Lili Kotlerman - לילי
קוטלרמן
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Contact info
Department
of Computer Science
Bar Ilan University
Ramat Gan,
52900, Israel
Lab: +972-3-5318054
E-mail: lili.dav @
gmail.com
I'm a PhD student at the Computer Science Department, Bar Ilan University,
under the supervision of Prof. Ido Dagan.
My research field is
Natural Language Processing. I focus on lexical entailment (my M.Sc research) and entailment-based text
exploration (my Ph.D research).
Talks
and presentations (peer-reviewed)
·
Clustering
Moderate-size Collections of Short Texts. The Israeli Seminar on Computational
Linguistics (ISCOL), Haifa University. 2014.
·
Textual
Entailment Graphs. The Israeli Seminar on Computational Linguistics (ISCOL),
Haifa University. 2014.
·
Sentence
Clustering via Projection over Term Clusters. The Israeli Seminar on
Computational Linguistics (ISCOL), Ben-Gurion University. 2013.
·
ParaQuery: Making Sense of Paraphrase
Collections. Bar-Ilan Symposium on the Foundations of
Artificial Intelligence (BISFAI), Bar Ilan
University. 2013.
·
Sentence
Clustering via Projection over Term Clusters. Bar-Ilan
Symposium on the Foundations of Artificial Intelligence (BISFAI), Bar Ilan University. 2013.
·
Deriving
Target-Domain Taxonomies from Wikipedia Category Hierarchy. The Israeli Seminar
on Computational Linguistics (ISCOL), Bar Ilan
University. 2011.
·
Directional
Semantic Similarity. IBM Machine Learning Seminar, Haifa University. 2009.
· Corpus-Based Distributional Learning of Lexical Entailment. The Israeli Seminar on Computational Linguistics (ISCOL), Bar Ilan University. 2008.
Academic
activities
-
“Statistical Methods in Computer
Science Research”, Bar Ilan University (since 2011).
-
“Introduction to Natural Language
Processing” and “Information Retrieval”, Bar Ilan
University (since 2006).
-
“Introduction to Cryptography and
Network Security”, The College of Management (2005-2006).
Awards and scholarships
Education
·
DIRECT: Directional
Distributional Term-Similarity Resource.
The resource contains
directional distributional term-similarity rules automatically extracted as
described in (Kotlerman et.al., JNLE-DLS
2010). Most of the rules are lexical entailment rules, where the meaning of the
rule's left-hand-side implies the meaning of its right-hand-side.
For instance: koala-->animal, bread-->food,
imprisonment-->arrest, wedding-->marriage.
·
Twitter dataset for
sentence clustering (banking domain).
The dataset of tweets used in Kotlerman et al.
(2012), and the output of the 4 compared sentence clustering methods for this
dataset. The data includes:
1.
Gold-standard
dataset of 194 sentences crawled from Twitter, expressing reasons for customer
dissatisfaction with Citibank. The sentences were gathered automatically by a
rule-based extraction algorithm and manually grouped to clusters according to
the reasons stated in them.
2.
A corpus of
31,898 tweets from the banking domain.
3.
Output produced
by the novel method suggested in the paper and by the three baseline methods.
·
IMDB dataset for text categorization.
Gold-standard dataset presented in
(Liebeskind et.al., LRE
2015). The taxonomy and the annotation guidelines are published in the paper. The dataset contains 1970 movie titles and the corresponding categories assigned by the annotator.
There are legal issues due to which we cannot publish the texts of the descriptions - they can be found at the IMDB website.