=Paper= {{Paper |id=Vol-1988/LPKM2017_paper_15 |storemode=property |title=A Survey of Textual Event Extraction from Social Networks |pdfUrl=https://ceur-ws.org/Vol-1988/LPKM2017_paper_15.pdf |volume=Vol-1988 |authors=Mohamed Mejri,Jalel Akaichi |dblpUrl=https://dblp.org/rec/conf/lpkm/MejriA17 }} ==A Survey of Textual Event Extraction from Social Networks== https://ceur-ws.org/Vol-1988/LPKM2017_paper_15.pdf

A Survey of Textual Event Extraction from Social
Networks
1 1
Mohamed MEJRI and Jalel AKAICHI

Institut Supérieur de Gestion de Tunis

Abstract. In the last decade, mining textual content on social networks
to extract relevant data and useful knowledge is becoming an omnipresent
task. One common application of text mining is Event Extraction, which
is considered as a complex task divided into multiple sub-tasks of varying
diculty. In this paper, we present a survey of the main existing text mining
techniques which are used for many dierent event extraction aims. First,
we present the main data-driven approaches which are based on statistics
models to convert data to knowledge. Second, we expose the knowledge-
driven approaches which are based on expert knowledge to extract knowledge
usually by means of pattern-based approaches. Then we present the main
existing hybrid approaches that combines data-driven and data-knowledge
approaches. We end this paper with a comparative study that recapitulates
the main features of each presented method.
Key-words: Event Extraction, Text Mining, Information Extraction, So-
cial Network.

1 Introduction
Social Networks are dened as web-based systems (dedicated websites or other ap-
plication) that allow users (individuals) to create public or semi-public proles and
communicate with each other, within the internet network, by posting information,
comments, messages, videos, etc. [4, 8].
In recent years, Social networks have become omnipresent because of the increasing
propagation and aordability of internet enabled devices such as personal com-
puters, smart phones, tablets and many other devices that allow users to connect
to social networks through the internet services [3]. These new possibilities allow
people from everywhere and anytime to add, update, share and consult massive
quantities of new information in real time. These huge quantities of new informa-
tion added by hundreds of millions of active users [26] are considered as a very
important source of data for many research elds.
These massive quantities of data are characterized by three computational issues:
size, noise and dynamism [4]. These issues make manual analysis of social network
data seems to be impossible. To remedy this problem, data mining provides a wide
range of techniques for detecting useful knowledge from massive datasets. Most of
data social network is initially unstructured and habitually described using human
natural language, which makes the understanding and interpretation of social net-
work content by machine a dicult task [6]. This problem impedes the automation
of Text Mining (TM) sub-tasks such as Information Retrieval [13]and Information
Extraction (IE) [15] processes which are frequently used in the decision making.
In general, we can dene Text Mining (TM) as the analysis of data contained in
natural language text. TM works by transposing words and phrases in unstruc-
tured data into numerical values which can then be linked with structured data in
a database and analyzed with traditional data mining techniques [40]. By means of
text mining, often using Natural Language Processing (NLP) techniques, informa-
tion is extracted from texts of various sources, such as news messages and blogs, and
is represented and stored in a structured way, (generally in databases). A specic
2

type of knowledge that can be extracted from text by means of TM is an event,
which can be represented as a complex combination of relations linked to a set of
empirical observations from texts [17]. Event Extraction (EE) from textual content
of social network has gained remarkable attention in the last few years. For ex-
ample, this representation presents an attack event.
Words identied in text referring to persons are linked to the concept ;
verbs having the meaning of attack are associated with . Thus, a similar
event representation can be detected from texts such as: John shot his friend, A
woman was attacked by a stranger. Etc.

Saval et al. [33] proposed a semantic extension for the modeling of events type
"natural disasters". They dene an event E as the combination of three components:
a semantic property S, a time interval I, and a spatial entity SP. Thus, an event
is represented as follows: E < I; SP; S>. In their work of 2014, Serrano et al.
[34] adapted this event representation by enriching it with an additional component
A corresponding to the dierent participants involved in the event. Thus, the
where A is a set of participants
representation will be as follows:
playing one or more role(s). A participant noted Pi wherein 0 < i < n, and
a role noted rj wherein 0 < j < k . Component A is then dened as follows:
A = {(P α, rβ)} as the participant P α plays the role rβ in the concerned event.
Event extraction from unstructured textual content could be useful for IE systems
in various ways. In fact, being able to detect and recuperate events could enhance
the quality and performance of personalized systems [14]. Therefore, the use of
extracted events form textual content of social networks to deal with several issues
is becoming an unavoidable task. However, Extracting events is a very dicult task
divided to many sub-tasks with dierent complexities and need the combination of
many techniques and methods depending on the treaty task.
In this paper, we present a survey of the main existing approaches in literature for
EE. In the rst section, we present the data-driven event extraction approaches,
which are based on methods relying on statistics to convert data to knowledge,
then, we expose the main knowledge-driven approaches which extract knowledge
through representation and exploitation of expert knowledge, usually by means of
pattern-based approaches. The last part of the rst section will be devoted to the
presentation of dierent hybrid methods based on the combination of data-driven
and knowledge-driven approaches. In section 3, we present a quick overview of the
main multilingual event extraction systems used in the recent literature. In the
third section, we discuss the main existing works that combine event extraction and
risk management. And we end this papers with a comparative study in which we
demonstrate the main dierences, advantages and disadvantages for each approach.

2 Event extraction from textual content
In the available annotated corpora geared toward information extraction, we see
two models of events, emphasizing these dierent aspects. On the one hand, there
is the TimeML model, in which an event is a word that points to a node in a net-
work of temporal relations. On the other hand, there is the ACE model, in which
an event is a complex structure, relating arguments that are themselves complex
structures, but with only ancillary temporal information (in the form of temporal
arguments, which are only noted when explicitly given). In the TimeML model,
every event is annotated, because every event takes part in the temporal network.
In the ACE model, only interesting events (events that fall into one of 34 prede-
ned categories) are annotated. The task of automatically extracting ACE events is
more complex than extracting TimeML events (in line with the increased complex-
ity of ACE events), involving detection of event anchors, assignment of an array of
3

attributes, identication of arguments and assignment of roles, and determination
of event coreference.
Events in the ACE program
The ACE program1 provides annotated data, evaluation tools, and periodic eval-
uation exercises for a variety of information extraction tasks. There are ve basic
kinds of extraction targets supported by ACE: entities, times, values, relations, and
events. The ACE tasks for 2005 are more fully described in [1].
ACE entities fall into seven types (person, organization, location, geo-political en-
tity, facility, vehicle, weapon), each with a number of subtypes. Within the ACE
program, a distinction is made between entities and entity mentions (similarly be-
tween event and event mentions, and so on). An entity mention is a referring
expression in text (a name, pronoun, or other noun phrase) that refers to some-
thing of an appropriate type. An entity, then, is either the actual referent, in the
world, of an entity mention or the cluster of entity mentions in a text that refer to
the same actual entity. The ACE Entity Detection and Recognition task requires
both the identication of expressions in text that refer to entities (i.e., entity men-
tions) and coreference resolution to determine which entity mentions refer to the
same entities.
ACE events, like ACE entities, are restricted to a range of types. Thus, not all
events in a text are annotatedonly those of an appropriate type. The eight
event types (with subtypes in parentheses) are Life (Be-Born, Marry, Divorce,
Injure, Die), Movement (Transport), Transaction (Transfer-Ownership, Transfer-
Money), Business (Start-Org, Merge-Org, Declare-Bankruptcy, EndOrg), Conict
(Attack, Demonstrate), Contact (Meet, Phone-Write), Personnel (Start-Position,
End-Position, Nominate, Elect), Justice (ArrestJail, Release-Parole, Trial-Hearing,
Charge-Indict, Sue, Convict, Sentence, Fine, Execute, Extradite, Acquit, Appeal,
Pardon). Since there is nothing inherent in the task that requires the two levels of
type and subtype, for the remainder of the paper, we will refer to the combination
of event type and subtype (e.g., Life:Die) as the event type. In addition to their
type, events have four other attributes (possible values in parentheses): modality
(Asserted, Other), polarity (Positive, Negative), genericity (Specic, Generic), tense
(Past, Present, Future, Unspecied).
The most distinctive characteristic of events (unlike entities, times, and values, but
like relations) is that they have arguments. Each event type has a set of possible
argument roles, which may be lled by entities, values, or times. In all, there are 35
role types, although no single event can have all 35 roles. A complete description
of which roles go with which event types can be found in the annotation guidelines
for ACE events [38]. Events, like entities, are distinguished from their mentions in
text. An event mention is a span of text (an extent, usually a sentence) with a
distinguished anchor (the word that most clearly expresses [an event's] occurrence
[38]) and zero or more arguments, which are entity mentions, timexes, or values in
the extent. An event is either an actual event, in the world, or a cluster of event
mentions that refer to the same actual event. Note that the arguments of an event
are the entities, times, and values corresponding to the entity mentions, timexes,
and values that are arguments of the event mentions that make up the event. The
ocial evaluation metric of the ACE program is ACE value, a cost-based metric
which associates a normalized, weighted cost to system errors and subtracts that
cost from a maximum score of 100%. For events, the associated costs are largely
determined by the costs of the arguments, so that errors in entity, timex, and value
recognition are multiplied in event ACE value. Since it is useful to evaluate the
performance of event detection and recognition independently of the recognition of
entities, times, and values, the ACE program includes diagnostic tasks, in which
partial ground truth information is provided. Of particular interest here is the di-
agnostic task for event detection and recognition, in which ground truth entities,
4

values, and times are provided.

According to ACE terminology, event trigger is the word that determines the
event occurrence; argument is an entity mention, a value or a temporal expression
that constitutes event attributes and event mention is an extent of text with the
distinguished trigger, entity mentions and other argument types [15].
As mentioned above, event extraction is a complex task divided on many sub-
tasks; therefore, many techniques for event extraction from textual content exist
in literature. As will be shown in this paper, the choice of suitable techniques is
based on the nal requirements of each extraction task. In this section, we present
a survey on the main methods and approaches sued in recent literature: the data-
driven approaches, knowledge-driven approaches and the hybrid approaches, we end
this section by a comparative study that recapitulating the main features, elds of
application, advantages and disadvantages of each approach.

2.1 Data-driven approaches for event extraction

In contrast to pattern-based approaches (which are presented in section 2.2), data-
driven approaches automatically build models for a particular NLP tasks (i.e. to
automated language processing) with no human intervention. In other words, these
approaches try to discover statistical relations through the use of only quantitative
methods such as probabilistic modeling, information theory, and linear algebra. So,
to develop these models that approximate linguistic phenomena, data-driven meth-
ods necessitate a large text corpora, which is why these techniques often are called
corpus-based. Examples of discovered facts are words or concepts that are (statisti-
cally) associated with one another. In recent literature, many techniques associated
to data-driven approaches could be used such as: word frequency counting, Term
Frequency - Inverse Document Frequency (TF-IDF), word sense disambiguation
(WSD), n-grams, and clustering.

One common task in data-driven approaches for event extraction from text is the
Part-of-Speech (POS) tagging which is the process of assigning a part-of-speech to
each word in a sentence. In their work of 2006, Guy et al [11] elaborated on a com-
parison between four data-driven taggers (TnT, MBT, SVMTool and MXPOST).
The experiments obtained through the application of these data-driven taggers on
a given dataset (the annotated Helsinki Corpus of Swahili) shows that MXPOST
as being the most accurate tagger for this dataset. In another set of experiments,
they further improved on the performance of the individual taggers by combining
them into a committee of taggers. Likewise, the obtained results showed that com-
bining many taggers may enhance the performance and accuracy of system. In the
same eld and to deal with for morphologically complex languages Mark et Joel
[12] extended a statistical tagger to handle ne grained tagsets and improve over
the best Icelandic POS tagger. Additionally, they develop a case tagger for non-
local case and gender decisions. Delia et al. [31] investigated dierent unsupervised
techniques for extracting and clustering complex events from news articles. As a
rst step they proposed two complementary event extraction algorithms, based on
identifying verbs and their arguments and shortest paths between entities, respec-
tively. Next, they obtained more general representations of the event mentions by
annotating the event trigger and arguments with concepts from knowledge bases.
The generalized arguments were used as features for a clustering approach, thus
determining related events.
In their work of 2014, Deyu et al [41] elaborated on a simple Bayesian modeling
approach to event extraction from Twitter, called Latent Event Model (LEM), to ex-
tract structured representation of events from social media. However, the proposed
5

approach is fully unsupervised and does not require annotated data for training.
So, the proposed model only requires the identication of named entities, locations
and time expressions. After that, the model can automatically extract events which
involving a named entity at certain time, location, and with event-related keywords
based on the co-occurrence patterns of the event elements. Okamoto et al. [27]
presented a method for the detection of occasional or volatile local events using
topic extraction technologies. They elaborate on a framework based on a two-level
hierarchical clustering method. The resort to clustering techniques gave accept-
able results with a good accuracy for event extraction. Liu et al. [5] presented
a framework for simultaneous key entities extraction and signicant events mining
from daily web news based on clustering, modeling entities and weighted undirected
bipartite graph. In the same led, the authors of [37] developed a real-time news
event extraction system based on automatic pattern learning from a small anno-
tated corpus and in order to guarantee that massive amounts of textual data can
be digested in real time, they have developed ExPRESS (Extraction Pattern En-
gine and Specication Suite), a highly ecient extraction pattern engine, which is
capable of matching thousands of patterns within seconds. In [24], Lei et al. pre-
sented a framework for extracting and tracking topic relevant event based on SVM
algorithm.
The use data-driven approaches for event extraction give a main advantage: there
is no need to expert knowledge or linguistic resources. However, data-driven ap-
proaches require large text corpora in order to develop models that approximate
linguistic phenomena. Another drawback is that data-driven methods do not deal
with the meaning of text. To remedy this problem, researchers resort to knowledge-
driven approaches which are based on patterns that express rules representing expert
knowledge.

2.2 Knowledge-driven approaches for event extraction

Also known as Rule-Based methods, knowledge-driven methods are commonly based
on patterns constructed by linguists. Patterns consist of lexically specied syntactic
templates that are matched to text, in much the same way as regular expressions,
which are applied along with type constraints on substrings of the match. These
patterns are lexically indexed local grammar fragments, annotated with semantic
relations between the various arguments and the knowledge representation [39]. So,
these rules or patterns are relying on linguistic knowledge about the structure of
language and written in a formal notation so that they used by the computer for
further parsing [25]. The design of patterns (that may be lexico-syntactic or lexico-
semantic pattern) and the choose of appropriate techniques are generally depends
on many factors such as the language of the text that is to be processed and the
nal purpose of processing. For the lexico-syntactic case, patterns combine lexi-
cal and syntactical information [22] while for the case of lexico-semantic patterns
are employed by the addition of semantic information generally through the use of
gazetteers [19] or ontologies [20].

Lexico-syntactic patterns

As we mention before, lexico-syntactic patterns is a combinations between lexi-
cal representations ( i.e., strings) and syntactical information (e.g., Part-Of-Speech).
For further clarication, we present the following lexico-syntactic pattern given by
Hearst in his work of 1998 [16]:

such NP as {NP,}∗ {(or | and)} NP
6

Where he aimed to nd hyponym and hypernym relations by discovering regu-
lar expression patterns in free text. In this pattern, NP indicates a proper noun.
Other text (i.e., such, as, or, and and) is used for lexical matching, while (
and ) contain conjunction and disjunction statements to be evaluated, in this case
a disjunction (denoted as |). Also, ∗ is a repetition parameter that indicates the
sequence between braces ( and ) is allowed to repeat zero to an innite number
of times. Apply this lexico-syntactic pattern on this sentence . . . works by such
authors as Herrick, Goldsmith, and Shakespeare gives the following results:
hyponym("author", "Herrick")
hyponym("author", "Goldsmith")
hyponym("author", "Shakespeare")

These patterns are often easy to comprehend by regular users, yet dening the
right patterns to mine corpora to obtain unknown information is not a trivial task.
Hearst stresses that, in order to return desired results successfully, patterns should
be dened in such a way that they occur frequently and in many text genres. Also,
they should often indicate the relation of interest and should be recognizable with
little or no pre-encoded knowledge. Furthermore, all existing syntactic variations
have to be included into a complex pattern to ensure its proper working.

Lexico-semantic patterns

Lexico-semantic patterns are employed to remedy problems of the absence of
concepts that have specic meaning (mean by the use of lexico-syntactic patterns).
In addition to the combination of lexical representations and syntactical informa-
tion used by lexico-syntactic patterns, lexico-semantic patterns also permit for the
usage of semantic information such as concepts that are dened in ontologies. So,
Lexico-semantic patterns combine lexical representations with syntactic and seman-
tic information. Lexico-semantic patterns are rst presented by [21] in their work
of 1991, where they made a system for text processing based on lexico-semantic
patterns. These patterns could include terms and operators like lexical features,
logical combinations, and repetition, which are mostly adopted from the regular
expression language.
The following example is given by Wooter el al [7] is a lexico-semantic pattern that
will classify the verb phrase left dead as to express death or injury:
?PIVOT = (or found left shot)
?OBJ =∗ ?EFFECT=dead
=> (mark-activator
murder d-vp) ;

This sentence would also match found dead and shot dead. Next to standard
elements such as repetition and wildcards, the rule presented here contains features
like variable assignment on the left-hand side (LHS) (where words preceded by ?
denote variables) and on the right-hand side (RHS) macros such as mark-activator,
which uses the results of the pattern match, including variable assignments, along
with some other constants, such as murder and d-vp, to tag and segment the
text. The use of lexico-semantic patterns gives many advantages, the most impor-
tant is that they take into account the domain semantics which help the parser
cope with the complexity and exibility of unstructured text written with natural
language [16].

In the current body of literature, many works based on knowledge-driven ap-
proaches for event extraction exists. For instance, in their work of 2012, Wooter et
al [19] proposed a rule-based method to learn ontology instances from text, where
7

they dened a lexico-semantic pattern language that, in addition to the lexical and
syntactical information present in lexico-syntactic rules, also makes use of semantic
information.
In [16], authors proposed the use of lexico-semantic patterns for extracting nancial
events from RSS news feeds in order to allow investors on nancial markets to mon-
itor nancial events when deciding on buying and selling equities. These patterns
use nancial ontologies, leveraging the commonly used lexico-syntactic patterns to a
higher abstraction level, thus enabling lexico-semantic patterns to recognize increas-
ingly precisely events than lexico-syntactic patterns from text. For that, authors
have developed rules based on lexico-semantic patterns used to nd events, and se-
mantic actions that allow for updating the domain ontology with the eects of the
discovered events. There, pattern creation was based on the triple paradigm (i.e.,
it makes use of a subject, a predicate, and an optional object), and that relies on
triple conversion to the Java Annotations Pattern Engine
1 (JAPE) language [10]
2
and SPARQL [2]. Another work for economic event extraction is also presented
for the same authors [18], in which they proposed a semantic-based information ex-
traction pipeline for economic event detection, which makes use of lexico-semantic
patterns that are dened in the JAPE language. Other works in the same eld
could be found in [35], [36].

The resort to knowledge-driven approaches has alleviated many problems g-
ured in case of data-driven approaches. The rst issue xed by the employ of
knowledge-driven approaches is that we don't need to use a huge amount of training
data (text corpora demanded by data-driven approaches) to develop models that
approximate linguistic phenomena. The second important advantage is that the
remedy to knowledge-driven approaches oers the possibility to rely on a combina-
tion of lexical, syntactical and semantic elements to dene powerful patterns which
can be used to extract and recognize very specic information. Nevertheless, one
common negative point concerns knowledge-driven approaches is that prior domain
knowledge is required, so we need to ask for expert linguist help, in other words, ,
in order to be able to dene patterns that retrieve the correct, desired information,
lexical knowledge and possibly also prior domain knowledge is required. Also, the
resort only to knowledge-driven approaches may cause troubles and returns weak
results especially when we need to recognize a big number of events.

2.3 Hybrid approaches for event extraction

Staying within the limits of one type of event extraction approaches may not give
the best results. So, combining data-driven approaches with knowledge-driven ones
possibly will alleviate drawbacks of each kind and this actually creates a new kind
of approaches: the hybrid approaches. In practice, it's dicult to rely only on one
kind of event extraction approaches. Therefore, the majority of works in the re-
cent literature relies on hybrid approaches. Generally, and during the application
of hybrid approaches, data-driven approaches are generally used for the statistical
processing (bootstrapping, POS tagging, initial clustering, etc) while knowledge-
driven approaches are used for dening powerful expressions generally by means
of lexical, syntactical and semantic elements [29]. In other words, data-driven ap-
proaches used to deal with huge amount of data while knowledge-driven used to
deal with specic meaning aims.
Kenji et al. [32] presented an approach to combine rule-based and data-driven NLP

1
https://gate.ac.uk/sale/tao/splitch8.html
2
http://www.w3.org/TR/rdf-sparql-query/
8

techniques in the extraction of grammatical relations. They have shown that start-
ing with a rule-based system, we can use unlabeled data and a corpus-based system
to improve recall (and F-score) of grammatical relations. In their work of 2004,
Camiano et al. [9] elaborated on a hybrid approach to resolve issues caused by the
lack of expert knowledge, so they resort to statistical methods to remedy these is-
sues. Pakhomov et al. [28] combined statistical methods with lexical knowledge. A
similar orientation could be found in [30] in this case, authors used hybrid approach
to reinforce statistical methods. The authors of [29] bootstrap a weakly supervised
pattern learning algorithm with clusters, in order to extract violence incidents from
online news with high precision and recall, and storing these in knowledge bases.
The authors of [23] employ a grammar-based statistical method to text mining, i.e.,
POS tagging. However, tagging is based on domain knowledge that is stored in
ontologies, thus making the event extraction a hybrid process. Finally, Chun et al.
[15] extract events from biomedical literature by means of lexico-syntactic patterns,
combined with term co-occurrences.

The combination of data-driven approaches with knowledge-driven ones bring
several enhancements. For instance, and even still need a big amount of data to
develop statistical models, the required amount of data in hybrid approaches is less
than in the case of purely data-driven approaches. The same, the required amount
of developed patterns by experts for detecting events is less than purely knowledge-
driven approaches and this is due to the resort to statistical methods to discover
events automatically. Drawbacks are generally caused by the complexity of hybrid
systems which encompasses many techniques and methods of data-driven and data-
knowledge approaches.

3 Discussion
In this section, we summarized the dierent discussed approaches and methods in
a table (Table 1), in which we tried to expose the main dierences between each
approach. To do so, we listed, the dierent techniques used for each approach (Data-
driven or knowledge driven approaches) then the used methods for each approach
(hierarchical, graphs, SVM. . . ) and the dierent types of events. We presented,
also, the amount of required data needed for each approach and nally the required
domain knowledge and expertise). As shown in Table 1, in term of data usage,
knowledge driven based approaches require fewer amounts of data. Experiments
shows that we need only couple hundreds of documents or sentences to generate
valuable and accurate event extraction rules. On the other hand, data-driven ap-
proaches require more than ten thousands documents to build useful statistical
models that give acceptable results. For the hybrid approaches that combine data-
driven and knowledge-driven methods, the amount of required data still elevated
but it's much better than the case of Data-driven approaches, where we rely solely
on statistical techniques to extract rules. For the interpretability, knowledge-driven
approaches give the best results, especially for the case of lexico-semantic patterns
that performs the high level of interpretability. The data-driven approaches give
the lowest accurate. Based on the results given by this survey, and in order to chose
the appropriate techniques and methods for event extraction, we recommend the
resort to knowledge-driven approaches for specic domains, due the ease, the sim-
plicity and the high accurate of rules based approaches. Also we need less amount
of data to generate useful models. In the other hand, we recommend data-driven
and hybrid approaches for users who deal with huge amount and variety of data to
extract various types of events.
Table 1. A comparison between the 3 event extraction categories in terms of: amount of necessary data, demanded knowledge and expertise

Technique Approach Method Events Data Knowledge Expertise Interpretability
Data Guy et al [11] data-driven taggers Med Low Low Low
Mark et Joel [12] High Low Low Low
Delia et al. [31] Med Low Low Low
Deyu et al [41] High Low Low Low
Okamoto et al. [27] two-level hierarchical clustering Topic extraction High Low Low Low
method
Liu et al. [5] clustering, modeling entities and daily web news Med Low Low Low
weighted undirected bipartite
graph
Tanev et al. [37] automatic pattern learning real-time news event extraction High Low Low Low
Lei et al. [24] SVM algorithm Topic tracking High Low Low Low
Knowledge Waterman et al. [39] Low Med High Med
Beata [25] Low High High Med
klaussner et al. [22] Low High High Med
IJntema et al. [20] Low Med High Med
Hearst et al. [16] lexico-semantic patterns based extracting nancial events from Low High High Med
on nancial ontology RSS news feeds
Jacobs et al. [21] Low Med High Med
Hogenboom et al. [18] use of lexico-semantic patterns economic event extraction Low Med High Med
Hybrid Piskorski et al. [29] bootstrap a weakly supervised extract violence incidents from N/A Med Med Low
pattern learning algorithm with online news
clusters
Kenji et al. [32] combine rule-based and data- extraction of grammatical rela- Med Med High Med
driven NLP techniques tions
Camiano et al. [9] -statistical methods, rules based resolve issues caused by the lack High Med Med Med
methods of expert knowledge
Pakhomov et al. [28] statistical methods with lexical reinforce statistical methods Med Med Med Med
9

knowledge
Lee et al. [23] grammar-based statistical e-news summarization Med Med Med Med
method
Chun et al. [15] lexico-syntactic patterns, com- Biomedical events Med Med Med Med
bined with term co-occurrences
10

4 Conclusions
We present in this survey the main approaches in current literature, for event ex-
traction from text. As shown, data-driven approaches (corpus based approaches)
require a huge amount of data to discover statistical relations through the use of
quantitative methods such as probabilistic modeling, information theory, and lin-
ear algebra to develop models that approximate linguistic phenomena, So these
approaches require little domain knowledge and expertise. The main advantage
of corpus based methods is that we don't need expert knowledge but we get low
interpretability as a result. For the knowledge-driven approaches, we rely basi-
cally on patterns developed by experts but we need also a little amount of data
to develop these patterns. Pattern based approaches gives better results with high
interpretability but can't deal with huge amount of data when we are looking for the
extraction of various types of events. The resort to hybrid approaches that combine
knowledge-driven and data-driven approaches seems to be a great solution to rem-
edy drawbacks of each family approach and get the advantages of both techniques:
patterns based and corpus based methods.
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