Calculating Semantic Similarity between Academic
Articles using Topic Event and Ontology
Ming Liua,1, Bo Lang a and Zepeng Gu a
a

State Key Laboratory of Software Development Environment, Beihang University, Beijing,
China

Abstract. Determining semantic similarity between academic documents is crucial to many
tasks such as plagiarism detection, automatic technical survey and semantic search. Current
studies mostly focus on semantic similarity between concepts, sentences and short text
fragments. However, document-level semantic matching is still based on statistical information
in surface level, neglecting article structures and global semantic meanings, which may cause
the deviation in document understanding. In this paper, we focus on the document-level
semantic similarity issue for academic literatures with a novel method. We represent academic
articles with topic events that utilize multiple information profiles, such as research purposes,
methodologies and domains to integrally describe the research work, and calculate the
similarity between topic events based on the domain ontology to acquire the semantic similarity
between articles. Experiments show that our approach achieves significant performance
compared to state-of-the-art methods.
Keywords. Document Semantic Similarity; Topic Event; Ontology; Scientific literature
analysis

1. Introduction

Text semantics matching is widely used in many applications such as machine
translation, automatic question answering, and knowledge retrieval. It also has great
significance in plagiarism detection, automatic technical survey, citation recommendation
and research trend analysis in academic domain. The issue of text semantics, such as word
semantics and sentence semantics has received increasing attentions in recent years.
However, rare research focuses on the document-level semantic matching due to its
complexity. Long documents usually have sophisticated structure and massive information,
which causes hardship to measure their semantic similarity, and there is even no public
available dataset as far as we know.
Large text units are composed of small text units. The semantics of long document can be
derived from the combination of small text unit semantics. Many recent studies follow this
thought to acquire the semantic similarity between larger text units. For example, the sentence
semantic similarity can be achieved from the integration of semantic similarities between word
1

Corresponding Author, Ming Liu, State Key Laboratory of Software Development Environment, Beihang
University, No.37 Xueyuan Rd., Haidian District, Beijing, China. E-mail: liuming@nlsde.buaa.edu.cn.

1

pairs from two sentences [1,2]. Besides lexical semantics, global sentence level features are also
considered for acquiring the semantic similarity between sentence [1-8]. However, those studies
only focus on short texts by lexical semantics and sentence-level features, which are still far
from the capability of document-level semantic similarity.
The study focusing on semantic similarity between documents is relatively rare. Existing
methods of document-level similarity mainly focus on information retrieval in surface level
rather than semantic level understanding. Those conventional similarity metrics [9-11] measure
the document similarity by statistics or morphology of words, neglecting documents’ structure
and meanings of their words, such as vector space model (VSM) [12]. VSM regards each
document as a collection of words and measures the document similarity mainly based on the
presence of words, e.g., there are two snippets of text: “Jack borrowed a book from the teacher”
and “The teacher borrowed a book from Jack”. VSM regards the two texts as equal, but actually
they have opposite meanings. Latent Dirichlet Allocation (LDA) [13] is proposed for the deep
analysis of documents based on the difference of topic distributions over documents, which can
be used to measure document-level semantics. There are also researches [14-19] trying to add
external knowledge to the document representation, which enrich the content by adding the
relevant terms from knowledge resources. However, these methods still suffer from problems
like computational complexity and representational opaqueness.
Long documents contain many topic transitions and different focuses, which makes it
difficult to capture their core semantics. However, we believe that those topics in a document
are coherent, and those correlations can be obtained by the comprehensive analysis on various
factors of a document. Hence, we represent the core semantic issue in each document as an event
which is called a Topic Event (TE). TE is the structured summary extracted from each document,
which contains the comprehensive key elements of the document.

D1

D1_Target
D1_Methodology
D1_Domain
D1_Keywords
D1_Style
D1_Date
Topic Event for D1

D2

D2_Target
D2_Methodology
D2_Domain
D2_Keywords
D2_Style
D2_Date

Research Style
Ontology

Semantic
Similarity
Computing

Document Semantic
Similarity

Domain Ontology

Topic Event for D2

Fig. 1. TE-based document semantic matching
The core semantics of an academic article is the authors’ research work. We construct TE
based on the article structure and using multiple information fields such as research targets,
methodologies, keywords and domains, which can integrally describe various facets of the
research work. Hence, the semantic similarity between academic papers can be measured by TE
similarity, which is shown in Fig. 1. For achieving high accuracy in TE similarity calculating,
we also develop and utilize the research style ontology and domain ontology in the academic
domain. In order to make our approach more practicable, we present the method of how to
automatically construct a TE. To verify performances, we construct an evaluation corpora by

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manual annotation using ACL Anthology Network (AAN) corpora [20]. Experiments
demonstrate that our methods gain outstanding performance, and the results are also more
conforming to human’s comprehensions. To summarize, the main contributions of our work are
as follows:
—We propose the idea of constructing the topic events as the semantic representations of
long documents, and give a general method for topic event similarity calculation.
—We develop and construct the research style ontology and the domain ontology for
academic articles, and these two knowledge resources can facilitate the semantic extraction and
similarity measurement procedures of the topic event effectively.
—We provide an ontology-based automatic TE construction method without labeled data
in a specific domain, and utilize this method in the topic events construction of computational
linguistic documents.
—We introduce a document semantic matching corpus with fine-grained annotations for
the first time, which can serve as the ground truth for the evaluation of document-level semantic
matching researches.
The remainder of the paper is organized as follows. In Section 2, we describe the related
work. Section 3 provides the sketch of the topic event in academic domain. In Section 4, an
automatic construction method of topic event is given. In Section 5, we describe the similarity
computing method of the topic event. In section 6, the domain ontology and evaluation corpora
in the computational linguistics domain are constructed, and then the experimental evaluations
are given. Finally, we conclude the paper in Section 7.

2. Related work
The document-level semantic similarity research is a new arising area, which is thought can
be enlightened by the concept and short text semantic matching methods. The concept-level
semantics is the bricks of document semantic comprehension, and the short text-level similarity
is the current most active area, which throws light on the document-level semantic similarity.
In general, concept-level semantic similarity can be measured by knowledge-based methods
and corpus-based methods. Knowledge-based methods mainly utilize the path between concepts
in knowledge resources to indicate their semantic similarity [21,22]. Lin [23] and Resnik [24]
measured the semantic similarity by the information content ratio of the least common subsumer
of the two concepts. Instead of directly exploiting the graph distance in knowledge resources,
several researches [25,26] manufactured concept vectors according to a set of ontology
properties for concept semantic similarity. Corpus-based methods assume that words with
similar meaning often occur in similar contexts, Latent Semantic Analysis (LSA) [27] represents
words as compact vectors via singular value decomposition (SVD) on the corpus matrix, and
GloVe [28] reduced computational costs by training directly on the non-zero elements in corpus
matrix. Turney [29] measured semantic similarity with the point-wise mutual information. Web
can also be regarded as a large corpus, and the Google Distance [30] utilizes the number of
words co-occur in web pages for concept semantic similarity. Mikolov [31] proposed the word
embedding approach with neural network to capture the word semantics occurred in a fixed size
window.
A challenge for determining semantic similarity of short texts is how to go from word-level
semantics to short-text-level semantics. A direct approach is utilizing weighted sum of word-toword semantic similarity, and [1,2] used the greedy word alignment method to form the sentence
semantic similarity. Banea et al. [3] measured the semantic similarity between text snippets
infused with opinion knowledge. D Ramage et al. [6] constructed a concept graph using words

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of each text snippet, then measured the similarity between two concept graphs. Recently, the
SemEval conference released the Semantic Text Similarity (STS) task especially for the boom
of short text semantics, and regression models are adopted by most teams [5,8] to predict the
similarity score, and lexical feature and syntactic features are exploited. The paragraph vector
[32], which is similar to word embedding, is also proposed with neural network to measure the
semantic similarity between short texts.
The research directly related to document semantic similarity is rare and undeveloped.
Traditional similarity researches such as the TF-IDF [12] converted documents to vectors via
word counting, and measured the similarity between documents by the similarity of vectors.
Academic articles can be regarded as semi-structured texts, which contain many structured
annotations besides plain text. The similarity between academic articles can be measured with
the help of annotated information. Martin et al. [33] fused the structured information, such as
authors and keywords with traditional text-based measures for academic articles similarity. [911] regard articles and their citations as an information network. The similarity between articles
turns to the similarity of two entities in information network. Unfortunately, the above
conventional similarity methods aim at document indexing in surface level, rather than semantic
level document understanding.
There are several studies trying to add external knowledge to achieve the semantic
representation of documents. [14,15,17] enriched the content by adding relevant terms from
knowledge resources, which aim at improving the quality of the document clustering results.
[18,19] extracted relation triples from source document and added entity relations from
background knowledge to construct triple graph for document enrichment. Schuhmacher and
PonzeRo [16] proposed a graph-based semantic model for representing document content, which
added knowledge to the document representation by linking the entities in the document to the
DBpedia knowledge base. Those methods acquire fine-grained relationships among entities and
generate a knowledge-rich document model, and the semantic similarity is calculated by the
graph edit distance. However, above methods are lack of interpretation. Furthermore, there are
rare entities such as persons, organizations and place names inside the content of academic
articles, which makes those methods unsuited.
Long documents such as academic articles usually have several focuses and vast amount of
words. LDA [13] obtains the semantic relatedness among different concepts via topics and
regards each document as a distribution over a set of topics. Thus LDA can be used in the
semantic analysis of long documents. Muhammad Rafi [17] defined a similarity measure based
on topic maps in the document clustering task. The documents are transformed into topic maps
based coded knowledge, and the similarity between a pair of documents is represented as a
correlation between their common patterns. M. Zhang et al. [34] enriched document with hidden
topics from external corpora, and measured the document similarity in text classification task
with the similarity of topic distributions. Along the direction of topic model, [35,36] measured
the semantic similarity between documents based on the divergence of topic distributions, which
can be calculated by the Kullback-Leibler (KL) distance, and the LDA-based method is befitting
in the semantic similarity task between academic articles.

3. Topic event
What can be used to convey the main semantics of a long document? The task is complicated
and would not benefit directly from the simple accumulation of massive concept semantics. To
get a global comprehension of a document, it’s necessary to extract key information from large
amount of words and form a core semantic of a document.

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3.1. Topic event structure
In academic domain, articles are used to convey the research progress. Most academic
articles have normative formats and regular structures, and the research work comprises similar
profiles, which can be described in a uniform topic event structure.
The most notable points of an academic paper are their purposes, methods and results, which
convey the main information of the research work. Keywords can fuzzily represent the core
semantics and acquaint readers with the general research cognition. The domain of research
issues indicates the research branch, and the type of research work reflects the research style and
difficulty. The publication dates imply different stages of research issues. We define the above
factors as the primary elements in a topic event, and the structure of the topic event is defined in
Fig. 2.
The items annotated with * is crucial and essential, which cannot be vacant, while other
items are optional. Eid and Did are essential identifications for topic event and the corresponding
paper. Style indicates the research type, and the elements such as Domain, Target, Methodology,
keywords are terminologies extracted from papers, while the elements of Conclusion,
Background, Performance and Forecast are key sentences in papers.

Topic Event={

* Eid: essential ID for Topic Event;
* Did: essential ID of the corresponding paper;
* Target: essential purpose of the research;

* Methodology: techniques used in the research ;
* Domain: essential category of the research issue;
* Style: essential type of the research manner;
*Keywords: keywords describing the article.;
*Date: essential date of publication;
Metadata:
{author; organization; citation; publication; venue}
Name: optional name of the developed system;

Object: optional objects being researched ;
Tools: optional tools used in the research;
Feature: distinguishing characters in research method;

Conclusion: optional conclusion sentences;
Background: optional supporting projects;
Forecast: optional sentences for future research;

Performance: the performance of the research;
Dataset: optional datasets used in the research;
}

Fig. 2. Structure of topic event

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3.2. Research style ontology
The styles of research work implicate important semantics. They can reflect the variance of
researches in difficulties, ways and types. For example, E1 and E2 are two research work as
follows.
E1: The authors survey the techniques around a certain issue and summarize them as an
academic article.
E2: The authors focus on a certain problem and propose a solution, the process and result
of the solution are written as an academic paper.
Table 1 Details of the research style ontology
Remark
Example
Proposing original Latent Dirichlet Allocation
approaches
Improving some
Improving LDA Topic Models for
methodologies or
Microblogs via Tweet Pooling and
theories
Automatic Labeling
System
Implementing
TEXTRUNNER:Open Information
Implementation some systems or
Extraction On the Web
tools
Issue Solution
Solving some
Biological Event Extraction using
problems with
Subgraph Matching
existing methods
Survey
Surveying certain
An Overview of Event Extraction
research issues
from Text
Analysis
Analyzing certain
a comparison of approaches to
issues
large-scale data analysis
Phenomenon
Uncovering certain The Role of Research Leaders on
Discovery
conclusions
the Evolution of Scientiﬁc
Communities
Type
Theoretical
Origination
Methodology
Improvement

There are distinct differences between E1 and E2. E1 is a paper of Survey class, while E2
is a paper of Issue Solution class. In general, E2 has more innovation and difficulty than E1, and
they have different values. E1 is suitable for beginners to get basic knowledge, while E2 is more
suitable for inspiring experienced persons. Therefore, the type of an academic paper is an
important factor in expressing its semantics. To express the knowledge implicated by types of
research styles, we first develop the style categories of topic event, which is constructed in Fig.
3 by using protégé[37]. Each definite style of each research work is shown and explained in
Table 1. The research style ontology implicates the relations between different styles of research
work, which can be used to measure the semantics between different academic researches.

6

Fig. 3. Hierarchy of research style ontology

4. Automatic construction of topic event
4.1. Overview
As shown in Figure 2, we need to extract the Target, Methodology, Domain, Style,
Keywords and Date from a document to construct the topic event. Present work on extracting
structured representations of events has focused mainly on newswire texts, which utilize labeled
entities, time expressions and values occurred in target sentences as candidate event items. The
traditional event extraction problem is regarded as a classification problem with the help of
labeled data. However, there is rare labeled data for training an extraction model, and almost no
labeled entity can serve as the candidate terminologies in a specific domain.
Academic literatures have several characteristics which present unique challenges and
opportunities for event item recognition. There are many structured annotations in academic
articles such as the citations, authors, date of publication, keywords and journals, which are
obvious to extract and can be used to enrich the topic event [38]. Notwithstanding, many
important items of topic event are hidden in the unstructured content of academic articles. The
main work of extraction is to identify terminologies such as target, methodology, domain and
style in the article content. To solve the problem of lacking labeled event data, we propose an
ontology and pattern-based extraction method.
Academic articles usually have clear topics and purposes, and there are many regular
syntactical structures in academic articles which give clues to event extraction. The process of
topic event construction is described in Fig. 4. Firstly, we divide the academic articles into
different sections and select the most significant sections for topic event extraction. Then we
conduct basic natural language processing (NLP) such as sentence splitting and part-of-speech
(POS) tagging on each implicated sentence in the selected sections. Thirdly, we choose all the
noun phrases (NP) in each sentence as candidate topic event items as well as a limited
terminology list derived from the domain ontology. After that, the best event arguments are
chosen from several candidates after pattern matching. At last, the extracted event items are
delivered to the domain ontology to expand the related event semantic items. Generally, the
domain ontology conveys the semantic relatedness among domain terminologies, which can
provide the terminologies as well as their relationships in a specific domain. In this paper, the
domain ontology can provide extern knowledge for the semantic comprehension of documents,
and assist the procedure of topic event construction.

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Preprocess

Academic
articles
Text
Chunker

POS Tag

Pattern match

Key Phrases

Sentence Split

NP phrases

Candidate
items

Implicated
Sentences

Pattern
Matching

Domain Ontology

Pattern Base

Semantic
Expansion

Formalization
Extended
Event items

Optimal Event items

Fig. 4. Process of topic event extraction
4.2. Event items recognition
In detail, we divide each academic article into several chunks according to its outline, and
the Title, Abstract, Introduction and Conclusion sections are believed to have a global
description of the whole research work without much unnecessary detail. Then we identify the
implicated sentences from above sections by trigger words. The implicated sentences may
contain the event items, and we define a total of 95 trigger words for items extraction.
To capture the candidates of event items in implicated sentences, we leverage the domain
ontology and some NLP processes. A terminology list derived from the domain ontology is used
to find potential event items. However, another key issue is how to find a lot of unknown phrases
in novel academic articles. To solve this problem, we conduct POS tagging on each sentence,
and then utilize all the noun phrases as the candidates of event items to capture the unknown
new phrases.
Recognition of target and methodology After acquiring the candidate terminology, the
next step is to confirm which candidate is the best event item in each sentence. We develop the
patterns for the extraction of Target, and Methodology. The extracting patterns are composed of
pre-patterns and post-patterns, which are the patterns occurs frequently ahead of the event items
and the patterns occurs frequently after the event items. Several typical extracting patterns of
Target and methodology are shown in Table 2. There are altogether over 550 patterns for the
extraction of Target and Methodology.
For example, the implicated sentence “In this paper, we propose a supervised machine
learning approach for relation extraction” is identified by the trigger words “propose” from the

8

introduction section of an academic article. It matches the preceding target pattern, i.e.
“approach for”. Thus, the terminology “relation extraction” is chosen as the target of this article.
Table 2 Typical extracting patterns for TE elements
Patterns preceding Patterns following the
the TE item
TE item
Target
the problem of
overview
the task of
evaluation
system to
track
survey on
system
approach for
called
framework for
process
Methodology by use of
based framework
that using
method to
which employ
algorithm for
takes advantage of
techniques to
methods of
is applied
through an
performs much better
TE item

Table 3 Typical patterns for research styles
Research category
Pattern
Theoretical Origination
model
model for
Methodology
improving
Improvement
improved
improvement
System Implementation
- , : ,corpus
Issue Solution
Using, based, focused,
framework, expediting, hybrid,
by, exploiting, incorporating,
utilizing, methods, extracting,
with, use, via, approach, measures
Survey
Overview
an overview of
survey
Introduction
Analysis
comparison
assessing
evaluation
evaluating
analysis
Challenges
the future for
Phenomenon Discovery
exploratory
exploratory of
Recognition of research style We can see that articles of different research styles have
different title characteristics from Table1. Many types of academic articles in computational

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linguistic domain can be diagnosed by the title of the paper. For example, each title of Issue
Solution type is distinct, it will start with the abbreviated name of their software and connect the
succeeding title with the punctuation “:” or “-”, e.g., “TEXTRUNNER : Open Information
Extraction On the Web”, “URES : An Unsupervised Web Relation Extraction System”. Most
titles have featured words to distinguish their research styles, and we develop patterns to identify
the topic research style in Table 3.
4.3. Ontology-based semantic expansion
Many items in a topic event are closely correlated such as the research purpose and domain,
the adopted methodology and toolkit, the research object and dataset. Generally, the research
purposes are the core issues of the academic articles and corresponding topic events, and the
domain that an academic article belongs to is decided by its research purpose. When we extract
the target of an academic article, we use the domain ontology to induct which domain it belongs
to.
The domain ontology
(e.g. Computational Linguistics Ontology)

Information
retrieval

Information
Extraction

Research Target

Machine
Translation

...

Semantic
computating

Question
Answering

Research Domain

Fig. 5. Architecture of ontology-based semantic expansion
After extracting the research target, the semantic similarities between the target and each
predefined domain concept are calculated based on the domain ontology, as shown in Fig. 5
which uses the Computational Linguistics Ontology as an example of domain ontology. The
domain concept which has maximal semantic similarity with the target concept is chosen as the
domain of the corresponding research article.

5. Topic event similarity calculation
5.1. General framework
Since topic event can represent the semantics of a document, the semantic similarity
between documents can be achieved via the similarity between topic events. In this paper, we
get the information hidden in each document via the topic event, and acquire the internal
relevance among concepts from the domain ontology.

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In our method, to compute the core semantics of papers in brief, six primary elements in
topic event are employed according to their characteristics, which are Target, Domain, Style,
Methodology, Keywords and Date. We use topic event ontology to measure the internal event
similarity between different types, and the domain ontology is used to measure the internal
semantic similarity between terminologies. We measure topic event similarity by the weighted
sum of element similarities in corresponding event structure, and it also can be extended by
metadata and other elements for a more detailed topic event similarity. Similarity between topic
events E1 and E2 is defined in equation (1):
𝑆𝑖𝑚𝑇𝐸𝑠(𝐸1 , 𝐸2 ) = ∑6𝑖=1 𝑊𝑖 × 𝑆𝑖 (𝐿1𝑖 , 𝐿2𝑖 )

(1)

where wi is the weight of the ith element in the topic event, Si is the similarity function
between the ith elements of L1 and L2. L1 and L2 are two topic events whose elements are
defined as L= {Target, Domain, Style, Methodology, Keywords, Date}.
The topic events extract the core semantics of each academic articles. However, the meaning
of event items could not be measured by their literal appearance. Background knowledge such
as lexical meaning is necessary to comprehend the semantics of articles. To get the internal
semantic relatedness among different terminologies, the terminologies in topic events should be
linked to the concept nodes in knowledge bases to obtain their semantic relatedness. In the
following section, we will introduce the concept linking method and the measurement of item
semantic similarity.
5.2. Domain ontology concept linking
To calculate the internal similarity between terminologies, an important issue is to link the
extracted terminologies to proper positions in the knowledge base. There are many synonyms in
academic articles, and many concepts may be described by different terminologies in different
papers such as “cross linguistic retrieval” and “multilingual information retrieval”, “text
understanding” and “message understanding”, “named entity recognition” and “named entity
tagger”. When we construct the domain ontology, we labeled all the known synonyms of each
concept in the node to facilitate entity linking.
Many automatically extracted terminologies wear trivial suffix and prefix, which may
hinder the entity linking. To solve this problem, we use the edit distance to measure the string
similarity and recognize the variations of the same concept. Since our ontology are constructed
for the computational linguistics domain, and all of the concepts nodes are extracted from
domain corpus, most terminologies will find the extract concept position in the domain ontology.
We first derive a terminology list to form the domain ontology. When a terminology extracted
from the academic articles comes, its edit distance with each of the terminology are calculated.
The concept node with minimum edit distance will be regarded as the terminology node.
5.3. Item similarity of topic event
The items such as Target, Methodology and Domain are terminologies, and their semantic
similarities can be measured by the domain ontology. The semantic similarity between Style can
be measured via the research style ontology. The Date similarity can be measured by their
interval. In brief, the similarity of those items in topic events can be measured by the following
methods.

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Research Style Similarity To measure the difference between different types of research
work, the research style ontology described in Table 1 can be used. The Style similarity between
different types of topic events is measured similar to Wu and Palmer method [22] based on the
research style ontology shown in Fig. 3, and the formula is as equation (2):
𝑆𝑖𝑚𝐸𝑡𝑦𝑝𝑒 =

2×𝑑𝑒𝑝𝑡ℎ(𝐿𝐶𝑆)
𝑑𝑒𝑝𝑡ℎ(𝑆𝑡𝑦𝑙𝑒1 )+𝑑𝑒𝑝𝑡ℎ(𝑆𝑡𝑦𝑙𝑒2 )

(2)

where Style1 and Style2 means the types of two topic events. LCS is the least common subsumer
of two style nodes.
Terminology Similarity Concept semantic similarity can be measured by knowledgebase.
We evaluated several knowledge-based methods and found Wu and Plamer [22] method is
suitable for the concept similarity in this domain. The contents of target, Domain, Methodology
and Keywords are collections of terminologies, which can be measured by Wu and Palmer
method based on the domain ontology or word embedding-based methods. The ontology-based
concept semantic similarity is measured by equation
(3):
𝑆𝑖𝑚𝑒𝑐 =

2×𝑑𝑒𝑝𝑡ℎ(𝐿𝐶𝑆)

(3)

𝑑𝑒𝑝𝑡ℎ(𝑒𝑐1 )+𝑑𝑒𝑝𝑡ℎ(𝑒𝑐2 )

where ec1 and ec2 represent terminologies in the topic event.
When the corpus-based concept similarity method is used for terminology semantic
similarity, the cosine similarity between terminology vectors can be used. The cosine similarity
is defined in equation (4):

𝑆𝑖𝑚𝑒𝑐 =

𝑇𝑒𝑟𝑚𝑉𝑒𝑐1 ∙𝑇𝑒𝑟𝑚𝑉𝑒𝑐2

(4)

|𝑇𝑒𝑟𝑚𝑉𝑒𝑐1 ||𝑇𝑒𝑟𝑚𝑉𝑒𝑐2 |

Date Similarity Research issues keep evolving along time, and researchers will focus on
different scientific issues in each period. We assume that the academic articles that have close
date will be more similar, and the academic articles published far from each other will have less
common points. Thus, date similarity can be measured by the time interval. Years and months
are used to compute the similarity between two dates. We define the Date Similarity formula is
as equation (5) .
𝑆𝑖𝑚𝐷𝑎𝑡𝑒 =

1
𝑚𝑜𝑛𝑡ℎ1
𝑚𝑜𝑛𝑡ℎ2
)−(𝑦𝑒𝑎𝑟2 +
)|
12
12

1+|(𝑦𝑒𝑎𝑟1 +

(5)

6. Experimental evaluation
6.1. Corpora construction
There are several public datasets used to evaluate the semantic similarity of short texts and
sentences, such as MSPR [3], Michael D.LEE 50 corpus [7] and SEMILAR corpus [5]. However,
no text of those datasets is more than 200 words, which could not validate the document-level
semantic similarity. Hence, we construct the semantic similarity dataset between documents

12

using the academic papers in computational linguistics domain. A set of paper pairs are
generated from AAN corpus [20]. The paper pairs are annotated by both a 2-level and by a 5level annotation as ground truth. Each paper pair is marked as 1 if semantically similar or 0 if
dissimilar in 2-level annotation. In 5-level annotation a paper pair is marked by integers ranging
from 1 to 5 according to their semantic similarity degree. If they are totally equal in semantic,
similarity between papers will be annotated as 5, and if they have nothing to do with each other,
similarity is annotated as 1. Twelve experts from our lab annotated and cross validated the
coherence of 1021 pairs of documents. Each paper pair get a second annotation by a different
person after the first annotation. If the second annotation is in accordance with the first one, it
will be annotated as the ground truth, else a third person will annotate the paper pair to get the
ground truth. In the end we got an annotated corpus with 1021 paper pairs. Now the corpus is
public, and the url is: https://github.com/buaaliuming/DSAP-document-semantics-foracademic-papers/tree/buaaliuming-annotation .
6.2. Computational linguistics ontology
Common knowledge resources such as WordNet couldn’t cover domain terminologies. In
order to compute semantic similarity between terminologies, we manually construct the domain
ontology to convey the semantics among different terminologies.

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Research
Issue
is-a
Research
Topics

is-a

Research
Object

is-a

Infrastructure is-a

Automatic
summarization

is-a

is-a Computational
Semantics

is-a

...

is-a
is-a

...

...

is-a

Fundamental
process

is-a

is-a

is-a

is-a

Computational
is-a
Linguistics

Information
Retrieval

Knowledge
Base

is-a

Information
Extraction

is-a

Machine
Translation

is-a

Question
Answering

is-a

Text
Category

is-a

Grammar
Checker

is-a

...

is-a

Academic
Organziation

...

is-a
is-a

Corpora

Toolkits
is-a

...

Hybrid
method
is-a

General
Approach

……

Statistical
Theory

is-a
is-a

Pattern
Matching

is-a

is-a
is-a

Knowledgebased Method

...

is-a

...

Ensemble
methods

is-a

Unsupervised
learning

is-a

...

Supervised
learning

is-a

...

Semi
supervised
learning

is-a

Reinforcement
learning

is-a

is-a
Heuristic
Method

is-a

is-a
Machine
Learning

is-a

...

Fig. 6. Main structure of computational linguistic ontology
The Concepts extracted from AAN corpus [20] are used to manually construct a
computational linguistics ontology. Currently our ontology includes 1195 concept nodes with a
hierarchy of 9 depths, which will be expanded persistently in future. The Computational
Linguistics Ontology (CL Ontology) architecture is shown in Fig. 6. The main relationships
between concepts in the ontology are hyponymy. Synonyms are considered and annotated in the
ontology concept nodes during the construction.
CL ontology is used to measure the similarity between computational linguistic domain
concepts. According to the characteristic of computational linguistic domain, we design the
ontology as three parts, they are: Research Topic, Infrastructure and General Approaches, and
each part is enriched by more detailed descendant nodes. The node General Approach includes
common methodologies used in computational linguistics, such as machine learning, pattern
matching and knowledge engineering, etc. The node Research Topic includes fundamental
language processes, research issues and research objects. Fundamental language process
includes natural language processing, such as word segmentation, syntactic parsing, POS
tagging, lemmatization, etc. Research issues include research hotspots such as machine
translation, text category, information extraction, information retrieval and speech recognition,

14

etc. The node Infrastructures contains the general toolkits, knowledge bases, corpora, and
organizations in the field of computational linguistics.
6.3. Experimental setup
The experiments are conducted on the DELL OptiPlex390 machine, which has 8G memory
and an I5-2400 CPU. Besides automatic constructed topic events, we also manually annotate the
corresponding topic events of academic papers for experimental contrast. The LDA-based
method is chosen as the baseline method. When computing the semantic similarity between
terminologies in topic events, the LSA method is used as well as the ontology-based method. In
brief, we conduct the following methods.
LDA_2013 The LDA-based method [36] in 2013 is the most related research and is chosen
as the baseline method for contrast. When performing the LDA-based method, several LDA
models with different parameters are trained based on the AAN corpus. In the following
presented results, we choose the LDA model with 200 topics for contrast, which achieves the
best performance among different LDA models under the same conditions.
TE_Onto the TE semantic similarity method are conducted on the golden topic event
annotations, and the concept semantic similarities are computed by our CL ontology.
AutoTE_Onto the TE semantic similarity method are conducted on the automatic extracted
topic events, and the concept semantic similarities are also computed by our CL ontology. The
AutoTE_Onto method is used as the comparison of TE_Onto method to measure the influence
of automatic topic event extraction.
TE_LSA the TE semantic similarity method are conducted on the golden topic event
annotations, and the concept semantic similarities are computed by the word vectors produced
by LSA. The LSA method derives each word representation by SVD operation, and the semantic
similarity between terminologies is captured by the similarity of the common topics. When the
LSA method is used in the calculation of TE item, we constructed term-by-document matrix
over the whole annotated corpus. The TE_LSA method is used as the comparison of the
TE_Onto method to measure the effect of the Computational Linguistics Ontology.
AutoTE_LSA the TE semantic similarity method are conducted on the automatic extracted
topic events, and the concept semantic similarities are also computed by the word vectors
produced by LSA.
TE item weights The primary items of the topic event are Target, Domain, Methodology,
Style, Keywords and Date, which are the essential items in Fig. 2. According to our experience,
the research target is the crucial issue in each academic article. The research domains, types of
research work and adopted methods in academic articles are important aspects, which are
discriminative characteristics of each research work. While the keywords are a set of fuzzy
description lack of definite semantics. The dates of publication imply the different ages of
technology, which are less discriminative and not directly related to various research work. We
set the weight of aforementioned items according to their importance, and in our experiments,
weights of the items, i.e. Target, Domain, Style, Methodology, Keywords and Date are set to 0.3,
0.25, 0.25, 0.1, 0.05 and 0.05 respectively.
Evaluation metrics We choose the Pearson's correlation to measure the quality of semantic
similarity scores. The larger Pearson's correlation is, the more correlated the predicted scores
and the ground truth are. The Pearson's correlation is shown in equation (6).
ρ𝑋,𝑌 =

𝑐𝑜𝑣(𝑋,𝑌)
𝜎𝑋 𝜎𝑌

=

𝐸((𝑋−𝜇𝑋 )(𝑌−𝜇𝑌 ))

(6)

𝜎𝑋 𝜎𝑌

15

In this work, X is the predicted semantic similarity score, and Y indicates the annotated
semantic similarity value. The 𝑐𝑜𝑣(𝑋, 𝑌) represents the covariance of X and Y. The 𝜇𝑋 and 𝜇𝑌
represent mean values of the variables X and Y; 𝜎𝑋 and 𝜎𝑌 are standard deviations of X and Y
respectively.
Since our corpus has both binary annotations and five-level annotations, we further set
different thresholds to predict whether two documents are semantic similar. Both the Accuracy
and F1-score can be the overall evaluation metric beside correlation. The Accuracy is shown it
equation (7).
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =

(𝑇𝑃+𝑇𝑁)

(7)

(𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁)

In equation (7), TP means the number of document pairs predicted to be similar that actually
are similar document pairs. TN means the number of document pairs predicted to be dissimilar
that actually are dissimilar. FP is the number of document pairs predicted to be similar that are
actually dissimilar FN is the number of document pairs predicted to be dissimilar that are actually
similar.
Accuracy means the general predict ability of a method, and F-score means the
comprehensive performance of precision and recall. The F-score is shown it equation (8).
𝐹 𝑠𝑐𝑜𝑟𝑒 =

(1+𝛽)𝑃

(8)

(𝛽𝑃+𝑅)

The P means Precision and the R means Recall. In general, the value of β is 1, and the F
score is annotated as F1-score in the following section.
6.4. Results and discussions
6.4.1. Pearson correlation
To verify the quality of different methods, we calculate the Pearson's correlations of 1021
similarity scores with the human annotated ground truth.
Comparison with the baseline The results in Table 4 show that our ontology-based TE
method has distinct advantage on the baseline method, i.e. LDA_2013. The 5-level annotation
has more detailed similarity levels; and the correlation scores with it are more convincing than
the 2-level correlation scores. Our TE_Onto method achieves 4.1% (relative) improvement over
the baseline method; when the topic events are extracted automatically, our AutoTE_Onto
method can gain 5.8% (relative) improvement over the baseline method.
Table 4 Pearson's correlations with ground truth
5-level Annotation 2-level Annotation
Correlations
Correlation
Correlation
TE_Onto
0.559
0.461
AutoTE_Onto
0.568
0.456
TE_LSA
0.480
0.346
AutoTE_LSA
0.463
0.327
LDA_2013
0.537
0.250

16

The impact of ontology Our TE_Onto method performs much better than the LSA-based
TE method ， and the AutoTE_Onto even shows 22.7% advantage over the AutoTE_LSA
method when topic events are extracted automatically.
The LSA-based method measures the semantic similarity of concepts by common word
topics or word co-occurrence, while the ontology-based methods measure the concept semantic
similarity via accurate knowledge in ontology. Above results show that the knowledge resource
such as the domain ontology are of great importance in the measurement of document semantics.
The impact of TE extraction The correlation scores of our methods on automatic
constructed topic events and human annotated topic events are close. The performance of our
methods with automatic extracted topic events is comparable to the golden topic events
annotated by experts. The TE_LSA method only shows minor advantage on the AutoTE_LSA
method, and the AutoTE_Onto method even performs little better than the TE_Onto method in
5-level annotation correlation. This shows that, the pattern-based extracting method can extract
necessary information with proper precision in a specific domain, and the process of automatic
extraction produces comparable performance to human annotation.
6.4.2. Accuracy and F1-score
Accuracy and F1-score can be the general evaluation metrics beside Correlation. The
Accuracy indicates the general capability of a method to predict the right result; the F1-score is
a balance of precision and recall, which indicates the comprehensive capacity of the method. In
practical applications, different thresholds can be set to predict whether two documents are
semantic matching. Generally, the best performance among different thresholds is considered as
an important factor in evaluation. In the following experiments, different thresholds are set.
Paper pairs are thought semantically similar if their similar score is greater than the given
thresholds.
Comparison with the baseline As shown in Fig. 7 and Fig. 8, the Accuracy of our TE
methods always has distinct advantage over the LDA_based method at different thresholds.
Our best F1-score is 0.639, while the best F1-score of the baseline method is 0.536. The F1score of our AutoTE_Onto method outperforms the LDA_2013 at most thresholds, and the F1scores of our TE_Onto methods with golden topic event show advantage over the baseline
method when the threshold is less than 0.75.
0.9
0.8
0.7

Accuracy

0.6
0.5
0.4
0.3
0.2
0.1
0

TE_Onto
0.05

0.1

0.15

0.2

0.25

AutoTE_Onto
0.3

0.35

0.4

0.45

0.5

LDA_2013
0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

Threshold

Fig. 7. Comparison of accuracy of our methods and the baseline method

17

0.7
0.6

F1-score

0.5
0.4
0.3

TE_Onto

0.2

AutoTE_Onto

0.1
0

LDA_2013
0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

Thresholds

Fig. 8. Comparison of F1 of our methods and the baseline method
The impact of ontology We compare the performance of LSA-based TE method and the
Ontology-based TE method. As shown in Fig. 9 and Fig. 10, the best Accuracy of LSA-based
TE method is 0.712, while the best Accuracy of Ontology-based TE method is 0.768. The
Ontology-based TE methods perform much better than the LSA-based TE methods when
thresholds are more than 0.250. To sum up, the ontology-based TE methods perform better than
LSA-based TE methods, which is in accordance with the result of the Pearson Correlation in the
last subsection. Those results show that the ontology plays a crucial role in the document
semantic similarity.
0.9
0.8
0.7

Accuracy

0.6
0.5
0.4
0.3
0.2
0.1
0

TE_Onto
0.05

0.1

0.15

0.2

TE_LSA
0.25

0.3

0.35

AutoTE_Onto
0.4

0.45

0.5

0.55

AutoTE_LSA
0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

Threshold

Fig. 9. Comparison of accuracy of Ontology-based TE methods and LSA-based TE
methods

18

0.7
0.6
0.5

F1-score

0.4
TE_Onto

0.3

TE_LSA

0.2
AutoTE_Onto

0.1
0

AutoTE_LSA
0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

Thresholds

Fig. 10. Comparison of F1 of Ontology-based TE methods and LSA-based TE methods
Discussion Above experiments demonstrate that TE methods can yield much better
performance in general. The similarity scores of TE_LSA method and AutoTE_LSA method
range from 0.0 to 0.70, and there is no true positive result when the thresholds are above 0.70.
Those low level similarity scores make the F1-scores of LSA-based TE method incalculable.
Academic articles usually have long contents containing lots of duplicate terminologies, and the
frequent terminologies in a specific domain trend to occur in many domain articles. Besides,
academic papers tend to review related work. The LDA-based method determines the topic
similarity by the word overlap. Hence, the similarity scores of LDA_2013 ranges relatively high
from 0.7 to 1.0, which makes it perform good in recall but poor in precision, and depress its
overall performance. Our methods hold the core semantics of a document directly via structured
topic event, the similarity scores of TE methods fluctuate from 0.0 to 1.0 according the similarity
of document pairs, and they are more discriminative and have better overall performance.
6.4.3. Time and memory cost
Each method in our experiment needs an off-line process. It’s hard to measure and compare
the off-line process of those costs in different environments and procedures. In this section we
measure the running time when computing the document-level semantic similarity under the
same condition. The average consuming time of TE methods is 0.002s and the memory
occupation is about 100M, while the LDA_2013 costs 4.83s and occupies more than 8G
memories. It’s obvious that our TE methods are more efficient than the traditional LDA-based
measurement in time and memory cost. The reason is that our TE methods utilize a domain
ontology to calculate the semantic similarity rather than a large LDA model. In general, our TE
Methods that achieve semantic similarities based on extraction and template get desirable overall
performance.

7. Conclusions
This paper proposes for the first time constructing a topic event to represent the semantics
of a document and measures the semantic similarity between the academic literatures by
computing the similarity of their corresponding topic events. We define the general architecture
of topic event and give the construction and similarity computing methods of topic events. The

19

research style ontology and an evaluation corpus are constructed. To measure the concept
semantic similarity, we design the Computational Linguistics Ontology. The evaluation
experiments show that our topic event method gets significantly improvement on current
semantic similarity measurements, and the ontology-based TE methods show overall advantages
in Correlation, Accuracy and F1-score. The knowledge resources such as the domain ontology
play a crucial role in the document semantic similarity.
Furthermore, our method can be used for modeling the semantics of different styles of
research work, and the ontology-based automatic construction and similarity calculation of TE
are concordant across different domains, which means our method could be easily utilized in
various academic domains.

Acknowledgments
This research was supported by the Foundation of the State Key Laboratory of Software
Development Environment (Grant No. SKLSDE-2015ZX-04). We thank the reviewers for their
valuable feedback, which help to improve the paper.

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