Bootstrapping Relationship Extractors with Distributional Semantics
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README.md

Bootstrapping Relationship Extraction with Distributional Semantics

BREDS is a bootstrapping system for relationship extraction relying on word vector representations (i.e., word embeddings). For more details please refer to:

Architecture: system description

Poster presented at EMNLP'15

Dependencies

You need to have Python 2.7 and the following libraries installed:

Numpy: http://www.numpy.org/

NLTK: http://www.nltk.org/

Gensim: https://radimrehurek.com/gensim/

which you can install issuing the following command:

pip install -r requirements.txt

Usage:

python BREDS.py parameters sentences positive_seeds negative_seeds similarity confidence

parameters

A sample configuration is provided in parameters.cfg. The file contains values for differentes parameters:

max_tokens_away=6           # maximum number of tokens between the two entities
min_tokens_away=1           # maximum number of tokens between the two entities
context_window_size=2       # number of tokens to the left and right

wUpdt=0.5                   # < 0.5 trusts new examples less on each iteration
number_iterations=4         # number of bootstrap iterations
wUnk=0.1                    # weight given to unknown extracted relationship instances
wNeg=2                      # weight given to extracted relationship instances
min_pattern_support=2       # minimum number of instances in a cluster to be considered a pattern

word2vec_path=vectors.bin   # path to a word2vecmodel in binary format

alpha=0.2                   # weight of the BEF context in the similarity function
beta=0.6                    # weight of the BET context in the similarity function
gamma=0.2                   # weight of the AFT context in the similarity function

sentences

A sample configuration is provided in sentences.txt, a text file containing sentences, one per line, with tags identifing the named type of named-entities, e.g.:

The tech company <ORG>Soundcloud</ORG> is based in <LOC>Berlin</LOC>, capital of Germany.
<ORG>Pfizer</ORG> says it has hired <ORG>Morgan Stanley</ORG> to conduct the review.
<ORG>Allianz</ORG>, based in <LOC>Munich</LOC>, said net income rose to EUR 1.32 billion.
<ORG>Pfizer</ORG>, based in <LOC>New York City</LOC> , employs about 90,000 workers.

positive_seeds

A file with examples of the relationships to be bootstrapped. The file must also specify the semantic type of the entities in the relationships. The first two lines specify that first entity in the relationship is of type ORG and that the second is of type LOC. Then a seed relationship is specified per line, e.g.:

e1:ORG
e2:LOC

Nokia;Espoo
Pfizer;New York
Google;Mountain View
Microsoft;Redmond

negative_seeds

The same thing as for positive relationships, but containing seeds that do not represent the relationships to be bootstrapped.

similarity

The threshold similarity real value [0,1] for clustering/extracting instances, e.g.:

0.6

confidance_threshold

The confidence threshold real value [0,1] for an instance to be used as seed, e.g.:

0.8

Demo

You need to specify a word2vec model in the parameters.cfg file, the model used in my experiments is available for download. It was generated from the sub collections of the English Gigaword Collection, namely the AFP, APW and XIN. The model is available here: afp_apw_xin_embeddings.bin

A sample file containing sentences where the named-entities are already tagged, which has 1 million sentences taken from the New York Times articles part of the English Gigaword Collection, is available here: sentences.txt

The golden standard used for evaluation is available here: relationships_gold.zip

To extract the locations/headquarters of companies from sentences.txt based on the seeds examples given in seeds_positive, run the following command:

python BREDS.py parameters.cfg sentences.txt seeds_positive.txt seeds_negative.txt 0.7 0.7

In the first step BREDS pre-processes the sentences.txt file, generating word vector representations of relationships (i.e.: processed_tuples.pkl). This is done so that then you can experiment with different seed examples without having to repeat the process of generating word vectors representations. Just use processed_tuples.pklas the second argument to BREDS.py instead of sentences.txt.

Running the whole bootstrapp process, depending on your hardware, sentences input size and number of iterations, can take very long time (i.e., a few hours). You can reduce the size of sentences.txt file, or you can also use a multi-core version of BREDS. In the multi-core version finding seed matchs and clustering them is done in parallel, levering multi-core architectures. You must specifiy at the end how many cores you want to use:

python BREDS-parallel.py parameters.cfg sentences.txt seeds_positive.txt seeds_negative.txt 0.7 0.7 #cpus

The output should be in a relationships.txt file. The file contains a list of the relationships extracted, containing the confidence score, the sentence where the relationship was found, the patterns that extracted the relationship and wether the passive voice is present in the relationship, e.g.:

instance: DynCorp       Reston  score:0.998
sentence: Because <ORG>DynCorp</ORG> , headquartered in <LOC>Reston</LOC> , <LOC>Va.</LOC> , gets 98 percent of its revenue from government work .
pattern_bef: Because
pattern_bet: , headquartered in
pattern_aft: , Va.
passive voice: False

instance: Handspring    Silicon Valley  score:0.893
sentence: There will be more firms like <ORG>Handspring</ORG> , a company based in <LOC>Silicon Valley</LOC> that looks as if it is about to become a force in handheld computers , despite its lack of machinery .
pattern_bef: firms like
pattern_bet: , a company based in
pattern_aft: that looks
passive voice: False