bp.go(sent)
if forest is not None:
num = Fast_Parser.num_combinations(forest)
if num > 1:
# Reduce the resulting forest
forest = rdc.go(forest)
except ParseError as e:
forest = None
if num > 0:
num_parsed_sent += 1
# Obtain a text representation of the parse tree
trees[num_sent] = ParseForestDumper.dump_forest(forest)
# ParseForestPrinter.print_forest(forest)
elif t[0] == TOK.P_BEGIN:
pass
elif t[0] == TOK.P_END:
pass
else:
sent.append(t)
result = dict(num_sent=num_sent, num_parsed_sent=num_parsed_sent)
return result, trees
def tag_toklist(session, toklist, all_names=False):
""" Parse plain text and return the parsed paragraphs as lists of sentences
where each sentence is a list of tagged tokens """
def xform(tokens, tree, err_index):
""" Transformation function that simply returns a list of POS-tagged,
normalized tokens for the sentence """
return TreeUtility.dump_tokens(tokens, tree, error_index=err_index)
with Fast_Parser(
verbose=False) as parser: # Don't emit diagnostic messages
pgs, stats = TreeUtility._process_toklist(parser, session, toklist,
xform)
from queries.builtin import create_name_register
register = create_name_register(toklist, session, all_names=all_names)
return pgs, stats, register
def raw_tag_toklist(
toklist: Iterable[Tok], root: Optional[str] = None
) -> Tuple[PgsList, StatsDict]:
""" Parse plain text and return the parsed paragraphs as lists of sentences
where each sentence is a list of tagged tokens. The result does not
include a name register. """
def xform(
tokens: List[Tok], tree: Optional[Node], err_index: Optional[int]
) -> List[TokenDict]:
""" Transformation function that simply returns a list of POS-tagged,
normalized tokens for the sentence """
return TreeUtility.dump_tokens(tokens, tree, error_index=err_index)
with Fast_Parser(verbose=False, root=root) as parser:
return TreeUtility._process_toklist(parser, toklist, xform)
def parse(self):
""" Parse the sentence """
num = 0
score = 0
try:
forest = self._ip._parser.go(self._s)
if forest is not None:
num = Fast_Parser.num_combinations(forest)
if num > 1:
forest, score = self._ip._reducer.go_with_score(forest)
except ParseError as e:
forest = None
self._err_index = e.token_index
self._tree = forest
self._score = score
self._ip._add_sentence(self, num)
return num > 0
def _parse(toklist):
""" Parse a token list as a query """
bp = Query._parser
num_sent = 0
num_parsed_sent = 0
rdc = Reducer(bp.grammar)
trees = dict()
sent = []
for t in toklist:
if t[0] == TOK.S_BEGIN:
sent = []
elif t[0] == TOK.S_END:
slen = len(sent)
if not slen:
continue
num_sent += 1
# Parse the accumulated sentence
num = 0
try:
# Parse the sentence
forest = bp.go(sent)
if forest is not None:
num = Fast_Parser.num_combinations(forest)
if num > 1:
# Reduce the resulting forest
forest = rdc.go(forest)
except ParseError:
forest = None
if num > 0:
num_parsed_sent += 1
# Obtain a text representation of the parse tree
trees[num_sent] = ParseForestDumper.dump_forest(forest)
elif t[0] == TOK.P_BEGIN:
pass
elif t[0] == TOK.P_END:
pass
else:
sent.append(t)
result = dict(num_sent=num_sent, num_parsed_sent=num_parsed_sent)
return result, trees
def parse_text(
session: Session, text: str, all_names: bool = False
) -> Tuple[PgsList, StatsDict, Optional["RegisterType"]]:
""" Parse plain text and return the parsed paragraphs as simplified trees """
def xform(
tokens: List[Tok], tree: Optional[Node], err_index: Optional[int]
) -> Union[None, List[TokenDict], CanonicalTokenDict]:
""" Transformation function that yields a simplified parse tree
with POS-tagged, normalized terminal leaves for the sentence """
if err_index is not None:
return TreeUtility.dump_tokens(tokens, tree, error_index=err_index)
# Successfully parsed: return a simplified tree for the sentence
return TreeUtility._simplify_tree(tokens, tree)
with Fast_Parser(verbose=False) as parser: # Don't emit diagnostic messages
# The type annotation cast(XformFunc, xform) is a hack
return TreeUtility._process_text(
parser, session, text, all_names, cast(XformFunc, xform)
)
def parse_text_with_full_tree(
session: Session, text: str, all_names: bool = False
) -> Tuple[Optional[List[TokenDict]], Optional[Node], StatsDict]:
""" Parse plain text, assumed to contain one sentence only, and
return its simplified form as well as its full form. """
full_tree: Optional[Node] = None
def xform(
tokens: List[Tok], tree: Optional[Node], err_index: Optional[int]
) -> Union[None, List[TokenDict], CanonicalTokenDict]:
""" Transformation function that yields a simplified parse tree
with POS-tagged, normalized terminal leaves for the sentence """
if err_index is not None:
return TreeUtility.dump_tokens(tokens, tree, error_index=err_index)
# Successfully parsed: return a simplified tree for the sentence
nonlocal full_tree
# We are assuming that there is only one parsed sentence
if full_tree is None:
# Note the full tree of the first parsed paragraph
full_tree = tree
return TreeUtility._simplify_tree(tokens, tree)
with Fast_Parser(verbose=False) as parser:
# The cast(XformFunction, xform) type annotation is a hack
pgs, stats, _ = TreeUtility._process_text(
parser, session, text, all_names, cast(XformFunc, xform)
)
if (
not pgs
or stats["num_parsed"] == 0
or not pgs[0]
or any("err" in t for t in pgs[0][0])
):
# The first sentence didn't parse: let's not beat around the bush with that fact
return (None, None, stats)
# Return the simplified tree, full tree and stats
assert full_tree is not None
return (pgs[0][0], full_tree, stats)
def _make_tree(text: str) -> Tree:
"""Tokenize and parse text, create tree representation string
from all the parse trees, return Tree object and token JSON."""
toklist = tokenize(text)
fp = Fast_Parser(verbose=False)
ip = IncrementalParser(fp, toklist, verbose=False)
pgs = []
# Dict of parse trees in string dump format,
# stored by sentence index (1-based)
trees = OrderedDict()
num_sent = 0
for p in ip.paragraphs():
pgs.append([])
for sent in p.sentences():
num_sent += 1
num_tokens = len(sent)
assert sent.parse(), "Sentence does not parse: " + sent.text
# Obtain a text representation of the parse tree
token_dicts = TreeUtility.dump_tokens(sent.tokens, sent.tree)
# Create a verbose text representation of
# the highest scoring parse tree
assert sent.tree is not None
tree = ParseForestDumper.dump_forest(sent.tree, token_dicts=token_dicts)
# Add information about the sentence tree's score
# and the number of tokens
trees[num_sent] = "\n".join(
["C{0}".format(sent.score), "L{0}".format(num_tokens), tree]
)
pgs[-1].append(token_dicts)
# Create a tree representation string out of
# all the accumulated parse trees
tree_string = "".join("S{0}\n{1}\n".format(key, val) for key, val in trees.items())
tokens_json = json.dumps(pgs, separators=(",", ":"), ensure_ascii=False)
tree = Tree()
tree.load(tree_string)
return tree, tokens_json
else:
raise
finally:
ArticleProxy.cleanup()
BIN_Db.cleanup()
else:
app.config["PRODUCTION"] = True
# Suppress information log messages from Werkzeug
werkzeug_log = logging.getLogger("werkzeug")
if werkzeug_log:
werkzeug_log.setLevel(logging.WARNING)
# Log our startup
log_str = ("Greynir instance starting with "
"host={0}:{1}, db_host={2}:{3} on Python {4}".format(
Settings.HOST,
Settings.PORT,
Settings.DB_HOSTNAME,
Settings.DB_PORT,
sys.version.replace("\n", " "),
))
logging.info(log_str)
print(log_str)
sys.stdout.flush()
# Running as a server module: pre-load the grammar into memory
with Fast_Parser() as fp:
pass
def test_entities():
text = """
Ég skipti við flugfélagið AirBerlin áður en það varð gjaldþrota.
Danska byggingavörukeðjan Bygma hefur keypt íslenska
verslunarfyrirtækið Húsasmiðjuna.
Bandarísku fjárfestingarsjóðirnir Attestor Capital og Goldman Sachs
eru hluthafar í Arion banka.
Fosshótel, stór hótelkeðja, var rekin með tapi í fyrra.
Lax, stór fiskur af ætt laxfiska, er veiddur í íslenskum ám.
Silfraður lax, fiskur af ætt laxfiska, er veiddur í íslenskum ám.
Ég ræddi við fulltrúa Norðuráls (álverksmiðjunnar í Hvalfirði) í gær.
Ég ræddi við fulltrúa Norðuráls (í Hvalfirði) í gær.
Primera Air var íslenskt flugfélag.
Ef veðrið er gott þá fullyrði ég að Primera Air sé danskt flugfélag.
Villeneuve-Loubet er franskt þorp.
Það er hægt að fá bragðgóðan ís í ísbúðinni Valdísi úti á Granda.
Í miðbæ Reykjavíkur er herrafataverslunin Geysir.
Mér er sagt að Geysir sé hættur að gjósa.
Geysir er hættur að gjósa.
Geysir er gamall goshver.
Fyrirtækið Apple-búðin selur Apple Mac tölvur.
Fyrirtækið Origo selur IBM tölvur.
Íslendingar stofnuðu skipafélagið Eimskipafélag Íslands hf.
"""
toklist = tokenize(text)
fp = Fast_Parser(verbose=False)
ip = IncrementalParser(fp, toklist, verbose=False)
# Dict of parse trees in string dump format,
# stored by sentence index (1-based)
trees = OrderedDict()
num_sent = 0
for p in ip.paragraphs():
for sent in p.sentences():
num_sent += 1
num_tokens = len(sent)
assert sent.parse(), "Sentence does not parse"
# Obtain a text representation of the parse tree
token_dicts = TreeUtility.dump_tokens(sent.tokens, sent.tree)
# Create a verbose text representation of
# the highest scoring parse tree
tree = ParseForestDumper.dump_forest(sent.tree,
token_dicts=token_dicts)
# Add information about the sentence tree's score
# and the number of tokens
trees[num_sent] = "\n".join(
["C{0}".format(sent.score), "L{0}".format(num_tokens), tree])
# Create a tree representation string out of
# all the accumulated parse trees
tree_string = "".join("S{0}\n{1}\n".format(key, val)
for key, val in trees.items())
tree = Tree()
tree.load(tree_string)
session = SessionShim()
tree.process(session, entities)
session.check(("Bygma", "er", "dönsk byggingavörukeðja"))
session.check(("Húsasmiðjan", "er", "íslenskt verslunarfyrirtæki"))
session.check(("Goldman Sachs", "er", "bandarískur fjárfestingarsjóður"))
session.check(
("Attestor Capital", "er", "bandarískur fjárfestingarsjóður"))
session.check(("Primera Air", "var", "íslenskt flugfélag"))
session.check(("Villeneuve-Loubet", "er", "franskt þorp"))
session.check(("Valdís", "er", "ísbúð"))
session.check(("Fosshótel", "var", "rekin með tapi"))
session.check(("Fosshótel", "er", "stór hótelkeðja"))
session.check(("Norðurál", "er", "álverksmiðjan í Hvalfirði"))
session.check(("Lax", "er", "stór fiskur af ætt laxfiska"))
session.check(("Geysir", "er", "gamall goshver"))
session.check(("Eimskipafélag Íslands hf", "er", "skipafélag"))
session.check(("Origo", "er", "fyrirtæki"))
session.check(("AirBerlin", "er", "flugfélag"))
assert session.is_empty()
def _init_class(cls):
""" Initialize class attributes """
if cls._parser is None:
cls._parser = Fast_Parser(
verbose=False) # Don't emit diagnostic messages
