=Paper=
{{Paper
|id=Vol-1866/paper_53
|storemode=property
|title=Entity Recognition and Language Identification with FELTS
|pdfUrl=https://ceur-ws.org/Vol-1866/paper_53.pdf
|volume=Vol-1866
|authors=Pierre Jourlin
|dblpUrl=https://dblp.org/rec/conf/clef/Jourlin17
}}
==Entity Recognition and Language Identification with FELTS==
https://ceur-ws.org/Vol-1866/paper_53.pdf
Entity Recognition and Language Identification
with FELTS
Pierre Jourlin
Laboratoire d’Informatique, Université d’Avignon, 84911 Avignon, France
Pierre.Jourlin@univ-avignon.fr
1 Introduction
This working notes describe the experiments we conducted in the Microblog
Cultural Contextualization Lab [2] of CLEF 2017 [3]. The microblog data is com-
posed of very short texts, with very heterogeneous styles. Some of them are
written in more than one language. We decided to takle the entity recognition
problem by using a non-statistical, dictionary-based, multiword term extractor.
On the other hand, our participation in the language identification task is based
on word and character uni-gram probabilities.
2 Task 1.5: Entity Recognition
In order to address the entity recognition problem, we make use of a free software
that we developed in 2012 : FELTS (for Fast Extractor for Large Term Sets)1 .
It was designed to support very large multi-word term dictionaries such as the
list of Wikipedia page titles. Using the Wikipedia’s database dumps of march
1st 2017, we were able to provide FELTS with a corpus of :
- 14,971,916 distinct terms, containing 4,811,345 distinct words for English.
- 3,384,979 distinct terms, containing 1,390,569 distinct words for French.
- 2,910,899 distinct terms, containing 978,297 distinct words for Spanish.
- 1,787,280 distinct terms, containing 800,612 distinct words for Portuguese.
In order to obtain a good level of efficiency, our approach is based on Minimal
Perfect Hash Function, more specifically, the Compress, Hash and Displace al-
gorithm[1], as it was implemented in the C Minimal Perfect Hashing Library
(CMPH) V2.02 in 2012.
We processed the 63,192,980 micro-blog messages of task 1 with a 64 bits per-
sonal computer equipped with a Intel Core i7-2600 (an octo-core CPU running
at 3.40GHz) and 7,8 Gb of RAM. The English term corpus and the associated
hash function needed 3.6Gb of RAM. It took less that half a second to extract
the 20,665 English terms contained in the 1095 task 1 ”topics” and less than
1
https://github.com/jourlin/FELTS
2
http://cmph.sourceforge.net/
8 hours to extract the 1,2 billion English terms contained in the 63 millions of
micro-blog messages.
With such an approach, we found it difficult to choose a relevance score for
each entity-language pair. Our system simply finds or does not find a Wikipedia
entity in a text. However, we believe longer entities are more likely to indicate
narrower senses and more relevant topics than shorter entities. We thus decided
to simply score the multi-word terms with their character length. For each text
of the test data, and each of the 4 languages, we provided the assessors with
the 10 longuest extracted entities, ranked by decreasing character length. At the
time this paper was written, we were not provided with relevance scores.
3 Task 1.2: Language identification
As an exploratory approach, we used a proven technique for language iden-
tification on long text : probabilistic decision based on word uni-grams. The
probabilities of a language given a word were computed on two distinct corpora
: The Wikipedia full text articles in all the 281 available languages (1st run)
and the 63 millions of micro-blogs messages for task 1 (2nd run). Both corpora
are very large but they both carry specific issues : high size disparities, distant
language levels, multi-byte character encoding, lack of word boundaries, erro-
neous language identification, untranslated terms, multi-language texts, etc. As
we realised that a word-based approach was bound to fail on languages such as
Japanese or Korean were word boundaries are not explicit, we submitted a 3rd
run based on character uni-gram probabilities.
We were provided with a partial manual evaluation of our first run. For only
121 out of 1095 microblog messages, our firt run identified a different language
than the locale configuration of its author. For 90 of these 121 messages, the
language identified by our 1st run was evaluated as correct. 11 of the remain-
ing 31 erroneous identifications occurred on Japanese or Korean texts mixed
with english multiword names. The last 20 erroneous indentifications are rather
difficult to analyse and various causes such as the co-occurence of original and
tanslated named entities can be suspected. Our third run (character uni-grams)
seems to be slighlty better for Japanese or Korean but it still mostly fails on
multi-language messages and is very weak at classifying languages that shares a
same root. Our second run (word uni-grams according the author’s locale con-
figuration) found the correct language for 6 of 31 messages for which our first
run failed. However, it is overall weaker than our first run.
4 Conclusion
The language identification results look very promising. However, we believe that
there is still room for improvement and that a combination of several methods,
and a specific processing of named entities could help.
References
[1] Botelho, F. C., Belazzougui, D. and Dietzfelbinger, M. Compress, hash and displace.
In Proceedings of the 17th European Symposium on Algorithms (ESA2009) Springer
LNCS 5757, 682-693 (2009)
[2] Ermakova, L., Goeuriot, L., Mothe, J., Mulhem, P., Nie, J.-Y., and SanJuan, E.
CLEF 2017 Microblog Cultural Contextualization Lab Overview International Con-
ference of the Cross-Language Evaluation Forum for European Languages Proceed-
ings Springer LNCS volume, Springer, CLEF 2017, Dublin.
[3] Jones, G. J. F., Lawless, S., Gonzalo, J., Kelly, L., Goeuriot, L., Mandl, T., Cappel-
lato, L., Ferro, N. Experimental IR Meets Multilinguality, Multimodality, and Inter-
action 8th International Conference of the CLEF Association, CLEF 2017, Dublin,
Ireland, September 11-14, 2017, Proceedings. Springer LNCS 10456.