=Paper=
{{Paper
|id=Vol-1866/paper_123
|storemode=property
|title=Microblog Search Task at CLEF 2017: Query Generation using IR and LDA Topic Modeling Combination
|pdfUrl=https://ceur-ws.org/Vol-1866/paper_123.pdf
|volume=Vol-1866
|authors=Malek Hajjem,Chiraz Latiri
|dblpUrl=https://dblp.org/rec/conf/clef/HajjemL17
}}
==Microblog Search Task at CLEF 2017: Query Generation using IR and LDA Topic Modeling Combination==
https://ceur-ws.org/Vol-1866/paper_123.pdf
Microblog Search Task at CLEF 2017:
Query Generation using IR and LDA Topic
Modeling Combination
Malek Hajjem and Chiraz Latiri
LIPAH research Laboratory, Faculty of Sciences of Tunis,
Tunis EL Manar Univeristy,Campus Universitaire Farhat Hached
B.P. n94, 1068 Tunis ,Tunisia
Abstract. The microblogs search task at CLEF 2017 consists of devel-
oping a system to search the most relevant microblogs for cultural query
in a collection about festivals in all languages. Our general approach to
get this objective is the following: we propose to generate from the ini-
tial tweet queries, provided for the task, extended queries able to get an
answer-rich set of microblogs. This is achieved using a thematic represen-
tation of tweet query extracted from microblog corpus. We investigate
in this paper a novel method to improve topics learned from Twitter
content without modifying the basic machinery of LDA. This latter is
based on Information Retrieval (IR) process to generate a query-specific
set of similar tweets. The result then represent the input of a basic LDA
topic modeling process. Finally, the output thematic cluster serves as
our source of expansion for the initial queries.
keywords: CLEF, Microblogs Search, LDA, Information Retrieval, Aggregation
1 Introduction
The microblog search is the second task 1 at CLEF 2017 [8](Conference and Labs
of the Evaluation Forum) from Cultural Microblog Contextualization track. This
task consists in developing a system to search the most relevant microblogs for
cultural query in a collection about festivals in all languages. Topics, announcing
some cultural event, were gathered from different sources2 [5, 4]. The goal is to
retrieve relevant and diverse tweets related to each event from a dataset of 70
000 000 microblogs. This corpus dates from May to September 2015 and is about
the keyword “Festival”.
We consider that using topic models such as Latent Dirichlet Allocation (LDA)
could be useful in this microblog search task. Taking into account that “topic
model is often employed to mine “latent topics” from high dimensionality of
terms in text”[2], these topics can be used to describe the content of a collection
1
https://mc2.talne.eu/spip/Tasks/2-microblog-search/
2
spanish query: http://www.jornada.unam.mx/2017/05/26/
or a query. In fact, the high probability topics and words within the topics can
be viewed as a loose description of a text data.
The contribution of this work is to identifying the “topic” or topics being dis-
cussed in a query, and then using this knowledge of topics to include semantically
related words. To better match with ambiguous nature of tweet query, topics are
extracted from a microblog corpus. A novel method to improve topics learned
from Twitter content without modifying the basic machinery of LDA is investi-
gated. Following this introduction, the paper is organized as follows. In Section
2, a state of the art is shown. In Section 3, the methodology used is presented.
The Conclusion section wraps up the paper.
2 Related works
2.1 Topic modeling for short texts
Topic models are used to uncover the latent semantic structure from text cor-
pus. A topic consists of a cluster of words that frequently occur together. Using
contextual clues, topic models can connect words with similar meanings and dis-
tinguish between uses of words with multiple meanings. Traditional topic models,
like LDA, rely on co-occurrence patterns of words in documents to learn latent
topics [2]. Due to the messy nature of short texts, applying directly conventional
topic models (e.g. LDA and pLSA) on such short texts is not efficient. Indeed,
naive topic models implicitly capture the document-level word co-occurrence
patterns to reveal topics. Thus, to avoid data sparsity, works such as [7, 12,
9] have applied topic models to tweets based on a pooling strategy. It consists
in aggregating similar short texts in one document. In [12], authors proposed
tweet pooling startegy which was based on user aggregated messages. Authors
in [7] also have experimented several schemes to train a standard topic model
and compare their quality and effectiveness. In [1], authors have proposed to
gather tweets occurring in a same user-to-user conversation and show that this
new technique outperforms other pooling methods in terms of clustering quality
and document retrieval. Closer to our work, authors in [9] have proposed a new
method of tweet pooling using hashtags where documents with common hashtag
were gathered. All these works have proved that by training a topic model on
aggregated messages, they obtained a higher quality of learned model.
2.2 Topic model for IR: query expansion
There are two obvious approaches to including topic models in IR. In the first,
a document is represented by itself and the topics to which it belongs. A second
approach is to calculate a query related topic by using topic models and use it for
query expansion. In this case, queries are reformulated (i.e. usually expanded)
to improve the retrieval effectiveness. Authors in [13] proposed a method to
find a good query-related topic based on LDA where experiments confirm that
query expansion based on the derived topics achieved statistically significant
improvements. Others, in [11] implement one of most common local approach
of query expansion - Pseudo Relevance Feedback (PRF). In this last, top k
documents are considered to be relevant and extracts their topic’s terms to
extend queries.
2.3 Topic model for microblog search
With the rapid development of microblogs, microblog search has become one
of the most trendy research areas in recent years. In contrast to traditional
text retrieval, microblog search significantly differs. In fact, microblogs users
often issue short queries to find relevant information. Moreover, the restriction
of messages length lead to a problem in discriminating terms within a given
item. To improve retrieval effectiveness in microblogs researches tend to use
query expansion techniques. The goal is reducing the usual query/document
mismatch. In this area, using topic model like, LDA, could be useful. However,
unlike regular text, Topic modeling is not good at processing short text. Rare
are works which try to enhance microblog search using topic modeling such as
LDA. We cite [10], where authors present a method of contextualization of
short messages using a thematic representation extracted from Wikipedia. This
representation allows to extend the vocabulary of short messages by a set of
thematically related words. The results show the contribution of this method
to a better understanding of short messages. Other works like [3] propose a
novel approach to locating the microblogging experts on a given query. First
they define the experts by social influence and content relevance. For the social
influence, they present a global-ranking algorithm as GUserRank and a topic-
ranking algorithm as TUserRank after applying the LDA topic model.
3 Methodology
3.1 Ressources and Data pre-proceeding
To build a robust LDA model, a large amount of data is needed. From this
perspective, two tweet corpora are used in different runs. We note that we have
choose to use explicitly microblog corpus to respect the noisy nature of tweet
query. The firt corpus is a comparable tweet corpus about Arab spring collected
through Twitter’s API3 in Arabic and French languages. Basically, accessing
Twitter data is done by collecting tweets that contain specific keywords. More
information about this corpus could be found here [6]. The second tweet corpus
is provided to the participants in the evaluation campaign. It is composed of
70 000 000 microblogs. This corpus dates from May to September 2015 and is
about the keyword “Festival”. Notice that before we applied LDA, redundancy
was eliminated by deleting retweets. A language detection was also performed
using a java library4 to remove foreign language tweets.
3
Otterapi a real time search engine that indexes the most influential tweets
search api https://dev.twitter.com/rest/public/search
4
https://code.google.com/p/language-detection/
3.2 Information retrieval based approach for tweet pooling
In this work, an unsupervised topic model based on aggregating tweets that are
thematically closed is presented. The goal is to adapt the LDA basic process to
short tweet text. This will lead to improve the quality of topics latent discov-
ered. To perform tweet pooling, we propose to use information retrieval strategy
and hierarchical classification in order to avoid data sparsity in short texts as
illustrated in Figure 1. Our approach represents an alternative of state of the
art methods based on tweet pooling via meta data (hashtags, user information,
etc). Indeed, such methods are highly dependent on the meta data content of
the tweet corpus. Our approach relies on three main steps, namely:
– Step 1: Preliminary set generation: For each tweeti , we propose to re-
trieve a set γi of matched tweets out of a large tweet collection C of n tweets,
using an information retrieval system. Thus, for a tweet tweeti , performed as
a query, several tweets in C may match it with different similarity degrees.
– Step 2: Pooled set construction: The idea is to aggregate a set of tweets
in Γ partitions by gathering preliminary sets, resulting from the IR process,
according to a combination criterion. If same search results are assigned to
different tweets then the tweets are considered thematically close.
– Step 3: Topic extraction: It consists on learning latent topics from ag-
gregating tweets via LDA.
Fig. 1: IR for tweet aggregation
3.3 Description of Runs submitted
The submitted runs follow three steps (see Figure 2):
– Extract Topics using the combined method of IR and aggregation strategy
as described above section 3.2
– Project the resulted Topic on the tweet text to detect the subset of thematic
relevant terms
– Reformulate the initial query using the subset of thematic terms as enriched
features in form of indri query
Fig. 2: Query expansion using latent topic inferred through IR-LDA method
– RUN 1: Extend the initial query using the latent topic extracted from com-
parable tweet corpus[5] through IR-LDA
– RUN 2: Extend the initial query using latent topic extracted from the Festival
tweet corpus through IR-LDA
4 Conclusion
A large multilingual collection of posts have become publicly available due to
the phenomenal growth of using social networks and microblogs all over the
world. This makes from Microblogs valuable information sources. However little
is known about how search socially-generated content in effective way. In this
paper, we present a method to expand short messages using a thematic represen-
tation. A novel method for aggregating tweets in order to improve the quality of
LDA-based topic modeling for short text is used to improve the quality of latent
topics.
Fig. 3: Exemple of query expansion using latent topic inferred through IR-LDA method
from Arab spring corpus
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