University of Glasgow at WebCLEF 2005: experiments in per-field normalisation and language specific stemming

We participated in the WebCLEF 2005 monolingual task. In this task, a search system aims to retrieve relevant documents from a multilingual corpus of Web documents from Web sites of European governments. Both the documents and the queries are written in a wide range of European languages. A challenge in this setting is to detect the language of documents and topics, and to process them appropriately. We develop a language specific technique for applying the correct stemming approach, as well as for removing the correct stopwords from the queries. We represent documents using three fields, namely content, title, and anchor text of incoming hyperlinks. We use a technique called per-field normalisation, which extends the Divergence From Randomness (DFR) framework, to normalise the term frequencies, and to combine them across the three fields. We also employ the length of the URL path of Web documents. The ranking is based on combinations of both the language specific stemming, if applied, and the per-field normalisation. We use our Terrier platform for all our experiments. The overall performance of our techniques is outstanding, achieving the overall top four performing runs, as well as the top performing run without metadata in the monolingual task. The best run only uses per-field normalisation, without applying stemming

MIRACLE Retrieval Experiments with East Asian Languages

This paper describes the participation of MIRACLE in NTCIR 2005 CLIR task. Although our group has a strong background and long expertise in Computational Linguistics and Information Retrieval applied to European languages and using Latin and Cyrillic alphabets, this was our first attempt on East Asian languages. Our main goal was to study the particularities and distinctive characteristics of Japanese, Chinese and Korean, specially focusing on the similarities and differences with European languages, and carry out research on CLIR tasks which include those languages. The basic idea behind our participation in NTCIR is to test if the same familiar linguisticbased techniques may also applicable to East Asian languages, and study the necessary adaptations

Crosslingual Document Embedding as Reduced-Rank Ridge Regression

There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages. In this paper, we shift the focus from words to documents and introduce a method for embedding documents written in any language into a single, language-independent vector space. For training, our approach leverages a multilingual corpus where the same concept is covered in multiple languages (but not necessarily via exact translations), such as Wikipedia. Our method, Cr5 (Crosslingual reduced-rank ridge regression), starts by training a ridge-regression-based classifier that uses language-specific bag-of-word features in order to predict the concept that a given document is about. We show that, when constraining the learned weight matrix to be of low rank, it can be factored to obtain the desired mappings from language-specific bags-of-words to language-independent embeddings. As opposed to most prior methods, which use pretrained monolingual word vectors, postprocess them to make them crosslingual, and finally average word vectors to obtain document vectors, Cr5 is trained end-to-end and is thus natively crosslingual as well as document-level. 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.Comment: In The Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19

GeoCLEF 2007: the CLEF 2007 cross-language geographic information retrieval track overview

GeoCLEF ran as a regular track for the second time within the Cross Language Evaluation Forum (CLEF) 2007. The purpose of GeoCLEF is to test and evaluate cross-language geographic information retrieval (GIR): retrieval for topics with a geographic specification. GeoCLEF 2007 consisted of two sub tasks. A search task ran for the third time and a query classification task was organized for the first. For the GeoCLEF 2007 search task, twenty-five search topics were defined by the organizing groups for searching English, German, Portuguese and Spanish document collections. All topics were translated into English, Indonesian, Portuguese, Spanish and German. Several topics in 2007 were geographically challenging. Thirteen groups submitted 108 runs. The groups used a variety of approaches. For the classification task, a query log from a search engine was provided and the groups needed to identify the queries with a geographic scope and the geographic components within the local queries

Evaluation of MIRACLE approach results for CLEF 2003

This paper describes MIRACLE (Multilingual Information RetrievAl for the CLEf campaign) approach and results for the mono, bi and multilingual Cross Language Evaluation Forum tasks. The approach is based on the combination of linguistic and statistic techniques to perform indexing and retrieval tasks

GeoCLEF 2006: the CLEF 2006 Ccross-language geographic information retrieval track overview

After being a pilot track in 2005, GeoCLEF advanced to be a regular track within CLEF 2006. The purpose of GeoCLEF is to test and evaluate cross-language geographic information retrieval (GIR): retrieval for topics with a geographic specification. For GeoCLEF 2006, twenty-five search topics were defined by the organizing groups for searching English, German, Portuguese and Spanish document collections. Topics were translated into English, German, Portuguese, Spanish and Japanese. Several topics in 2006 were significantly more geographically challenging than in 2005. Seventeen groups submitted 149 runs (up from eleven groups and 117 runs in GeoCLEF 2005). The groups used a variety of approaches, including geographic bounding boxes, named entity extraction and external knowledge bases (geographic thesauri and ontologies and gazetteers)

Sub-word indexing and blind relevance feedback for English, Bengali, Hindi, and Marathi IR

The Forum for Information Retrieval Evaluation (FIRE) provides document collections, topics, and relevance assessments for information retrieval (IR) experiments on Indian languages. Several research questions are explored in this paper: 1. how to create create a simple, languageindependent corpus-based stemmer, 2. how to identify sub-words and which types of sub-words are suitable as indexing units, and 3. how to apply blind relevance feedback on sub-words and how feedback term selection is affected by the type of the indexing unit. More than 140 IR experiments are conducted using the BM25 retrieval model on the topic titles and descriptions (TD) for the FIRE 2008 English, Bengali, Hindi, and Marathi document collections. The major findings are: The corpus-based stemming approach is effective as a knowledge-light term conation step and useful in case of few language-specific resources. For English, the corpusbased stemmer performs nearly as well as the Porter stemmer and significantly better than the baseline of indexing words when combined with query expansion. In combination with blind relevance feedback, it also performs significantly better than the baseline for Bengali and Marathi IR. Sub-words such as consonant-vowel sequences and word prefixes can yield similar or better performance in comparison to word indexing. There is no best performing method for all languages. For English, indexing using the Porter stemmer performs best, for Bengali and Marathi, overlapping 3-grams obtain the best result, and for Hindi, 4-prefixes yield the highest MAP. However, in combination with blind relevance feedback using 10 documents and 20 terms, 6-prefixes for English and 4-prefixes for Bengali, Hindi, and Marathi IR yield the highest MAP. Sub-word identification is a general case of decompounding. It results in one or more index terms for a single word form and increases the number of index terms but decreases their average length. The corresponding retrieval experiments show that relevance feedback on sub-words benefits from selecting a larger number of index terms in comparison with retrieval on word forms. Similarly, selecting the number of relevance feedback terms depending on the ratio of word vocabulary size to sub-word vocabulary size almost always slightly increases information retrieval effectiveness compared to using a fixed number of terms for different languages

Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning

While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages. This is primarily due to a lack of data set that are suitable to train ranking algorithms. In this paper, we tackle the lack of data by leveraging pre-trained multilingual language models to transfer a retrieval system trained on English collections to non-English queries and documents. Our model is evaluated in a zero-shot setting, meaning that we use them to predict relevance scores for query-document pairs in languages never seen during training. Our results show that the proposed approach can significantly outperform unsupervised retrieval techniques for Arabic, Chinese Mandarin, and Spanish. We also show that augmenting the English training collection with some examples from the target language can sometimes improve performance.Comment: ECIR 2020 (short

Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval

Although more and more language pairs are covered by machine translation services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application which needs translation functionality of a relatively low level of sophistication since current models for information retrieval (IR) are still based on a bag-of-words. The Web provides a vast resource for the automatic construction of parallel corpora which can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways in a retrieval model. In this paper, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost.Comment: 37 page

An evaluation resource for geographic information retrieval

In this paper we present an evaluation resource for geographic information retrieval developed within the Cross Language Evaluation Forum (CLEF). The GeoCLEF track is dedicated to the evaluation of geographic information retrieval systems. The resource encompasses more than 600,000 documents, 75 topics so far, and more than 100,000 relevance judgments for these topics. Geographic information retrieval requires an evaluation resource which represents realistic information needs and which is geographically challenging. Some experimental results and analysis are reported