=Paper=
{{Paper
|id=Vol-1391/54-CR
|storemode=property
|title=IIITH at BioASQ Challenge 2015 Task 3a: Extreme Classification of PubMed Articles using MeSH Labels
|pdfUrl=https://ceur-ws.org/Vol-1391/54-CR.pdf
|volume=Vol-1391
|dblpUrl=https://dblp.org/rec/conf/clef/KamineniFDSC15
}}
==IIITH at BioASQ Challenge 2015 Task 3a: Extreme Classification of PubMed Articles using MeSH Labels==
https://ceur-ws.org/Vol-1391/54-CR.pdf
IIITH at BioASQ Challenge 2015 Task 3a:
Extreme Classification of PubMed Articles using
MeSH Labels
Avinash Kamineni?1 , Nausheen Fatma?1 , Arpita Das?1 ,
Manish Shrivastava1 , and Manoj Chinnakotla2
1
International Institute of Information Technology Hyderabad, India
2
Microsoft, India
{avinash.kamineni,nausheen.fatma,arpita.das}@research.iiit.ac.in
m.shrivastava@iiit.ac.in,manojc@microsoft.com
Abstract. Automating the process of indexing journal abstracts has
been a topic of research for several years. Biomedical Semantic Index-
ing aims to assign correct MeSH terms to the PubMed documents. In
this paper we report our participation in the Task 3a of BioASQ chal-
lenge 2015. The participating teams were provided with PubMed articles
and asked to return relevant MeSH terms. We tried three different ap-
proaches: Nearest Neighbours, IDF-Ratio based indexing and multi-label
classification. The official challenge results demonstrate that we consis-
tently performed better than the baseline approaches for Task 3a.
Keywords: MeSH Indexing; Biomedical Semantic Indexing; Hierarchi-
cal Text Classification; FastXML; PubMed; Information Retrieval and
Extraction; Metamap
1 Introduction
The annotation of biomedical journals by the experts is both expensive and
time-consuming. Therefore, Large Scale Hierarchical Text Classification in this
domain has gained much importance over the past few years. It is also helpful in
fields like Question Answering, Information Retrieval, Categorization etc. The
challenge introduced by BioASQ [23] deals with handling large scale complex
data and automatically assigning relevant MeSH [1] terms to the PubMed [3]
articles.
Researchers have tried to crack the problem of biomedical semantic indexing
using a wide variety of methods such as Latent Semantic Analysis [14], Latent
Dirichlet Allocation (LDA) [7], Support Vector Machines [9] etc. We approach
the problem from a document clustering perspective, based on the observation
that similar documents often share MeSH terms. In this paper, we built a generic
model for tagging the documents with MeSH terms which can be utilized in
?
These authors contributed equally
any other domain. Three different approaches namely Nearest Neighbours, IDF-
Ratio based learning and FastXML [21] based extreme classification were used.
All the three approaches beat the BioASQ baseline and had high precision values,
however the values of recall were comparatively low.
The rest of the paper is divided into following sections: Section 2, describe the
previous works done in BioASQ semantic indexing task. Sections 3 explains the
model using different approaches in detail. Section 4, contain the experiments
performed and the results obtained. Section 5, comprises of the conclusion and
future work.
2 Related Work
Semantic Indexing has been a topic of research for several years. Amongst the
successful unsupervised models, the most well known one is Latent Semantic
Analysis (LSA) [14] developed by Deerwester et al. LSA takes the high dimen-
sional vector space representation of documents and applies dimension reduction
by Singular Value Decomposition (SVD) on it. The similarities between docu-
ments are more reliably estimated in the latent semantic space than in the orig-
inal one. However, LSA lacks solid statistical foundation. Hence, Hoffman et al.
introduced Probabilistic Latent Semantic Analysis (PLSA) [15] based on a sta-
tistical latent class model.This model dealt with domain specific synonymy and
polysemy. David M. Blei et al. introduced Latent Dirichlet Allocation (LDA) [7]
considering the mixture models that capture the exchangeability (Exchangeabil-
ity and related topics David J. Aldous) of both words and documents. Each item
of a collection is modeled as a finite mixture over an underlying set of topics.
Few supervised methods were also developed in this area. Bing Bai et al.
proposed Supervised Semantic Indexing (SSI) [6] which defines a class of models
that can be trained on a supervised signal (i.e., labeled data) to provide a ranking
of a database of documents given a query. Sutanu et al. proposed sprinkling
[11] to automatically index documents. Sprinkling is a simple extension of LSI
based on augmenting the set of features using additional terms that encode
class knowledge. But sprinkling treats all classes in the same way. To overcome
this problem, they proposed Adaptive Sprinkling (AS) which leverages confusion
matrices to emphasise the differences between those classes which are hard to
separate.
Considering prediction of MeSH headings, we have Medical Text Indexer
(MTI) [20], the official solution of National Library of Medicine (NLM). The
major components of MTI are:
1. MetaMap Indexing (MMI) [4]
2. PubMed Related Citations [17]
3. Restrict to MeSH [8]
4. Extract MeSH Descriptors
5. Clustering and Ranking [2]
The approach of Tsoumakas, G. et al. [24] performed better than MTI. MetaL-
abeler [22] by Tang et al. used binary classification model trained using linear
SVM. Also a regression model was trained to predict the number of MeSH head-
ings for each citation. Finally, given a target citation, different MeSH headings
were ranked according to the SVM prediction score of each classifier, and the top
K MeSH headings were returned. Learning to rank (LTR) method, which was
utilized by Lu et al. [19] [16] for automatic MeSH annotation. In this method,
each citation was deemed as a query and each MeSH headings as a document.
LTR method was utilized to rank candidate MeSH headings with respect to tar-
get citation. The candidate MeSH headings came from similar citations (nearest
neighbors). In the similar line of thought Huang et al. reformulated the indexing
task as a ranking problem [16]. They retrieved 20 neighbor documents, obtained
a list of MeSH main headings from neighbors, and ranked the MeSH headings
using ListNet learning-to-rank algorithm [10].
3 Our Approach
Our system mainly consists of three different modules. We compare these differ-
ent systems. In this section, we explain these approaches in detail.
Fig. 1: System Modules
We have implemented three distinct techniques to index articles. Eventually,
our aim was to find which of these techniques contribute the most in finding
relevant MeSH terms. The following are the three techniques:
1. K Nearest Neighbours approach
2. IDF-Ratio based approach
3. Extreme Classification using FastXML.
3.1 K Nearest Neighbours Approach
In this approach,we use a K Nearest Neighbours [12] based lazy learning ap-
proach to find the most relevant MeSH headings.
Fig. 2: K Nearest Neighbours approach
The method is as follows :
1. The training files were first converted to Lucene index with fields
“pmid”,“title”,“abstractText”,“meshMajors”.
2. K Nearest Neighbours are retrieved for finding the candidate MeSH terms
For a given unknown test instance, the fields abstract and title were concate-
nated as a single string. We then find K Nearest Neighbours (with k=60)
from the Lucene index. Similarity of documents is computed by finding the
number of overlapping words and giving them different weights based on
TF-IDF [18].
3. Rank to each candidate MeSH term is given by its number of occurrences in
the neighbours
Top 60 (k=60) similar records were retrieved and a HashMap was created
with every MeSH term found in the neighbours as key and the count of total
number of times that MeSH term occurs in the all the neighbours together as
value. The HashMap keys become our candidate MeSH terms for the given
test instance.
4. Threshold is used for final predictions
For every pair in the hashmap created above, the value is com-
pared against a threshold α. If value >= α then the key is included in a set
S. If the value < α then we check if the key (which is a MeSH term) exists
in the title or abstract. If the key is present in the title or abstract then it is
very likely that the key is a relevant label and is added to the set S. After all
the pairs have been iterated,the set S becomes our final MeSH
label set for x.
α was set to 12 empirically for k=60. It was observed that threshold α = k/5
generally gave optimum results for unweighted votes.
Query k alpha precision recall
Title + abstract -stopwords 60 12 0.510845 0.503196
Title + abstract -stopwords 75 3.75 0.472817 0.539864
nounphrases(From Title + abstract) 75 3.75 0.451753 0.540818
Nouns(From Title + abstract) 75 3.85 0.464746 0.541609
Nouns(From Title + abstract) 75 15 0.511757 0.487618
Nouns(From Title + abstract) 60 12 0.50631 0.496969
Table 1: Results of different approaches for Nearest Neighbours Candidate Se-
lection
Some variations using this approach were also tried :
1. Weighted votes are used with similarity distance score as weight.
2. Using just noun phrases as queries
3. Using just nouns as queries
3.2 IDF-Ratio based approach
We know that IDF (Inverse Document Frequency) measures the importance of
a particular term in a set of documents. But certain terms like “is”, “and”, and
“are”, may appear frequently but have little importance. Hence idf weighs down
the frequently occurring terms and boosts up the rare and significant ones. IDF
for a term t can be expressed as:
N
IDF (t) = log (1)
Nt
where, N is total number of documents, Nt is number of documents with term
t.
Here for the task of semantic indexing we need to find that how much a particu-
lar word is important for a MeSH term. In other words we want to find out which
particular word(s) in a document can lead to a MeSH term.For extracting this
information the novel concept of IDF-Ratio is introduced. This ratio identifies
the word(s) in a document that will certainly result in a MeSH term. The IDF
Ratio with respect to a MeSH term for a word can be expressed as :
Nm
Ntm
IDF − Ratio(t|m) = ( N ) (2)
Nt
where, Nm is number of times a particular MeSH term m is occurring, Ntm is
total number of times the term t occurred with that MeSH term m. Thus, IDF-
Ratio(t|m) for a t term exists for every 27455 MeSH terms (m) provided.
We have IDF Ratio of a word for all the MeSH terms. It does not make sense
to consider all the 27455 MeSH terms for a single word, since a word cannot lead
to all the MeSH terms. So it is necessary to filter out the unwanted MeSH terms
for each word. We do this by thresholding. After experimenting with different
values, a threshold of 0.55 was found to be optimum. Now every word is related
to 5-15 relevant MeSH terms which it can potentially lead to. Some of the MeSH
terms like “humans”, “male”, “female”, “animals” are very common and occurs
with almost every word, so for any word, the IDF Ratios with respect to these
MeSH terms are very high. So almost all the words lead to these MeSH terms.
Fig. 3: IDF Ratio based indexing
Algorithm
1. Pre-processing
The documents given to index are tokenized. The set of biomedical stopwords
are eliminated from the documents. Some Special symbols are removed. The
symbols necessary for retaining the meaning of chemical components are
kept intact.
2. Extraction of meaning words
POS-Tagger is used to extract the NN ,NNS, NNP,VB,JJ and RB tags from
the documents. SENNA [13] is used for the tagging purpose. It uses deep
learning (unsupervised convolutional neural network) to tag sentences.
3. Collection of candidate MeSH terms
After obtaining the meaning words we consult the IDF Ratios with respect
to the MeSH terms. For each word, we choose a set of MeSH terms it can
lead to. Finally we get a candidate set of potential MeSH terms.
4. Ranking the candidate MeSH terms
The MeSH terms in the candidate set has to be ranked correctly. The fol-
lowing ranking approaches were used:
(a) Ranking in the order of IDF-Ratio: The words possess IDF Ratio
with respect to the MeSH terms, we can rank these MeSH terms in the
order of these ratios. If more than one word in the document leads to
the same MeSH term ,their corresponding IDF ratios are simply added.
(b) Ranking in terms of maximum intersection: In a document if sev-
eral words are pointing to the same MeSH term then that MeSH term
must be important for that document. This concept is utilised in this
ranking method. We gather the set of MeSH terms for each meaning
word and find the intersection of these sets. The elements of intersection
are assigned as indices of the document.
(c) SVM-Rank:3 It is used to rank lists of items. For training, the inputs to
SVM-Rank are ordered entries of every possible pair of items which are
assigned weights depending upon the correctness of the order. Initial step
of optimisation problem is formulated as ordinal regression; however, it
is turned into a classification problem due to the pair wise difference.
In the semantic indexing task, feature vector is composed for the MeSH
terms. The feature vector consists of bag of words, IDF Ratio weights,
etc. The above two methods of ranking mentioned in a) and b) did not
yield good results, so the rankings obtained through them were included
as features for training SVM-Rank. Inclusion of this feature resulted in
a slight improvement in the performance.
The main difficulty was in assigning weights to the MeSH terms. While
training, we give all the terms assigned to that document very high
weights, but we cannot grade them in some order, as we have no clue
which of the tags assigned to the document has more weight and which
has less weight. Similarly, we have no other way of giving weights to the
3
SVM for ranking http://www.cs.cornell.edu/people/tj/svm_light/svm_rank.
html#References
remaining MeSH terms in the data provided, that are not assigned to
that document .
After ranking is done ,the filtered top-ranked MeSH terms are assigned to
the document.
Fig. 4: MeSH Term vs Number of Samples in Task 3a Training Data
3.3 Extreme Classification using FastXML
The main objective of FastXML [21] is to acquire fast and efficient training of a
model. Training of 4 Million BioASQ 2015 documents took about 36 hours on a
4 core machine. Also, FastXML is capable of learning the hierarchy of the MeSH
terms by optimizing the ranking loss function. Existing approaches optimize
local measures of performance which depends solely on predictions made by the
current node being partitioned. FastXML allows the hierarchy to be learned node
by node, starting from the root and going down to the leaves, thus it is more
efficient than learning all the nodes jointly. The frequent MeSH terms could be
learnt better compared to the rare ones.
FastXML is based on the assumption that only a few number of labels occur
at each region of the feature space. It learns ensemble of trees and does not
rely on base classifiers. The output of the classifier is the labels along with their
probabilities. It also provides the precision at 1..k, where k is the max number
of labels that must be tagged for a document. The experimental results of this
approach is explained below.
Fig. 5: FastXML approach
1. Tokenization
As the terms in this particular domain contains special symbols in the chem-
ical formulae etc, special care is taken while tokenizing. Few special sym-
bols like (-,) are maintained. This tokenization is done using the tokeniza-
tion module of word2vec4 source code provided in Open source software by
BioASQ. They also have the vocabulary list of 1.7 million words.5
2. DF Matrix Construction
We iterate over each document in the BioASQ 2015 training set and tokenize
the title and abstract, for each token we increment the corresponding MeSH
term column. So, this gives us a sparse matrix, indexed accordingly, which
is later used for feature extraction.
4
The word vectors can then be used, for example, to estimate the relatedness
of two words or to perform query expansion. http://bioasq.lip6.fr/tools/
BioASQword2vec/
5
For the unidentified words in the vocabulary, we have done simple Laplace Smoothing
for updating the weights of the feature.
3. Feature Extraction
Different features used for the classification.They are described as follows:
(a) Unigrams with TF-IDF weights
From a document, each token in the title and abstract is taken and their
term frequency is found out. Document frequency can be found from the
DF Matrix. Hence, we can calculate TF-IDF value for each token. These
TF-IDF weights act as a feature.
(b) Exact Match MeSH heading feature
We create bag of words of all the the 27K MeSH terms. If the document
text contains a MeSH heading, then its position in the 27K dimension
feature vector is set to 1 , else 0.
(c) Noun Phrase Feature
We need few specific tools for dealing with the Biomedical domain. Using
metamap [5], we can find the part of speech tags for the sentences, iden-
tify the chunks from the given article, identify the head or main word
from the passage, using which we could find the noun phrases from the
article and pass the tokens present in noun phrase as features.
(d) Semantic Feature
Using nouns extracted from the noun phrases, we try to get the meaning
words for the identified concepts from the MetaMap, MeSH Heading
Descriptors etc. LESK Algorithm6 is used to obtain semantically similar
words.
MeSH Term/ Index in Overall DF in DF in DF in .. DF in
Vocabulary Vocabulary DF MH1(Human) MH2 MH3 . MH26456
aminopepsin 283542 3 3 2 2 .. 0
cardio 428700 57 45 0 34 .. 0
Table 2: Index of the Document Frequency Matrix
4 Experiments and Results
As a part of the BioASQ 3a challenge 2015, we have made weekly submissions
of the two of three batches. We performed better than the baseline System each
time. The results of one of submission of 3a Batch 3, Week 3 are shown in the
following tables.
In tables 3 and 4, IIIT System 3 represents the Nearest Neighbours approach,
IIIT System 4 represents the IDF Ratio based approach and qaiiit system 1
represents the FastXML approach.
6
Semantic Word Matching Algorithm http://en.wikipedia.org/wiki/Lesk_
algorithm
Submission /Metric MIF MIP MIR Acc.
IIIT System 3 0.4978 0.4412 0.5711 0.3323
IIIT System 4 0.4534 0.5737 0.3748 0.2198
qaiiit system 1 0.4164 0.4047 0.4287 0.2672
BioASQ Baseline 0.262 0.2391 0.2897 0.1542
Table 3: Flat Measures of Task 3a Batch 3,Week 3
Submission / Metric HIP HIR HIF LCAF
IIIT System 3 0.6408 0.7074 0.648 0.4362
qaiiit system 1 0.6191 0.5477 0.5555 0.3767
IIIT System 4 0.7591 0.4304 0.5111 0.3636
BioASQ Baseline 0.5337 0.5831 0.5321 0.3054
Table 4: Hierarchial Measures of Task 3a Batch 3,Week 3
The results of IDF Ratio method are as follows:
1. This method gives a very high precision of 0.84 but the candidate set is too
large in number.
2. SVM-Rank gives a very low recall of 0.25 only. This is due to the inability
to assign proper weights in descending order to the MeSH terms.
3. Ranking in the order of IDF-Ratio gave a recall of 0.267. Very common MeSH
terms like male, females, rats had very high IDF-Ratio value in the overall
documents, hence they were assigned to almost all the documents,thus de-
creasing the recall value.
4. Ranking in terms of maximum intersection also gave a recall of 0.232. This
faced the similar problem as that in ranking in the order of IDF-Ratio.
Mostly, the common MeSH terms were found in the intersection set .
5. Due to the high precision and low recall the overall F-score reduced to 0.4.
Precision Recall F-Score
SVM-Rank 0.84 0.25 0.39
IDFRatio order 0.84 0.267 0.41
Intersection 0.84 0.232 0.36
Table 5: Showing results for different ranking methods using IDF Ratio
Error Analysis
1. Few of the common MeSH terms like “Humans”, “Male”,“Female” occurs in
most of the articles hence these terms are tagged with high probability.
2. Rare MeSH terms like “2-Oxoisovalerate Dehydrogenase (Acylating)”, “Hydroxyacyl-
CoA Dehydrogenase” occurs in very few articles,hence their probability of
being tagged is very low.
Observations
For IDF Ratio based approach, the following observations were made:
1. The concept of IDF Ratio is pretty intuitive, it help us determine the impor-
tance of a word for a particular MeSH term. We can determine the presence
of which words lead to a MeSH term.
2. As a part of an experiment, hierarchy information was tried to be infused
in this method. Several approaches were tried like for a MeSH term, its
child, parent and siblings are included till 2 levels in the candidate set, or if
a parent is included in candidate set its child is excluded,etc. Several such
schemes were applied but with no significant change in results. No particular
hierarchial pattern was followed by the data provided.
3. As already mentioned the precision of this approach was high, the candi-
date set sort of formed a superset of the answers obtained by the other two
methods i.e., Extreme Classification and Nearest Neighbour.
5 Conclusion and Future Work
It can be stated that by using the Nearest Neighbours we can limit the can-
didate MeSH terms by maintaining the precision and recall. By the IDF Ratio
approach we can gather all the mesh terms a word can lead to. It sort of cap-
tures both lexical and semantic information. By using Extreme Classification,
training can be done quickly even on a single machine, this process is scalable.
The information of hierarchy between the MeSH terms can be captured. These
three approaches mentioned, are implemented independently. The next logical
step would be to combine these results and use them as features for the ranking
algorithm, which will be done as a part of our future work. Future work includes :
1. To come up with a better ranking algorithm to rank the MeSH terms in the
candidate set.
2. To exploit the hierarchy information of the MeSH headings provided.
3. To merge the 3 approaches to get a compact and smaller version of the
candidate set.
4. In IDF-Ratio approach we are basically finding the MeSH terms which are
pointed by individual words, in future it would be a better idea to find the
MeSH terms which the entire document is leading to.
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