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Word Embeddings for Information Retrieval
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README.md

Vec4IR

Word embeddings for information retrieval.

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Quick start

Visit the test file for a rough but quick introduction to the framework.

For a comparison between the methods available in vec4ir, we refer to our paper Word Embeddings for Practical Information Retrieval (Author Copy).

User’s guide

Detailed information retrieval pipeline as considered in the vec4ir framework. With the query node as starting point, each out-going edge is a configurable option of the native evaluation script. Rectangles resemble algorithms, and folder-like shapes resemble data stored on disk. One example configuration is highlighted by bold edges.

Philosophy

The information retrieval framework vec4ir has the goal is simulate a practical information retrieval setting. In order to encourage research in this field, the focus lies on extensibility. Adding a new retrieval model for evaluation should be as easy as possible. The target audience are researchers evaluating their own retrieval models and curious data scientists, who want to evaluate which retrieval model fits their data best.

Terminology

In the following, We recapture the most important definitions that are relevant to the framework.

Information Retrieval (IR)

The information retrieval task can be defined as follows:

Given a corpus of documents D and a query q, return {dDd relevant to q} in rank order.

A practical information retrieval system typically consists of at least the two components: matching, similarity scoring. Several other components can also be considered, such as query expansion, pseudo-relevance feedback, query parsing and the analysis process itself.

Matching

The matching operation refers to the initial filtering process of documents. For instance, the output of the matching operation contains all the document which contain at least one of the query terms. Other variants of the matching operation may also allow fuzzy matching (up to a certain threshold in Levenshtein distance) or enforce exact match of a phrase.

Similarity scoring

The scoring step refers to the process of assigning scores. The scores resemble the relevance of a specific document to the query. They are used to create a ranked ordering of the matched documents. This sorting happens either ascending, when considering distance scores, or descending in case of similarity scores.

Word embeddings and Word2Vec

A word embedding is a distributed (dense) vector representation for each word of a vocabulary. It is capable of capturing semantic and syntactic properties of the input texts . Interestingly, even arithmetic operations on the word vectors are meaningful: e.g. King - Queen = Man - Woman. The two most popular approaches to learn a word embedding from raw text are:

  • Skip-Gram Negative Sampling by Mikolov et al. (2013) (Word2Vec)
  • Global Word Vectors by Pennington, Socher, and Manning (2014) (GloVe)

Skip-gram negative sampling (or Word2Vec) is an algorithm based on a shallow neural network which aims to learn a word embedding. It is highly efficient, as it avoids dense matrix multiplication and does not require the full term co-occurrence matrix. Given some target word wt, the intermediate goal is to train the neural network to predict the words in the c-neighbourhood of wt: wt − c, …, wt − 1, wt + 1, …, wt + c. First, the word is directly associated to its respective vector, which as used as input for a (multinomial) logistic regression to predict the words in the c-neighbourhood. Then, the weights for the logistic regression are adjusted, as well as the vector itself (by back-propagation). The Word2Vec algorithm employs negative sampling: additional k noise words which do not appear in the c-neighbourhood are introduced as possible outputs, for which the desired output is known to be false. Thus, the model does not reduce the weights to all other vocabulary words but only to those sampled k noise words. When these noise words appear in a similar context as wt, the model gets more and more fine-grained over the training epochs. In contrast to Word2Vec, the GloVe (Pennington, Socher, and Manning 2014) algorithm computes the whole term co-occurrence matrix for a given corpus. To obtain word vectors, the term co-occurrence matrix is factorised. The training objective is that the euclidean dot product of each two word vectors match the log-probability of their words’ co-occurrence.

Word centroid similarity (WCS)

An intuitive approach to use word embeddings in information retrieval is the word centroid similarity (WCS). The representation for each document is the centroid of its respective word vectors. Since word vectors carry semantic information of the words, one could assume that the centroid of the word vectors within a document encodes its meaning to some extent. At query time, the centroid of the query’s word vectors is computed. The cosine similarity to the centroids of the (matching) documents is used as a measure of relevance. When the initial word frequencies of the queries and documents are first re-weighted according to inverse-document frequency (i.e. frequent words in the corpus are discounted), the technique is labeled IDF re-weighted word centroid similarity (IWCS).

Features

The key features of this information retrieval framework are:

  • Simulation of a practical IR setting
  • Native word embedding support in conjunction with gensim (Řehuřek and Sojka 2010)
  • Built-in evaluation
  • API design inspired by sklearn (Buitinck et al. 2013) .
  • Extendable by new retrieval models

Dependencies

Besides python3 itself, the package vec4ir depends on the following python packages.

Installation

As vec4ir is packaged as a python package, it can be installed by via pip:

We provide a helper script to setup a new virtual environment and install all dependencies. It can be invoked via bash requirements.sh. For using the installed package later, you have to activate the virtual created environment via source venv/bin/activate.

If a new virtual environment is not needed, you can install vec4ir manually:

pip install -e .

In case anything went wrong with the installation of dependencies, try to install them manually: We also recommend to install numpy and scipy in beforehand (either manually, or as binary system packages).

pip install -r requirements.txt

WordMoversDistance and pyemd

To compute the Word Mover's distance, the pyemd package is required. It can be installed via pip install pyemd. However, the python3 developer system package (such as python3-devel on Solus) may be required for the installation of pyemd to succeed. This step is only necessary if you want to use the Word Mover's distance.

The evaluation script

The package includes a native command line script vec4ir-evaluate for evaluation of an information retrieval pipeline. The pipeline of query expansion, matching and scoring is applied to a set of queries and the metrics mean average precision (MAP), mean reciprocal rank (MRR), normalised discounted cumulative gain (NDCG), precision and recall are computed. Hence, the script may be used as-is for evaluation of your datasets or as a reference implementation for the usage of the framework. The behaviour of the evaluation script and the resulting information retrieval pipeline can be controlled by the following command-line arguments:

Meta options

As a meta option (affecting other options), we allow the specification of a configuration file.

  • -c, --config Path to a configuration file, in which the file paths for the datasets and embedding models are specified. The default value is config.yml.

General options

  • -d, --dataset The data set to operate on. (mandatory)
  • -e, --embedding The word embedding model to use. (mandatory)
  • -r, --retrieval-model The retrieval model for similarity scoring. One of tfidf (default), wcd, wmd, d2v.
  • -q, --query-expansion The expansion technique to apply on the queries. One of wcd, eqe1, eqe2.

The arguments --dataset and --embedding expect the provided keys to be present in the configuration file specified by --config. While the key for the embedding should be below embeddings in the configuration file, the key for the dataset should be below data. An minimal example structure would be:

embeddings:
    word2vec: # possible value for --embedding
        path: "/path/to/GoogleNews-vectors-negative300.bin.gz"
data:
    short-ntcir2: # possible value for --dataset
        type: "ntcir"
        root_path: "path/to/data/NTCIR2/"
        field: "title"
        topic: "title"
        rels: 2

The options below the respective keys specify the responsible constructor (type) and its arguments (root_path, field, …). More details on the natively supported dataset formats can be found in the developer’s guide. The alternatives for the retrieval model (--retrieval-model) are described in more detail below.

Embedding options

  • -n, --normalize Normalise the embedding model’s word vectors.
  • -a, --all-but-the-top All-but-the-top embedding post-processing as proposed by Mu, Bhat, and Viswanath (2017) .
  • -t, --train Number of epochs used for up-training out-of-vocabulary words.

In the special case of --train 0 the behaviour of the script is not equivalent to omitting this parameter. After building a vocabulary from the input documents, the word vectors are intersected with the word vectors of the embedding. More specific, missing vocabulary words are initialised with close-to-zero random vectors. Instead, out-of-vocabulary words are ignored, when the --train parameter is omitted.

Retrieval options

  • -I, --no-idf Do not use IDF re-weighted word frequencies for aggregation of word vectors.
  • -w, --wmd Fraction of additional documents to take into account for wmd retrieval model.

By default, vec4ir-evaluate uses IDF re-weighted word centroid similarity if -r wcd is provided. If IDF re-weighting is not desired, it is necessary to provide the --no-idf argument. The --wmd argument refers to the fraction of additional documents to consider, when the Word Mover’s distance (-r wmd) was chosen as retrieval model. For example, consider --wmd 0.1 and 120 matching documents for a specific query. When 20 documents should be retrieved, the word centroid similarity is consulted to retrieve the top 20 + 0.1 ⋅ (120 − 20)=30 documents. These 30 most relevant (with respect to word centroid similarity) documents are then be re-ranked according to the Word Mover’s distance. A --wmd value of zero corresponds to re-ranking the top k documents by Word Mover’s distance, while the highest possible value of 1.0 corresponds to computing the full Word Mover’s distance without taking the WCS into account.

Output options

The output options control the online and persistent output of the evaluation script. The special option --stats can be provided to compute the ratio of out-of-vocabulary tokens, print the top 50 most frequent out-of-vocabulary tokens and exit.

  • -o, --output File path for writing the output.
  • -v, --verbose Verbosity level.
  • -s, --stats Compute out-of-vocabulary statistics and exit.

Evaluation options

The following arguments affect the evaluation process:

  • -k Number of documents to retrieve (defaults to 20).
  • -Q, --filter-queries Drop queries, which contain out-of-vocabulary words.
  • -R, --replacement Treatment for missing relevance information in the gold standard. Chose drop to disregard them (default) or zero to treat them as non-relevant.

In most cases, the defaults of not filtering the queries and replacing missing values by zeroes (indicating non-relevance) are appropriate.

Analysis options

The analysis process of splitting a string into tokens can be customised by the following arguments:

  • -M, --no-matching Do not conduct a matching operation.
  • -T, --tokenizer The tokeniser for the matching operation. One of sklearn, sword, nltk.
  • -S, --dont-stop Do not remove English stop-words.
  • -C, --cased Conduct a case sensitive analysis.

Please note that the analysis process, also directly affects the matching operation. The defaults are to conduct a matching operation, tokenise according to sklearn tokeniser (F. Pedregosa et al. 2011) (a token consists of at least two subsequent word-characters), remove English stop-words and transform all characters to lower case. The sword tokeniser additionally considers single-character words as token. The nltk tokeniser retains all punctuation punctuation as tokens (Bird 2006).

Basic framework usage

We provide a minimal example including the matching operation and the TF-IDF retrieval model. As an example, assume we have two documents fox valley and dog nest and two queries fox and dog. First, we create an instance of the Matching class, whose optional arguments are passed directly to sklearn’s CountVectorizer.

>>> match_op = Matching()

In its default form, the matching is a non-fuzzy boolean OR matching. The Matching instance can be used after fitting a set of documents via match_op.fit(documents) using its match_op.predict(query_string) method to return the indices of the matching documents. However, the preferred way to apply the matching operation is inside of a Retrieval instance. The Retrieval instance expects a retrieval model as mandatory argument, besides an optional Matching instance (and an optional query expansion instance). For now, we create an instance of the Tfidf class and pass it to the Retrieval constructor.

>>> tfidf = Tfidf()
>>> retrieval = Retrieval(retrieval_model=tfidf, matching=match_op)
>>> retrieval.fit(titles)
>>> retrieval.query("fox")
[0]
>>> retrieval.query("dog")
[1]

At index time, the Retrieval instance invokes the fit method on the provided delegates of query expansion, matching and the retrieval model. At query time, the Retrieval instance consults the predict method of the matching argument for a set of matching indices. The matching indices are passed as indices keyword argument to the query method of the retrieval model. The result of the retrieval model is then transformed back into respective the document identifiers.

Employing word embeddings

Several supplied retrieval models make use of a word embedding. However those retrieval models do not learn a word embedding themselves, but take a gensim Word2Vec object as argument.

from gensim.models import Word2Vec
model = Word2Vec(documents['full-text'], min_count=1)
wcd = WordCentroidDistance(model)
RM = Retrieval(wcd, matching=match_op).fit(documents['full-text'])

Several embedding-based retrieval models are provided natively:

  • Word centroid similarity
    The cosine similarity between centroid of the document’s word vectors and the centroid of the query’s word vectors (WordCentroidDistance(idf=False)).

  • IDF re-weighted word centroid similarity
    The cosine similarity between the document centroid and the query centroid after re-weighting the terms by inverse document frequency (WordCentroidDistance(idf=True)).

  • Word Mover’s distance
    The cost of moving from the words of the query to the words of the documents is minimised. The optional complete parameter (between zero and one) allows to control the amount of considered documents. Setting complete=0 results in re-ranking the documents returned by word centroid similarity with respect to Word Mover’s Distance, while setting complete=1.0 computes the Word Movers distance for all matched documents.

  • Doc2Vec inference
    The Doc2VecInference class expects a gensim Doc2Vec instance as base model instead of a Word2Vec object. The model is employed to infer document vectors for the documents and the queries. The matching documents are ranked according to the cosine similarity between inferred vectors of the query and the documents. The parameters alpha, min_alpha and epochs control the inference step of the Doc2Vec instance.

All these provided embedding-based retrieval models are designed for usage inside the Retrieval wrapper.

Evaluating the model

To evaluate your retrieval model RM just invoke its evaluate(X, Y) method with X being a list of (query_id, query_string) pairs. The gold standard Y is a two-level data structure. The first level corresponds to the query_id and the second level to the document_id. Thus Y[query_id][document_id] should return the relevance number for the specific query-document pair. The exact type of Y is flexible, it can be a list of dictionaries, a 2-dimensional numpy array, or a pandas.DataFrame with a (hierarchical) multi-index.

scores = RM.evaluate(X, Y)

The evaluate(X, Y, k=None) method returns a dictionary of various computed metrics per query. The scores can be manually reduced to their mean afterwards with a dictionary comprehension:

import numpy as np
mean_scores = {k : np.mean(v) for k,v in scores.items()}

The metrics and therefore keys of the resulting scores dictionary consist of:

  • MAP
    Mean Average Precision (@k)

  • precision
    Precision (@k)

  • recall
    Recall (@k)

  • f1_score
    Harmonic mean of precision and recall

  • ndcg
    Normalised discounted cumulative gain (produces sensible scores with respect to the gold standard, even if k is given)

  • MRR
    Mean reciprocal rank (@k)

Where k is the number of documents to retrieve.

Extending by new models

In order to create a new retrieval model, one has to implement a class with at least two methods: fit(X) and query(query, k=None, indices=None). In the following, we will demonstrate the implementation of a dummy retrieval model: the GoldenRetriever model which returns a random sample of k matched documents.

import random as RNG

class GoldenRetriever(RetriEvalMixin):
    def __init__(self, seed='bone'):
        # Configuration
        RNG.seed(seed)
      
    def fit(self, documents):
        # Keep track of the documents
        self._fit_X = np.asarray(documents)

    def query(self, query, k=None, indices=None):
        # Pre-selected matching documents
        if indices is not None:
            docs = self._fit_X[indices]
        else:
            docs = self._fit_X

        # Now we could do more magic with docs, or…
        ret = RNG.sample(range(docs.shape[0]), k)

        # Note that our result is assumed to be
        # relative to the matched indices
        # and NOT a subset of them
        return ret

The newly developed class contains the following three mandatory methods:

  • The constructor
    All configuration is performed in the constructor, such that the model is ready to fit documents. No expensive computation is performed in the constructor.

  • fit(self, documents)
    The fit method is called at index time, the retrieval model becomes aware of the documents and saves its internal representation of the documents.

  • query(self, query, k=None, indices=None)
    The query method is called at query time. Beside the query string itself, it is expected to allow two keyword arguments: k resembling the number of desired documents, and indices which provides the indices of documents, that matched the query.

The reduction of the models own view on the documents (docs = self._fit_X[indices]) is important, since the returned indices are expected to be relative to this reduction (more details in the next section). These relative indices provide one key benefit. Oftentimes, the documents identifiers are not plain indices but string values. Using relative (to the matching) indices allows our retrieval model to disregard the fact that the document identifiers could be a string value or some other index, which does not match the position in our array of documents X. The presumably surrounding Retrieval class will keep track of the document identifiers for you and transpose the query’s result ret to the identifier space.

The inheritance from RetriEvalMixin provides the evaluation method described above, which internally invokes the query method of the new retrieval model.

Matching, scoring, and query expansion

We provide a Retrieval class that implements the desired retrieval process of Figure . The Retrieval class consists of up to three components. It combines the retrieval model (mentioned above) as mandatory object and two optional objects: a matching operation and a query expansion object. Upon invocation of fit(X) the Retrieval class delegates the documents X to all prevalent components, i.e. it calls fit(X) on the matching object, the query expansion object, and the retrieval model object. A query expansion class is expected to provide two methods.

  • fit(X)
    This method is invoked with the set of raw documents X

  • transform(q)
    This method is invoked with the query string q and should return the expanded query string.

We provide more details on the implementation of a full information retrieval pipeline in the Developer’s Guide.

Combining multiple fields and models

Highly experimental, some techniques do not support it yet

The vec4ir package also provides an experimental operator overloading API for combining multiple retrieval models.

RM_title = WordCentroidDistance().fit(documents['title'])
RM_content = Tfidf().fit(documents['full-text'])
RM = RM_title ** 2 + RM_content  # Sum up scores, weight title field twice
R = Retrieval(retrieval_model=RM, labels=documents['id'])

On invocation of the query method on the combined retrieval model RM, both the model for the title and the model for the content get consulted and their respective scores are merged according to the operator. Operator overloading is provided for addition, multiplication, as well as the power operator **, which effectivly weights all returned scores of the retrieval model.

For these Combined retrieval models, the consulted operand retrieval models are expected to return (doc_id, score) pairs in their result set. However, in this case the result set does not have to be sorted. Thus, the query method of the operand retrieval models is invoked with sort=False. Still, the retrieval instance R will invoke the outer-most, combined retrieval model with sort=True, such that the final results can be sorted.

References

Bird, Steven. 2006. “NLTK: The Natural Language Toolkit.” In ACL 2006, 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, Sydney, Australia, 17-21 July 2006, edited by Nicoletta Calzolari, Claire Cardie, and Pierre Isabelle. The Association for Computer Linguistics. http://aclweb.org/anthology/P06-4018.

Buitinck, Lars, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, et al. 2013. “API Design for Machine Learning Software: Experiences from the Scikit-Learn Project.” In ECML Pkdd Workshop: Languages for Data Mining and Machine Learning, 108–22.

Mikolov, Tomas, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. “Distributed Representations of Words and Phrases and Their Compositionality.” In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a Meeting Held December 5-8, 2013, Lake Tahoe, Nevada, United States., edited by Christopher J. C. Burges, Léon Bottou, Zoubin Ghahramani, and Kilian Q. Weinberger, 3111–9. http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.

Mu, Jiaqi, Suma Bhat, and Pramod Viswanath. 2017. “All-but-the-Top: Simple and Effective Postprocessing for Word Representations.” CoRR abs/1702.01417. http://arxiv.org/abs/1702.01417.

Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–30.

Pennington, Jeffrey, Richard Socher, and Christopher D. Manning. 2014. “Glove: Global Vectors for Word Representation.” In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A Meeting of Sigdat, a Special Interest Group of the ACL, edited by Alessandro Moschitti, Bo Pang, and Walter Daelemans, 1532–43. ACL. http://aclweb.org/anthology/D/D14/D14-1162.pdf.

Řehuřek, Radim, and Petr Sojka. 2010. “Software Framework for Topic Modelling with Large Corpora.” In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, 45–50. Valletta, Malta: ELRA.

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