This paper studies cross-lingual transfer for dependency parsing , focusing on very low-resource settings where delexicalized transfer is the only fully automatic option . We show how to boost parsing performance by rewriting the source sentences so as to better match the linguistic regularities of the target language . By viewing neural networks as a cascade of feature extractors followed by a logistic regression classifier , we hypothesise that the hidden layers , which act as feature extractors , will be transferable between languages . As a corollary , we propose that training the hidden layers on multiple languages makes them more suitable for such cross-lingual transfer . We experimentally confirm these hypotheses on the GlobalPhone corpus using seven languages from three different language families : Germanic , Romance , and Slavic . It therefore serves as a fine case study . The article first examines potentials and constraints in the three types of interlingual transfer represented , then the target texts are analysed , and finally they are compared and assessed in order to determine whether differences are due to limitations specific to one type of interlingual transfer or to choices and decisions made by individual interpreters , subtitlers or translators . It is concluded that comparisons can , in fact , be made . Typology Driven Estimation of Grammatical Error Distributions in ESL . This work examines the impact of crosslinguistic transfer on grammatical errors in English as Second Language ( ESL ) texts . Using a computational framework that formalizes the theory of Contrastive Analysis ( CA ) , we demonstrate that language specific error distributions in ESL writing can be predicted from the typological properties of the native language and their relation to the typology of English . This paper studies the relative effectiveness of different methods for multilingual model combination and dictionary mapping for recognizing a new unseen target language if training data are limited . We examine the crosslanguage transfer from monolingual and multilingual models to German and Russian language for large vocabulary speech recognition using a dictation database which has been collected under the project GlobalPhone . This project at the University of Karlsruhe investigates LVCSR systems in 15 languages of the world , namely Arabic , Chinese , Croatian , English , French , German , Italian , Japanese , Korean , Portuguese , Russian , Spanish , Swedish , Tamil , and Turkish . Based on a global phoneme set we create recognizer which combine up to eight languages and perform recognition results in language independent and adaptive setups . We found that multilingual context dependent models outperform monolingual models for the purpose of crosslanguage transfer . Two dictionary mapping approaches are compared . Process continues until the terminal nodes are reached . We choose a standard neural tagger and we focused on our recursive parsing strategy and on the cross lingual transfer problem to develop a robust model for the French language , using only few training samples . Inspired by recent progress of injecting pre-trained language model ( LM ) on many Natural Language Processing ( NLP ) task , we proposed to fine-tune pre-trained language model from high-resources languages to low-resources languages to improve the performance of both scenarios . Our empirical experiment demonstrates significant improvement when fine-tuning pre-trained language model in cross-lingual transfer scenarios for small gold corpus and competitive results in large silver compare to supervised cross-lingual transfer , which will be useful when there is no parallel annotation in the same task to begin . We compare our proposed method of cross-lingual transfer using pre-trained LM to different sources of transfer such as mono-lingual LM and Part-of-Speech tagging ( POS ) in the downstream task of both large silver and small gold NER dataset by exploiting character-level input of bi-directional language model task . This work describes the UoE-LMU submission for the CoNLL-SIGMORPHON 2017 Shared Task on Universal Morphological Reinflection , Subtask 1 : given a lemma and target morphological tags , generate the target inflected form . We evaluate several ways to improve performance in the 1000-example setting : three methods to augment the training data with identical input-output pairs ( i.e. , autoencoding ) , a heuristic approach to identify likely pairs of inflectional variants from an unlabeled corpus , and a method for crosslingual knowledge transfer . We find that autoencoding random strings works surprisingly well , outperformed only slightly by autoencoding words from an unlabelled corpus . When Part-of-Speech annotated data is scarce , e.g. for under resourced languages , one can turn to cross- lingual transfer and crawled dictionaries to collect partially supervised data . We cast this problem in the framework of ambiguous learning and show how to learn an accurate history-based model . Disamping itu , penelitian ini juga menunjukan bahwa kesalahan tata bahasa siswa disebabkan oleh transfer interlingual , transfer intralingual , konteks pembelajaran , dan strategi komunikasi . Kata Kunci : kesalahan- kesalahan , kesalahan tata bahasa , surat bisnis . Cross - lingual word embeddings are often used to build bag - of - word representations of longer linguistic units by taking their respective centroid Klementiev:2012:coling_reuters . While this approach has the advantage of requiring a weak ( or even no ) cross - lingual signal , it has been shown that the resulting sentence embeddings works rather poorly in practical cross - lingual transfer settings Conneau:2018:emnlp_xnli . A more competitive approach that we follow here is to use a sequence - to - sequence encoder - decoder architecture Schwenk:2017:repl4nlp , hassan2018achieving . This enables us to learn a classifier on top of the resulting embeddings using English annotated data only , and transfer it to any of the 93 languages without any modification . Our experiments in cross-lingual natural language inference ( XNLI data set ) , cross-lingual document classification ( MLDoc data set ) , and parallel corpus mining ( BUCC data set ) show the effectiveness of our approach . We also introduce a new test set of aligned sentences in 112 languages , and show that our sentence embeddings obtain strong results in multilingual similarity search even for low- resource languages . Different from the traditional hard matching strategy , this paper extends traditional generalized vector space model ( GVSM ) to handle cross-lingual cases , referred to as CLGVSM , by incorporating cross-lingual word similarity measures . With this model , we further compare different word similarity measures in cross-lingual document clustering . To select cross-lingual features effectively , we also propose a softmatching based feature selection method in CLGVSM . Bilingual models that capture the semantics of sentences are typically only evaluated on cross-lingual transfer tasks such as cross-lingual document categorization or machine translation . In this work , we evaluate the quality of the monolingual representations learned with a variant of the bilingual compositional model of Hermann and Blunsom ( 2014 ) , when viewing translations in a second language as a semantic annotation as the original language text . Moreover , since our algorithm uses the singular value decomposition as its core operation , it is highly scalable . Experiments show that our method achieves state-of-the-art performance on a crosslingual document retrieval task . Finally , although not trained for embedding sentences and words , it also achieves competitive performance on crosslingual sentence and word retrieval tasks . In this work we propose to evaluate collections of embeddings by adapting downstream natural language tasks to the optimal transport framework . We show how the family of Wasserstein distances can be used to solve cross-lingual document retrieval and the cross-lingual document classification problems . We argue on the advantages of this approach compared to more traditional evaluation methods of embeddings like bilingual lexical induction . This paper develops a word vector embedding based CLIR model that uses the average distances between the embedded word vectors of the source and target language documents to rank candidate document pairs . Our experiments with the WMT bilingual document alignment dataset reveal that the word vector based similarity significantly improves the recall of crosslingual document alignment in comparison to the classical language modeling based CLIR . Recent work in learning bilingual representations tend to tailor towards achieving good performance on bilingual tasks , most often the crosslingual document classification ( CLDC ) evaluation , but to the detriment of preserving clustering structures of word representations monolingually . In this work , we propose a joint model to learn word representations from scratch that utilizes both the context coocurrence information through the monolingual component and the meaning equivalent signals from the bilingual constraint . This helps the proposed model to generalize for representation and transfer learning tasks efficiently for high dimensional data . Finally , we evaluate the performance of the proposed model on three multi-view and cross-modal problems viz . , audio articulation , cross-modal image retrieval and multilingual ( cross-language ) document classification . Through extensive experiments , we find that the proposed model performs much better than the current state-of-the-art deep learning techniques on all three multi-view and cross-modal tasks . Our approach sets a new state - of - the - art on zero - shot cross - lingual natural language inference for all the 14 languages in the XNLI dataset but one . We also achieve very competitive results in cross - lingual document classification ( MLDoc dataset ) . Our sentence embeddings are also strong at parallel corpus mining , establishing a new state - of - the - art in the BUCC shared task for 3 of its 4 language pairs . subsection : MLDoc : cross - lingual classification Cross - lingual document classification is a typical application of multilingual representations . In order to evaluate our sentence embeddings in this task , we use the MLDoc dataset of Schwenk:2018:lrec_mldoc , which is an improved version of the Reuters benchmark Lewis : Reuters:2004 , Klementiev:2012:coling_reuters with uniform class priors and a wider language coverage . The Zipporah model learns a logistic regression classifier over bag - of - word features to distinguish between ground truth translations and synthetic noisy ones xu2017zipporah . Many of these content - based approaches rely on cross - lingual document retrieval to some extent utiyama2003reliable , munteanu2005improving , munteanu2006extracting , rauf2009comparable , typically to obtain an initial alignment that is then further filtered . In addition to that , there have been several proposals that rely on machine translation to score parallel sentence candidates using automatic metrics like BLEU and TER rauf2009comparable , bouamor2018h2 . Finally , we conduct a case-study on a non-Latin language , Bengali , which suggests that leveraging knowledge from Wikipedia will be a promising direction to further improve the model performances . Our results can shed light on future research for improving cross-lingual NER . We present two systems that select the most appropriate Spanish substitutes for a marked word in an English test sentence . These systems were official entries to the SemEval-2010 Cross-Lingual Lexical Substitution task . The first system , Swat-E , finds Spanish substitutions by first finding English substitutions in the English sentence and then translating these substitutions into Spanish using an English-Spanish dictionary . In this paper we describe the SemEval-2010 Cross-Lingual Lexical Substitution task , where given an English target word in context , participating systems had to find an alternative substitute word or phrase in Spanish . The task is based on the English Lexical Substitution task run at SemEval-2007 . TREC 10 we participated in the Named Page Finding Task and the Cross-Lingual Task . In the web track , we explored the use of linear combinations of term collections based on document structure . Explaining Cross-Lingual Task Performance with Eigenvalue Divergence . Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand : e.g. , previous work has suggested there is a connection between the expected success of bilingual lexicon induction ( BLI ) and the assumption of ( approximate ) isomorphism between monolingual embedding spaces . Dagstuhl Seminar 15201 was conducted on " Cross-Lingual Cross-Media Content Linking : Annotations and Joint Representations " . Participants from around the world participated in the seminar and presented state-of-the-art and ongoing research related to the seminar topic . The chapter classifies the presented methodology , highlights the main pros and cons , and discusses the perspectives of the extension of the current research towards cross-lingual summarization systems . Combining Machine Learning and Natural Language Processing for Language-Specific , MultiLingual , and Cross-Lingual Text Summarization : A Wide-Ranging Overview Luca Cagliero Politecnico di Torino , Italy Proceedings . Ontologies.- Cross-Lingual Ontology Mapping - An Investigation of the Impact of Machine Translation.- Modeling Common Real-Word Relations Using Triples Extracted from n-Grams.- Supporting the Development of Data This paper describes the Cross-Lingual Word Sense Disambiguation system UvT-WSD1 , developed at Tilburg University , for participation in two SemEval-2 tasks : the Cross-Lingual Word Sense Disambiguation task and the Cross-Lingual Lexical Substitution task . The UvT-WSD1 system makes use of k-nearest neighbour classifiers , in the form of single-word experts for each target word to be disambiguated . We describe evaluation experiments conducted by submitting retrieval runs for the Chinese , Japanese and Korean Single Language Information Retrieval subtasks of the Cross-Lingual Information Retrieval ( CLIR ) Task of the 6th NII Test Collection for IR Systems Workshop ( NTCIR-6 ) . We show that a Generalized Success@10 measure exposes a downside of the blind feedback technique that is overlooked by traditional ad hoc retrieval measures such as mean average precision , R-precision and Precision@10 . Full Papers.- Using Support Vector Machines for Terrorism Information Extraction.- Criminal Incident Data Association Using the OLAP Technology.- Names : A New Frontier in Text Mining.- Web-Based Intelligence Reports System.- Authorship Analysis in Cybercrime Investigation.- Behavior Profiling of Email.- Detecting Deception through Linguistic Analysis.- A Longitudinal Analysis of Language Behavior of Deception in E-mail.- Evacuation Planning : A Capacity Constrained Routing Approach.- Locating Hidden Groups in Communication Networks Using Hidden Markov Models.- Automatic Construction of Cross-Lingual Networks of Concepts from the Hong Kong SAR Police Department.- Decision Based Spatial Analysis of Crime.- CrimeLink Explorer : Using Domain Knowledge to Facilitate Automated Crime Association Analysis.- A Spatio Temporal Visualizer for Law Enforcement.- Tracking Hidden Groups Using Communications.- Examining Technology Acceptance by Individual Law Enforcement Officers : An Exploratory Study.- " Atrium " - A Knowledge Model for Modern Security Forces in the Information and Terrorism Age.- Untangling Criminal Networks : A Case Study.- Addressing the Homeland Security Problem : No language-specific tools are used , hence making this approach extensible for other languages . We used the datasets provided by Forum for Information Retrieval Evaluation ( FIRE ) for their 2010 Adhoc Cross-Lingual document retrieval task on Indian languages . Experiments have been performed with different ranking algorithms and their results are compared . Cross-Lingual Multi-Keyword Rank Search Over Encrypted Cloud Data . Multi-user multi-keyword ranked search scheme in arbitrary language is a novel multi-keyword rank searchable encryption ( MRSE ) framework based on Paillier Cryptosystem with Threshold Decryption ( PCTD ) .
This paper studies cross-lingual transfer for dependency parsing , focusing on very low-resource settings where delexicalized transfer is the only fully automatic option . We show how to boost parsing performance by rewriting the source sentences so as to better match the linguistic regularities of the target language .
By viewing neural networks as a cascade of feature extractors followed by a logistic regression classifier , we hypothesise that the hidden layers , which act as feature extractors , will be transferable between languages . As a corollary , we propose that training the hidden layers on multiple languages makes them more suitable for such cross-lingual transfer . We experimentally confirm these hypotheses on the GlobalPhone corpus using seven languages from three different language families : Germanic , Romance , and Slavic .
It therefore serves as a fine case study . The article first examines potentials and constraints in the three types of interlingual transfer represented , then the target texts are analysed , and finally they are compared and assessed in order to determine whether differences are due to limitations specific to one type of interlingual transfer or to choices and decisions made by individual interpreters , subtitlers or translators . It is concluded that comparisons can , in fact , be made .
Typology Driven Estimation of Grammatical Error Distributions in ESL . This work examines the impact of crosslinguistic transfer on grammatical errors in English as Second Language ( ESL ) texts . Using a computational framework that formalizes the theory of Contrastive Analysis ( CA ) , we demonstrate that language specific error distributions in ESL writing can be predicted from the typological properties of the native language and their relation to the typology of English .
This paper studies the relative effectiveness of different methods for multilingual model combination and dictionary mapping for recognizing a new unseen target language if training data are limited . We examine the crosslanguage transfer from monolingual and multilingual models to German and Russian language for large vocabulary speech recognition using a dictation database which has been collected under the project GlobalPhone . This project at the University of Karlsruhe investigates LVCSR systems in 15 languages of the world , namely Arabic , Chinese , Croatian , English , French , German , Italian , Japanese , Korean , Portuguese , Russian , Spanish , Swedish , Tamil , and Turkish . Based on a global phoneme set we create recognizer which combine up to eight languages and perform recognition results in language independent and adaptive setups . We found that multilingual context dependent models outperform monolingual models for the purpose of crosslanguage transfer . Two dictionary mapping approaches are compared .
Process continues until the terminal nodes are reached . We choose a standard neural tagger and we focused on our recursive parsing strategy and on the cross lingual transfer problem to develop a robust model for the French language , using only few training samples .
Inspired by recent progress of injecting pre-trained language model ( LM ) on many Natural Language Processing ( NLP ) task , we proposed to fine-tune pre-trained language model from high-resources languages to low-resources languages to improve the performance of both scenarios . Our empirical experiment demonstrates significant improvement when fine-tuning pre-trained language model in cross-lingual transfer scenarios for small gold corpus and competitive results in large silver compare to supervised cross-lingual transfer , which will be useful when there is no parallel annotation in the same task to begin . We compare our proposed method of cross-lingual transfer using pre-trained LM to different sources of transfer such as mono-lingual LM and Part-of-Speech tagging ( POS ) in the downstream task of both large silver and small gold NER dataset by exploiting character-level input of bi-directional language model task .
This work describes the UoE-LMU submission for the CoNLL-SIGMORPHON 2017 Shared Task on Universal Morphological Reinflection , Subtask 1 : given a lemma and target morphological tags , generate the target inflected form . We evaluate several ways to improve performance in the 1000-example setting : three methods to augment the training data with identical input-output pairs ( i.e. , autoencoding ) , a heuristic approach to identify likely pairs of inflectional variants from an unlabeled corpus , and a method for crosslingual knowledge transfer . We find that autoencoding random strings works surprisingly well , outperformed only slightly by autoencoding words from an unlabelled corpus .
When Part-of-Speech annotated data is scarce , e.g. for under resourced languages , one can turn to cross- lingual transfer and crawled dictionaries to collect partially supervised data . We cast this problem in the framework of ambiguous learning and show how to learn an accurate history-based model .
Disamping itu , penelitian ini juga menunjukan bahwa kesalahan tata bahasa siswa disebabkan oleh transfer interlingual , transfer intralingual , konteks pembelajaran , dan strategi komunikasi . Kata Kunci : kesalahan- kesalahan , kesalahan tata bahasa , surat bisnis .
Cross - lingual word embeddings are often used to build bag - of - word representations of longer linguistic units by taking their respective centroid Klementiev:2012:coling_reuters . While this approach has the advantage of requiring a weak ( or even no ) cross - lingual signal , it has been shown that the resulting sentence embeddings works rather poorly in practical cross - lingual transfer settings Conneau:2018:emnlp_xnli . A more competitive approach that we follow here is to use a sequence - to - sequence encoder - decoder architecture Schwenk:2017:repl4nlp , hassan2018achieving .
This enables us to learn a classifier on top of the resulting embeddings using English annotated data only , and transfer it to any of the 93 languages without any modification . Our experiments in cross-lingual natural language inference ( XNLI data set ) , cross-lingual document classification ( MLDoc data set ) , and parallel corpus mining ( BUCC data set ) show the effectiveness of our approach . We also introduce a new test set of aligned sentences in 112 languages , and show that our sentence embeddings obtain strong results in multilingual similarity search even for low- resource languages .
Different from the traditional hard matching strategy , this paper extends traditional generalized vector space model ( GVSM ) to handle cross-lingual cases , referred to as CLGVSM , by incorporating cross-lingual word similarity measures . With this model , we further compare different word similarity measures in cross-lingual document clustering . To select cross-lingual features effectively , we also propose a softmatching based feature selection method in CLGVSM .
Bilingual models that capture the semantics of sentences are typically only evaluated on cross-lingual transfer tasks such as cross-lingual document categorization or machine translation . In this work , we evaluate the quality of the monolingual representations learned with a variant of the bilingual compositional model of Hermann and Blunsom ( 2014 ) , when viewing translations in a second language as a semantic annotation as the original language text .
Moreover , since our algorithm uses the singular value decomposition as its core operation , it is highly scalable . Experiments show that our method achieves state-of-the-art performance on a crosslingual document retrieval task . Finally , although not trained for embedding sentences and words , it also achieves competitive performance on crosslingual sentence and word retrieval tasks .
In this work we propose to evaluate collections of embeddings by adapting downstream natural language tasks to the optimal transport framework . We show how the family of Wasserstein distances can be used to solve cross-lingual document retrieval and the cross-lingual document classification problems . We argue on the advantages of this approach compared to more traditional evaluation methods of embeddings like bilingual lexical induction .
This paper develops a word vector embedding based CLIR model that uses the average distances between the embedded word vectors of the source and target language documents to rank candidate document pairs . Our experiments with the WMT bilingual document alignment dataset reveal that the word vector based similarity significantly improves the recall of crosslingual document alignment in comparison to the classical language modeling based CLIR .
Recent work in learning bilingual representations tend to tailor towards achieving good performance on bilingual tasks , most often the crosslingual document classification ( CLDC ) evaluation , but to the detriment of preserving clustering structures of word representations monolingually . In this work , we propose a joint model to learn word representations from scratch that utilizes both the context coocurrence information through the monolingual component and the meaning equivalent signals from the bilingual constraint .
This helps the proposed model to generalize for representation and transfer learning tasks efficiently for high dimensional data . Finally , we evaluate the performance of the proposed model on three multi-view and cross-modal problems viz . , audio articulation , cross-modal image retrieval and multilingual ( cross-language ) document classification . Through extensive experiments , we find that the proposed model performs much better than the current state-of-the-art deep learning techniques on all three multi-view and cross-modal tasks .
Our approach sets a new state - of - the - art on zero - shot cross - lingual natural language inference for all the 14 languages in the XNLI dataset but one . We also achieve very competitive results in cross - lingual document classification ( MLDoc dataset ) . Our sentence embeddings are also strong at parallel corpus mining , establishing a new state - of - the - art in the BUCC shared task for 3 of its 4 language pairs .
subsection : MLDoc : cross - lingual classification Cross - lingual document classification is a typical application of multilingual representations . In order to evaluate our sentence embeddings in this task , we use the MLDoc dataset of Schwenk:2018:lrec_mldoc , which is an improved version of the Reuters benchmark Lewis : Reuters:2004 , Klementiev:2012:coling_reuters with uniform class priors and a wider language coverage .
The Zipporah model learns a logistic regression classifier over bag - of - word features to distinguish between ground truth translations and synthetic noisy ones xu2017zipporah . Many of these content - based approaches rely on cross - lingual document retrieval to some extent utiyama2003reliable , munteanu2005improving , munteanu2006extracting , rauf2009comparable , typically to obtain an initial alignment that is then further filtered . In addition to that , there have been several proposals that rely on machine translation to score parallel sentence candidates using automatic metrics like BLEU and TER rauf2009comparable , bouamor2018h2 .
Finally , we conduct a case-study on a non-Latin language , Bengali , which suggests that leveraging knowledge from Wikipedia will be a promising direction to further improve the model performances . Our results can shed light on future research for improving cross-lingual NER .
We present two systems that select the most appropriate Spanish substitutes for a marked word in an English test sentence . These systems were official entries to the SemEval-2010 Cross-Lingual Lexical Substitution task . The first system , Swat-E , finds Spanish substitutions by first finding English substitutions in the English sentence and then translating these substitutions into Spanish using an English-Spanish dictionary .
In this paper we describe the SemEval-2010 Cross-Lingual Lexical Substitution task , where given an English target word in context , participating systems had to find an alternative substitute word or phrase in Spanish . The task is based on the English Lexical Substitution task run at SemEval-2007 .
TREC 10 we participated in the Named Page Finding Task and the Cross-Lingual Task . In the web track , we explored the use of linear combinations of term collections based on document structure .
Explaining Cross-Lingual Task Performance with Eigenvalue Divergence . Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand : e.g. , previous work has suggested there is a connection between the expected success of bilingual lexicon induction ( BLI ) and the assumption of ( approximate ) isomorphism between monolingual embedding spaces .
Dagstuhl Seminar 15201 was conducted on " Cross-Lingual Cross-Media Content Linking : Annotations and Joint Representations " . Participants from around the world participated in the seminar and presented state-of-the-art and ongoing research related to the seminar topic .
The chapter classifies the presented methodology , highlights the main pros and cons , and discusses the perspectives of the extension of the current research towards cross-lingual summarization systems . Combining Machine Learning and Natural Language Processing for Language-Specific , MultiLingual , and Cross-Lingual Text Summarization : A Wide-Ranging Overview Luca Cagliero Politecnico di Torino , Italy
Proceedings . Ontologies.- Cross-Lingual Ontology Mapping - An Investigation of the Impact of Machine Translation.- Modeling Common Real-Word Relations Using Triples Extracted from n-Grams.- Supporting the Development of Data
This paper describes the Cross-Lingual Word Sense Disambiguation system UvT-WSD1 , developed at Tilburg University , for participation in two SemEval-2 tasks : the Cross-Lingual Word Sense Disambiguation task and the Cross-Lingual Lexical Substitution task . The UvT-WSD1 system makes use of k-nearest neighbour classifiers , in the form of single-word experts for each target word to be disambiguated .
We describe evaluation experiments conducted by submitting retrieval runs for the Chinese , Japanese and Korean Single Language Information Retrieval subtasks of the Cross-Lingual Information Retrieval ( CLIR ) Task of the 6th NII Test Collection for IR Systems Workshop ( NTCIR-6 ) . We show that a Generalized Success@10 measure exposes a downside of the blind feedback technique that is overlooked by traditional ad hoc retrieval measures such as mean average precision , R-precision and Precision@10 .
Full Papers.- Using Support Vector Machines for Terrorism Information Extraction.- Criminal Incident Data Association Using the OLAP Technology.- Names : A New Frontier in Text Mining.- Web-Based Intelligence Reports System.- Authorship Analysis in Cybercrime Investigation.- Behavior Profiling of Email.- Detecting Deception through Linguistic Analysis.- A Longitudinal Analysis of Language Behavior of Deception in E-mail.- Evacuation Planning : A Capacity Constrained Routing Approach.- Locating Hidden Groups in Communication Networks Using Hidden Markov Models.- Automatic Construction of Cross-Lingual Networks of Concepts from the Hong Kong SAR Police Department.- Decision Based Spatial Analysis of Crime.- CrimeLink Explorer : Using Domain Knowledge to Facilitate Automated Crime Association Analysis.- A Spatio Temporal Visualizer for Law Enforcement.- Tracking Hidden Groups Using Communications.- Examining Technology Acceptance by Individual Law Enforcement Officers : An Exploratory Study.- " Atrium " - A Knowledge Model for Modern Security Forces in the Information and Terrorism Age.- Untangling Criminal Networks : A Case Study.- Addressing the Homeland Security Problem :
No language-specific tools are used , hence making this approach extensible for other languages . We used the datasets provided by Forum for Information Retrieval Evaluation ( FIRE ) for their 2010 Adhoc Cross-Lingual document retrieval task on Indian languages . Experiments have been performed with different ranking algorithms and their results are compared .
Cross-Lingual Multi-Keyword Rank Search Over Encrypted Cloud Data . Multi-user multi-keyword ranked search scheme in arbitrary language is a novel multi-keyword rank searchable encryption ( MRSE ) framework based on Paillier Cryptosystem with Threshold Decryption ( PCTD ) .
Zero-resource Dependency Parsing: Boosting Delexicalized Cross-lingual Transfer with Linguistic Knowledge Multilingual training of deep neural networks Comparing types of interlingual transfer Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL Adaptation of Pronunciation Dictionaries for Recognition of Unseen Languages MaskParse@Deskin at SemEval-2019 Task 1: Cross-lingual UCCA Semantic Parsing using Recursive Masked Sequence Tagging Cross-Lingual Transfer for Distantly Supervised and Low-resources Indonesian NER Training Data Augmentation for Low-Resource Morphological Inflection Apprentissage partiellement supervisé d'un étiqueteur morpho-syntaxique par transfert cross-lingue A STUDY OF GRAMMATICAL ERRORS MADE BY THE TWELFTH GRADE STUDENTS OF SMK PARIWISATA TRIATMA JAYA SINGARAJA IN WRITING A BUSINESSES LETTER IN THE ACADEMIC YEAR 2013/2014 Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond CLGVSM: Adapting Generalized Vector Space Model to Cross-lingual Document Clustering Learning Monolingual Compositional Representations via Bilingual Supervision Crosslingual Document Embedding as Reduced-Rank Ridge Regression Wasserstein distances for evaluating cross-lingual embeddings Word Embedding based Semantic Cross-Lingual Document Alignment in Comparable Corpora Bilingual Word Representations with Monolingual Quality in Mind Representation learning using step-based deep multi-modal autoencoders Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis SWAT: Cross-Lingual Lexical Substitution using Local Context Matching, Bilingual Dictionaries and Machine Translation SemEval-2010 Task 2: Cross-Lingual Lexical Substitution IIT at TREC 2002 Linear Combinations Based on Document Structure and Varied Stemming for Arabic Retrieval Lost in Embedding Space: Explaining Cross-Lingual Task Performance with Eigenvalue Divergence Cross-Lingual Cross-Media Content Linking: Annotations and Joint Representations (Dagstuhl Seminar 15201) Combining Machine Learning and Natural Language Processing for Language-Specific, Multi-Lingual, and Cross-Lingual Text Summarization: A Wide-Ranging Overview The Semantic Web, Fourth Asian Conference, ASWC 2009, Shanghai, China, December 6-9, 2009. Proceedings UvT-WSD1: A Cross-Lingual Word Sense Disambiguation System Sampling Precision to Depth 9000: Evaluation Experiments at NTCIR-6 Intelligence and security informatics : first NSF/NIJ symposium, ISI 2003, Tucson, AZ, USA, June 2-3, 2003 : proceedings Ranking Multilingual Documents Using Minimal Language Dependent Resources Achieving Secure and Efficient Cloud Search Services: Cross-Lingual Multi-Keyword Rank Search Over Encrypted Cloud Data
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COLING ICASSP CoNLL SemEval@NAACL-HLT CoNLL Shared Task (1) Transactions of the Association for Computational Linguistics IJCNLP ACL (2) WSDM FIRE VS@HLT-NAACL Transactions of the Association for Computational Linguistics ACL SemEval@ACL SEW@NAACL-HLT TREC SemEval@ACL NTCIR CICLing (2)
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