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Added Parameter to NEL to take n sentences into account (#5548)

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* added setting for neighbour sentence in NEL

* added spaCy contributor agreement

* added multi sentence also for training

* made the try-except block smaller
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theudas 2020-06-12 02:03:23 +02:00 committed by Adriane Boyd
parent 4724fa4cf4
commit 3f5e2f9d99
2 changed files with 189 additions and 48 deletions
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106
.github/contributors/theudas.md vendored Normal file
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@ -0,0 +1,106 @@
# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | ------------------------ |
| Name | Philipp Sodmann |
| Company name (if applicable) | Empolis |
| Title or role (if applicable) | |
| Date | 2017-05-06 |
| GitHub username | theudas |
| Website (optional) | |

131
spacy/pipeline/pipes.pyx
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@ -1170,6 +1170,9 @@ class EntityLinker(Pipe):
self.model = True self.model = True
self.kb = None self.kb = None
self.cfg = dict(cfg) self.cfg = dict(cfg)
# how many neightbour sentences to take into account
self.n_sents = cfg.get("n_sents", 0)
def set_kb(self, kb): def set_kb(self, kb):
self.kb = kb self.kb = kb
@ -1218,6 +1221,9 @@ class EntityLinker(Pipe):
for doc, gold in zip(docs, golds): for doc, gold in zip(docs, golds):
ents_by_offset = dict() ents_by_offset = dict()
sentences = [s for s in doc.sents]
for ent in doc.ents: for ent in doc.ents:
ents_by_offset[(ent.start_char, ent.end_char)] = ent ents_by_offset[(ent.start_char, ent.end_char)] = ent
@ -1228,17 +1234,34 @@ class EntityLinker(Pipe):
# the gold annotations should link to proper entities - if this fails, the dataset is likely corrupt # the gold annotations should link to proper entities - if this fails, the dataset is likely corrupt
if not (start, end) in ents_by_offset: if not (start, end) in ents_by_offset:
raise RuntimeError(Errors.E188) raise RuntimeError(Errors.E188)
ent = ents_by_offset[(start, end)] ent = ents_by_offset[(start, end)]
for kb_id, value in kb_dict.items(): for kb_id, value in kb_dict.items():
# Currently only training on the positive instances # Currently only training on the positive instances
if value: if value:
try: try:
sentence_docs.append(ent.sent.as_doc()) # find the sentence in the list of sentences.
sent_index = sentences.index(ent.sent)
except AttributeError: except AttributeError:
# Catch the exception when ent.sent is None and provide a user-friendly warning # Catch the exception when ent.sent is None and provide a user-friendly warning
raise RuntimeError(Errors.E030) raise RuntimeError(Errors.E030)
# get n previous sentences, if there are any
start_sentence = max(0, sent_index - self.n_sents)
# get n posterior sentences, or as many < n as there are
end_sentence = min(len(sentences) -1, sent_index + self.n_sents)
# get token positions
start_token = sentences[start_sentence].start
end_token = sentences[end_sentence].end
# append that span as a doc to training
sent_doc = doc[start_token:end_token].as_doc()
sentence_docs.append(sent_doc)
sentence_encodings, bp_context = self.model.begin_update(sentence_docs, drop=drop) sentence_encodings, bp_context = self.model.begin_update(sentence_docs, drop=drop)
loss, d_scores = self.get_similarity_loss(scores=sentence_encodings, golds=golds, docs=None) loss, d_scores = self.get_similarity_loss(scores=sentence_encodings, golds=golds, docs=None)
bp_context(d_scores, sgd=sgd) bp_context(d_scores, sgd=sgd)
@ -1309,69 +1332,81 @@ class EntityLinker(Pipe):
if isinstance(docs, Doc): if isinstance(docs, Doc):
docs = [docs] docs = [docs]
for i, doc in enumerate(docs): for i, doc in enumerate(docs):
sentences = [s for s in doc.sents]
if len(doc) > 0: if len(doc) > 0:
# Looping through each sentence and each entity # Looping through each sentence and each entity
# This may go wrong if there are entities across sentences - which shouldn't happen normally. # This may go wrong if there are entities across sentences - which shouldn't happen normally.
for sent in doc.sents: for sent_index, sent in enumerate(sentences):
sent_doc = sent.as_doc() if sent.ents:
# currently, the context is the same for each entity in a sentence (should be refined) # get n_neightbour sentences, clipped to the length of the document
sentence_encoding = self.model([sent_doc])[0] start_sentence = max(0, sent_index - self.n_sents)
xp = get_array_module(sentence_encoding) end_sentence = min(len(sentences) -1, sent_index + self.n_sents)
sentence_encoding_t = sentence_encoding.T
sentence_norm = xp.linalg.norm(sentence_encoding_t)
for ent in sent_doc.ents: start_token = sentences[start_sentence].start
entity_count += 1 end_token = sentences[end_sentence].end
to_discard = self.cfg.get("labels_discard", []) sent_doc = doc[start_token:end_token].as_doc()
if to_discard and ent.label_ in to_discard:
# ignoring this entity - setting to NIL
final_kb_ids.append(self.NIL)
final_tensors.append(sentence_encoding)
else: # currently, the context is the same for each entity in a sentence (should be refined)
candidates = self.kb.get_candidates(ent.text) sentence_encoding = self.model([sent_doc])[0]
if not candidates: xp = get_array_module(sentence_encoding)
# no prediction possible for this entity - setting to NIL sentence_encoding_t = sentence_encoding.T
sentence_norm = xp.linalg.norm(sentence_encoding_t)
for ent in sent.ents:
entity_count += 1
to_discard = self.cfg.get("labels_discard", [])
if to_discard and ent.label_ in to_discard:
# ignoring this entity - setting to NIL
final_kb_ids.append(self.NIL) final_kb_ids.append(self.NIL)
final_tensors.append(sentence_encoding) final_tensors.append(sentence_encoding)
elif len(candidates) == 1:
# shortcut for efficiency reasons: take the 1 candidate
# TODO: thresholding
final_kb_ids.append(candidates[0].entity_)
final_tensors.append(sentence_encoding)
else: else:
random.shuffle(candidates) candidates = self.kb.get_candidates(ent.text)
if not candidates:
# no prediction possible for this entity - setting to NIL
final_kb_ids.append(self.NIL)
final_tensors.append(sentence_encoding)
# this will set all prior probabilities to 0 if they should be excluded from the model elif len(candidates) == 1:
prior_probs = xp.asarray([c.prior_prob for c in candidates]) # shortcut for efficiency reasons: take the 1 candidate
if not self.cfg.get("incl_prior", True):
prior_probs = xp.asarray([0.0 for c in candidates])
scores = prior_probs
# add in similarity from the context # TODO: thresholding
if self.cfg.get("incl_context", True): final_kb_ids.append(candidates[0].entity_)
entity_encodings = xp.asarray([c.entity_vector for c in candidates]) final_tensors.append(sentence_encoding)
entity_norm = xp.linalg.norm(entity_encodings, axis=1)
if len(entity_encodings) != len(prior_probs): else:
raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length")) random.shuffle(candidates)
# cosine similarity # this will set all prior probabilities to 0 if they should be excluded from the model
sims = xp.dot(entity_encodings, sentence_encoding_t) / (sentence_norm * entity_norm) prior_probs = xp.asarray([c.prior_prob for c in candidates])
if sims.shape != prior_probs.shape: if not self.cfg.get("incl_prior", True):
raise ValueError(Errors.E161) prior_probs = xp.asarray([0.0 for c in candidates])
scores = prior_probs + sims - (prior_probs*sims) scores = prior_probs
# TODO: thresholding # add in similarity from the context
best_index = scores.argmax() if self.cfg.get("incl_context", True):
best_candidate = candidates[best_index] entity_encodings = xp.asarray([c.entity_vector for c in candidates])
final_kb_ids.append(best_candidate.entity_) entity_norm = xp.linalg.norm(entity_encodings, axis=1)
final_tensors.append(sentence_encoding)
if len(entity_encodings) != len(prior_probs):
raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length"))
# cosine similarity
sims = xp.dot(entity_encodings, sentence_encoding_t) / (sentence_norm * entity_norm)
if sims.shape != prior_probs.shape:
raise ValueError(Errors.E161)
scores = prior_probs + sims - (prior_probs*sims)
# TODO: thresholding
best_index = scores.argmax()
best_candidate = candidates[best_index]
final_kb_ids.append(best_candidate.entity_)
final_tensors.append(sentence_encoding)
if not (len(final_tensors) == len(final_kb_ids) == entity_count): if not (len(final_tensors) == len(final_kb_ids) == entity_count):
raise RuntimeError(Errors.E147.format(method="predict", msg="result variables not of equal length")) raise RuntimeError(Errors.E147.format(method="predict", msg="result variables not of equal length"))