Recurrent neural networks are increasingly used to classify text data, displacing feed-forward networks. This article is a demonstration of how to classify text using Long Term Term Memory (LSTM) network and their modifications, i.e. Bidirectional LSTM network and Gated Recurrent Unit. We present the superiority of this method over other algorithms for text classification on the example of three sets: Spambase Data Set, Farm Advertisement and Amazon book reviews. The results of the first two datasets were compared with AdaBoost ensemble of feedforward neural networks. In the case of the last database, the result is compared to the bag-of-words algorithm. In this article, we focus on classifying two groups in the first two collections, since we are only interested in whether something is classified into a SPAM or an eligible message. In the last dataset, we distinguish three classes.

LSTM Recurrent Neural Networks for Short Text and Sentiment Classification

In this paper we give an overview of the 2007 evaluation campaign for the International Workshop on Spoken Language Translation (IWSLT) 1 . As with previous evaluation campaigns, the primary focus of the workshop was the translation of spoken language in the travel domain. This year there were four language pairs; the translation of Chinese, Italian, Arabic, and Japanese into English. The input data consisted of the output of ASR systems for read speech and clean text. The exceptions were the challenge task of the Italian English language pair which used spontaneous speech ASR outputs and transcriptions and the Chinese English task which used only clean text. A new characteristic of this year’s evaluation campaign was an increased focus on the sharing of resources. Participants were requested to submit the data and supplementary resources used in building their systems so that the other participants might be able to take advantage of the same resources. A second new characteristic this year was the focus on the human evaluation of systems. Each primary run was judged in the human evaluation for every task using a straightforward ranking of systems. This year's workshop saw an increased participation over last year's workshop. This year 24 groups submitted runs to one or more of the tasks, compared to the 19 groups that submitted runs last year [1]. Automatic and human evaluation were carried out to measure MT performance under each condition, ASR system outputs for read speech, spontaneous travel dialogues, and clean text.

Overview of the IWSLT 2007 Evaluation Campaign

This paper gives an overview of the evaluation campaign results of the International Workshop on Spoken Language Translation (IWSLT) 20091. In this workshop, we focused on the translation of task-oriented human dialogs in travel situations. The speech data was recorded through human interpreters, where native speakers of different languages were asked to complete certain travel-related tasks like hotel reservations using their mother tongue. The translation of the freely-uttered conversation was carried out by human interpreters. The obtained speech data was annotated with dialog and speaker information. The translation directions were English into Chinese and vice versa for the Challenge Task, and Arabic, Chinese, and Turkish, which is a new edition, into English for the standard BTEC Task. In total, 18 research groups participated in this year’s event. Automatic and subjective evaluations were carried out in order to investigate the impact of task-oriented human dialogs on automatic speech recognition (ASR) and machine translation (MT) system performance, as well as the robustness of state-of-the-art MT systems for speech-tospeech translation in a dialog scenario.

Overview of the IWSLT 2009 evaluation campaign.

This paper presents an open and flexible methodological framework for the automatic acquisition of multiword expressions (MWEs) from monolingual textual corpora. This research is motivated by the importance of MWEs for NLP applications. After briefly presenting the modules of the framework, the paper reports extrinsic evaluation results considering two applications: computer-aided lexicography and statistical machine translation. Both applications can benefit from automatic MWE acquisition and the expressions acquired automatically from corpora can both speed up and improve their quality. The promising results of previous and ongoing experiments encourage further investigation about the optimal way to integrate MWE treatment into these and many other applications.

/pdf/a-generic-framework-for-multiword-expressions-treatment-from-24ua4ge00a.pdf

A Generic Framework for Multiword Expressions Treatment: from Acquisition to Applications

This paper gives an overview of the evaluation campaign results of the International Workshop on Spoken Language Translation (IWSLT) 20081. In this workshop, we focused on the translation of spontaneous speech recorded in a real situation and the feasability of pivot-language-based translation approaches. The translation directions were English into Chinese and vice versa for the Challenge Task, Chinese into English and English into Spanish for the Pivot Task, and Arabic, Chinese, Spanish into English for the standard BTEC Task. In total, 19 research groups building 58 MT engines participated in this year’s event. Automatic and subjective evaluations were carried out in order to investigate the impact of spontaneity aspects of field data experiments on automatic speech recognition (ASR) and machine translation (MT) system performance as well as the robustness of stateof-the-art MT systems towards speech-to-speech translation in real environments.

Overview of the IWSLT 2008 evaluation campaign.

This paper proposes a method to recommend paragraphs of Wikipedia pages as a travel guide. This method helps tourists (users) read attractive descriptions of Wikipedia pages during their travel. In order to rate paragraphs, importance, non-redundancy, and novelty of the paragraphs are evaluated based on a Tf-Idf manner. Especially, novelty is done by user's experience estimated from geo-tagged tweets located to places that the user visited in the past. As the results of the experiments, we confirmed the geo-tagged tweets reflected user's experience and the recommendations exceeded the expectation of MAP criteria.

Recommending paragraphs of wikipedia pages as a travel guide

A large-scale sentence pattern dictionary (SP-dictionary) for Japanese compound and complex sentences has been developed. The dictionary has been compiled based on the non-compositional language model. Sentences with 2 or 3 predicates are extracted from a Japanese-to-English parallel corpus of 1 million sentences, and the compositional constituents contained within them are generalized to produce a SP-dictionary containing a total of 215,000 pattern pairs. In evaluation tests, the SP-dictionary achieved a syntactic coverage of 92% and a semantic coverage of 70%.

https://unicorn.ike.tottori-u.ac.jp/ikehara/paper/ICCPOL_2006_12/paper.pdf

Pattern dictionary development based on non-compositional language model for japanese compound and complex sentences

Pattern Dictionary Development based on Non-Compositional Language Model for Japanese Compound and Complex Sentences

The present paper proposes a tool to help analysts make tourism development ideas while reading blog articles. Since reading the entire text of an article is time consuming, it is useful to extract from the blog articles significant sentences that are relevant to tourism development. The proposed tool extracts such sentences using a support vector machine (SVM) and an active learning method. In the first learning step, the proposed tool is trained using corpora that include hint-tags. The analyst then provides target blog articles to the tool and receives sentences as the results of the SVM classification. Some of these sentences are analyzed manually in order to annotate new hint-tags. In the second learning step, both the original corpora and the annotation results are used. Finally, the analyst reads plausible sentences extracted from the second classification of the target articles. In the experiments, we confirmed that the proposed active learning method provides better results than the simple learning method.

An Active Learning Based Support Tool for Extracting Hints of Tourism Development from Blog Articles

We have developed a two-stage machine translation (MT) system. The first stage is a rule-based machine translation system. The second stage is a normal statistical machine translation system. For Japanese-English machine translation, first, we used a JapaneseEnglish rule-based MT, and we obtained "ENGLISH" sentences from Japanese sentences. Second, we used a standard statistical machine translation. This means that we translated "ENGLISH" to English machine translation. We believe this method has two advantages. One is that there are fewer unknown words. The other is that it produces structured or grammatically correct sentences. From the results of experiments, we obtained a BLEU score of 0.2565 in the Intrinsic-JE task using our proposed method. In contrast, we obtained a BLEU score of 0.2165 in the Intrinsic-JE task using a standard method (moses). And we obtained a BLEU score of 0.2602 in the Intrinsic-EJ task using our proposed method. In contrast, we obtained a BLEU score of 0.2501 in the Intrinsic-EJ task using a standard method (moses). This means that our proposed method was effective for the IntrinsicJE and Intrinsic-EJ task. For the future study, we will try to improve the performance by optimizing parameters.

/pdf/statistical-machine-translation-adding-rule-based-machine-t18634gwl0.pdf

Statistical Machine Translation Adding Rule-based Machine Translation.

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