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The authors would like to acknowledge the contribution made by Amit Gupta of the Department of CSE, IIT Bombay.
Citations
... Taksonomi Bloom merupakan sebuah kerangka dasar yang digunakan dalam pengkategorian tujuan kurikulum, penyusunan ujian, dan visi dari pendidikan lainnya. Taksonomi Expected Answer Type menganalisis jawaban pertanyaan pada sebuah paragraf menjadi salah satu dari kategori EAT, setelah menemukan kategori nya maka tipe pertanyaannya juga akan langsung didapatkan [17]. ...
Pembuatan pertanyaan pada suatu ujian merupakan proses yang kompleks, dikarenakan proses ini membutuhkan pengetahuan dan waktu yang lama dalam penyusunannya. Penyusunan pertanyaan dapat dilakukan dengan lebih mudah, cepat, dan terstruktur dengan adanya sistem Automatic Question Generator (AQG). Aplikasi ini memanfaatkan Metode Text Matching untuk menemukan kata kunci pada suatu paragraf, dimana kata kunci ini akan diidentifikasi menggunakan Metode Expected Answer Type (EAT). Metode EAT membantu untuk mengidentifikai jenis jawaban pada suatu paragraf sehingga dapat diketahui jenis pertanyaan yang akan di generate. Jenis pertanyaan yang digunakan yaitu 5W + 1H yang terdiri dari Siapa, Dimana, Kapan, Mengapa, Apa, Bagaimana, dan Berapa Banyak. Metode selanjutnya adalah Metode Template Based yang berperan dalam menyusun kalimat pertanyaan berdasarkan template yang sudah didaftarakan sebelumnya. Pertanyaan yang dihasilkan menggunakan konsep Revisi Taksonomi Bloom, dimana pertanyaan ini terdiri dari kategori (1) mengingat (2) memahami (3) mengaplikasikan (4) menganalisis (5) mengevaluasi dan (6) mencipta. Hasil uji coba dari 14 materi pembelajaran, aplikasi dapat meghasilkan 826 pertanyaan dengan tingkat rata-rata akurasi sebesar 89%.
... In Table 4.5, we summarize the main techniques reported by participants, namely nlel (Correa et al., 2010a), bpac (Nemeskey, 2010), dict (Sabnani & Majumder, 2010), elix (Agirre et al., 2010), iles (Agirre et al., 2010), ju c (Pakray et al., 2010), uaic (Iftene et al., 2010), uiir (Toba et al., 2010b) and uned . We remarked that by almost half of the systems that have reported the used retrieval model have employed Okapi BM25 10 , while other reported models have resorted to Lucene 11 . ...
Question Answering (QA) aims to directly return succinct and accurate answers to natural language questions. Passage Retrieval (PR) is deemed to be the kernel of a typical QA system where the goal is to reduce the search space from a huge set of documents to a few number of relevant passages, from which the required answer can be found. Although there has been an abundance of work on this task, it still requires non-trivial endeavor. Recently, community Question Answering (cQA) services have evolved into a popular way of online information seeking, where users caninteract and exchange knowledge in the form of questions and answers. The Question Retrieval (QR) problem in cQA is to certain extent analogue to the PR task in traditional QA. While passage retrieval matches the user question with the document passages to search for correct excerpts in response to the user, question retrieval matches the user’s question with the archived questions to find out those that are semantically similar to the queried one. By the time, with the sharp increase of community archives and the accumulation of duplicated questions, the QR problem has become increasingly alarming and it remains more challenging than PR due to the shortness of the community questions as well as the lexical gap problem. In this thesis, we tackle both tasks: PR in open domain QA and QR in cQA. We propose different approaches to improve these critical problems in different languages. For PR, we were mainly based on SVM and n-grams while for QR, we were opted for neural networks mainly word embeddings and Long Short-Term Memory (LSTM). We run our experiments on large scale data sets from CLEF and Yahoo! Answers in different languages to show the efficiency and generality of our proposed approaches. Interestingly, the obtained results transcend that of other previously proposed ones.
... • In 2011, Jadavpur University from Kolkata [12] considered retrieving paragraphs instead of sentences. The idea was to adapt the system introduced in [62] and used as part of the participation in the Paragraph Selection (PS) Task and Answer Selection (AS) Task of QA@CLEF 2010 -ResPubliQA [64]. It appeared that the use of paragraphs improves readability but does not allow informativeness optimization [72]. ...
Microblogging platforms such as Twitter are increasingly used for on-line client and market analysis. This motivated the proposal of a new track at CLEF INEX lab of Tweet Contextualization. The objective of this task was to help a user to understand a tweet by providing him with a short explanatory summary (500 words). This summary should be built automatically using resources like Wikipedia and generated by extracting relevant passages and aggregating them into a coherent summary.Running for four years, results show that the best systems combine NLP techniques with more traditional methods. More precisely the best performing systems combine passage retrieval, sentence segmentation and scoring, named entity recognition, text part-of-speech (POS) analysis, anaphora detection, diversity content measure as well as sentence reordering.This paper provides a full summary report on the four-year long task. While yearly overviews focused on system results, in this paper we provide a detailed report on the approaches proposed by the participants and which can be considered as the state of the art for this task. As an important result from the 4 years competition, we also describe the open access resources that have been built and collected. The evaluation measures for automatic summarization designed in DUC or MUC were not appropriate to evaluate tweet contextualization, we explain why and depict in detailed the LogSim measure used to evaluate informativeness of produced contexts or summaries. Finally, we also mention the lessons we learned and that it is worth considering when designing a task.
... In this section we will describe Information Retrieval [14] for Hindi Songs Lyrics. The Apache Lucene IR system has been used for the present task. ...
The article presents the experiments carried out as part of the participation in Shared Task on Transliterated Search 1 at Forum for Information Retrieval Evaluation (FIRE) in 2013. In this shared task there were two subtasks. For Subtask 1, Trigram Model (Tri), Joint Source Channel Model (JSC), Modified Joint Source Channel Model (MJSC), Improved Modified Joint Source-Channel Model (IMJSC) have been used for transliteration. For training purpose we have used NEWS 2009 Machine Transliteration Shared Task 2 datasets. For Subtask 2, Information Retrieval (IR) purposes we have used Apache Lucene 3 . We have submitted two system results (runs) for Subtask 1 and three system results (runs) for Subtask 2. For Subtask 1, we have submitted one run for English-Bangla and one run for English-Hindi. For English-Bangla run, the system demonstrated Transliteration-Fscore of 0.1841, Eng-Fscore of 0.5768 and L-Fscore of 0.7551. For English-Hindi, the system demonstrated Transliteration-Fscore of 0.2515, Eng-Fscore of 0.7036 and L-Fscore of 0.8519. For Subtask-2, three runs of nDCG@5 is 0.2049, 0.5229, 0.5613 and nDCG@10 is 0.2073, 0.5198, and 0.5596.
The article presents the experiments carried out as part of the participation in the pilot task (Modality and Negation) 1 of QA4MRE@CLEF 2012. Modality and Negation are two main grammatical devices that allow to express extra-propositional aspects of meaning. Modality is a grammatical category that allows to express aspects related to the attitude of the speaker towards statements. Negation is a grammatical category that allows to change the truth value of a proposition. The input for the systems is a text where all events expressed by verbs are identified and numbered the output should be a label per event. The possible values are: mod, neg, neg-mod, none. In the developed system, we first build a database for modal verbs of two categories: epistemic and deontic. Also, we used a negative verb list of 1877 verbs. This negative verb list has been used to identify negative modality. We extract the each tagged events from each sentences. Then our system check modal verbs by that database from each sentences. If any modal verbs is found before that an event then that event should be modal verb and tagged as mod. If modal verb is there and also negeted words is found before that evet then that event should negeted mod and tagged as neg-mod. If no modal verb is found before that an event but negeted word are found before that event then that event should be negeted and tagged as neg. Otherwise the event should tagged as none. We trained our system by traing data (sample data) that was provided by QA4MRE organizer. Then we are tested our system on test dataset. In test data set there are eight documents, two per each of the four topics such as Alzheimer, music and society, AIDs and climate change. Our system overall accuracy is 0.6262 (779 out of 1244).
The article presents the experiments carried out as part of the participation in the pilot task (Modality and Negation)1 of QA4MRE@CLEF 2012. Modality and Negation are two main grammatical devices that allow to express extra-propositional aspects of meaning. Modality is a grammatical category that allows to express aspects related to the attitude of the speaker towards statements. Negation is a grammatical category that allows to change the truth value of a proposition. The input for the systems is a text where all events expressed by verbs are identified and numbered the output should be a label per event. The possible values are: mod, neg, neg-mod, none. In the developed system, we first build a database for modal verbs of two categories: epistemic and deontic. Also, we used a negative verb list of 1877 verbs. This negative verb list has been used to identify negative modality. We extract the each tagged events from each sentences. Then our system check modal verbs by that database from each sentences. If any modal verbs is found before that an event then that event should be modal verb and tagged as mod. If modal verb is there and also negeted words is found before that evet then that event should negeted mod and tagged as neg-mod. If no modal verb is found before that an event but negeted word are found before that event then that event should be negeted and tagged as neg. Otherwise the event should tagged as none. We trained our system by traing data (sample data) that was provided by QA4MRE organizer. Then we are tested our system on test dataset. In test data set there are eight documents, two per each of the four topics such as Alzheimer, music and society, AIDs and climate change. Our system overall accuracy is 0.6262 (779 out of 1244).
The article presents the experiments carried out as part of the participation in the QA track of INEX 2011. We have submitted two runs. The INEX QA task has two main sub tasks, Focused IR and Automatic Summarization. In the Focused IR system, we first preprocess the Wikipedia documents and then index them using Nutch. Stop words are removed from each query tweet and all the remaining tweet words are stemmed using Porter stemmer. The stemmed tweet words form the query for retrieving the most relevant document using the index. The automatic summarization system takes as input the query tweet along with the tweet’s text and the title from the most relevant text document. Most relevant sentences are retrieved from the associated document based on the TF-IDF of the matching query tweet, tweet’s text and title words. Each retrieved sentence is assigned a ranking score in the Automatic Summarization system. The answer passage includes the top ranked retrieved sentences with a limit of 500 words. The two unique runs differ in the way in which the relevant sentences are retrieved from the associated document. Our first run got the highest score of 432.2 in Relaxed metric of Readability evaluation among all the participants.
