=Paper=
{{Paper
|id=Vol-1180/CLEF2014wn-Pan-SuchomelEt2014
|storemode=property
|title=Heterogeneous Queries for Synoptic and Phrasal Search
|pdfUrl=https://ceur-ws.org/Vol-1180/CLEF2014wn-Pan-SuchomelEt2014.pdf
|volume=Vol-1180
|dblpUrl=https://dblp.org/rec/conf/clef/SuchomelB14
}}
==Heterogeneous Queries for Synoptic and Phrasal Search==
https://ceur-ws.org/Vol-1180/CLEF2014wn-Pan-SuchomelEt2014.pdf
Heterogeneous Queries for Synoptic and Phrasal Search
Notebook for PAN at CLEF 2014
Šimon Suchomel and Michal Brandejs
Faculty of Informatics, Masaryk University
{suchomel,brandejs}@fi.muni.cz
Abstract This paper describes an architecture of the source retrieval system used
at PAN 2014 lab on uncovering plagiarism, authorship, and social software mis-
use. The system is based on the systems used in past years at PAN 13 [6] and PAN
12 [5]. The majority of features were adapted with some improvements described
in this paper. The source retrieval subsystem forms an integral part of a modern
system for plagiarism discovery.
1 Introduction
Systems which compute similarities between documents can significantly help with pla-
giarism detection. They automate the tedious work such as locating possible sources of
plagiarism and finding similar text passages. If the suspicious passages are highlighted
by the system, the supervisor only checks whether a passage is a plagiarism case or not.
The state of the art anti-plagiarism systems evaluate document similarities in order to
select suspicious passages of a source document. This evaluation is refered in PAN as
the document alignment. Documents are algorithmically aligned to a corpus of known
documents. However, if the corpus does not contain the original document the similar-
ity between the original and the suspicious document can not be detected. Therefore a
potential source documents should be retrieved from all documents prior to text align-
ment calculations. The corpus of all documents is usually very large, for example the
entire web, and a search engine is utilized as a retrieval tool. It is ideally utilized auto-
matically in the same manner as plagiarizing users would do manually. The global view
of the system for unoriginal text detection is depicted in figure 1.
With the usage of a given search engine, the problem of source retrieval is then
reduced to the problem of combining proper queries and passing them to the search en-
gine. Selecting and downloading search engine results also influence total performance
of the source retrieval system. The queries pose the most expensive piece of operation,
whereas the downloads are quite cheap. In the real-world scenario, we are often lim-
ited by a number of queries executed in a given time period or by a total number of
queries per document. During the operation the system should maximize the recall and
precision of retrieved results and also minimize the total number of executed queries as
much as possible.
The following sections describe the key parts of the system for source retrieval used
at PAN 2014. More information about the task and the competition can be found in the
task overview written by the lab organizers [2].
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Suspicious
Document Plagiarism discovery
Detailed
Knowledge-based
Document post-processing
Comparison
Candidate Document Candidate
Retrieval Documents
Documents
Indices
The Additional Similar
Web Sources Passages
Figure 1. A global view of a modern anti-plagiarism software.
For obtaining the search results two search engines have been utilized: The Chat-
noir [3] search engine for queries based on extracted keywords; and the Indri [4] search
engine for combined queries and phrases. Both search engines index the ClueWeb09
corpus which served as a main external corpus for document retrieval. The software
were executed and evaluated via TIRA framework [1] on a test corpus of selected
english-written and plagiarized documents.
2 Building of the Queries
Several types of queries were prepared.This year we combined keywords-based queries,
paragraph-based queries and headers-based queries together. Some of the prepared
queries could be discarded from the execution, no query refinement was applied ac-
cording to the results, but some of the top scored keywords could appear in the different
combination in more than one query.
2.1 Keywords-based Queries
From the whole suspicious document, there were extracted keywords using TF-IDF
scoring of lemmas created via Python NLTK lemmatizer and omitting english stop-
words. Firstly we created so called pilot query from top scored six terms. This query
was passed to both Chatnoir and Indri search engines with Indri setting for combine
belief operator of the query.
Based on the three top-scored single-term keywords, their collocations of two more
words were extracted. Those served as phrasal search queries, which were passed to
Indri in order to lookup a contextual occurrence of the selected keywords. All other
subsequent keywords-based queries were passed to Chatnoir only and they were created
by combining collocations of top scored keywords together with the rest of the extracted
keywords up to 6 tokens long. From the rest of the keywords, if any, the remaining
six-term long queries were created. The total number of used keywords-based queries
depended on each document characteristics. From some of the documents, there were
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identified fewer keywords than from others. Usually there were prepared around 10
keywords-based queries per document.
2.2 Paragraph-based Queries
From each paragraph of the suspicious document, a single paragraph-based query was
created. The longest sentence from the specific paragraph was used to build the query.
From the selected sentence six subsequent terms were selected from a random position
within the sentence. The query was created from those six terms, only the punctuation
was removed. The resulting query was passed to the Indri search engine with proximity
term number of 1 denoting the phrasal search.
2.3 Headers-based Queries
The headers-based queries were used in the form in which they appeared in the text
limited to six words in length. They were passed to Indri as a phrasal query as well. For
discovering the headers in the text the approach adopted from Suchomel et al. 2012 [5]
was used with no modifications.
2.4 Chunking
Three types of text chunking were applied: sentence and word chunking for keywords
extraction; headers detection; and paragraph chunking. For each type of query the cor-
responding chunking method was always applied on the whole document from the be-
ginning.
3 Search Control
All prepared queries were processed according to their priority. Starting with the key-
words-based, then the paragraph-based and last the header-based queries. Only all key-
words-based queries – the pilot query, collocation queries and remaining keywords-
combined queries – were executed for each suspicious document. After each query all
its results were processed and positions of discovered similarities were stored. To each
subsequent (not keywords-based) query its position was also attached, if that position
collided with any of already found similarities, the query was omitted from the queue
of prepared queries.
4 Downloading the Results
For each search engine result a snippet based on the given query can be obtained prior to
the full document download. The snippet is obtained via ChatNoir API, it is considered
as no operation and it provides rough information about the document content. It con-
tains a portion of the document up to 500 characters around a given textual string. We
generated the snippet for all documents from results based on specific query for each
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term in that query. The all snippets from one document were concatenated together
and its tuples of two terms were compared with tuples from the suspicious document.
Concordance of the tuples of 20 % or more was the threshold for decision about the
document download.
The downloaded results were textually aligned to the suspicious document using
feature type selection for computing similarities described in Suchomel, Kasprzak et
al. 2013 [6]. If any similarity were detected, the document were reported as a potential
source of plagiarism. Thus all the reported documents contain at least some similarity
with the suspicious document.
5 Conclusions
This paper described the key aspects and changes from our erstwhile systems for candi-
date document retrieval used at PAN 14 lab on uncovering plagiarism. The architecture
stems from PAN 12 and PAN 13 labs and the real-world anti-plagiarism system which
is in use at Masaryk University. The results of the PAN show that this approach is one
of the best for a real-life adoption, since it achieved a decent recall with just a fraction
of used queries. Such approach is applicable for detection of suspicious texts, which
may contain plagiarism, that can then be selected for further investigation.
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