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EVALITA. Evaluation of NLP and Speech Tools for Italian

Pierpaolo Basile
Franco Cutugno
Malvina Nissim
et al.

Part II: EVALITA 2016: Task overviews and participants reports

Question Answering System for Frequently Asked Questions

Divyanshu Bhardwaj, Partha Pakray, Jereemi Bentham, Saurav Saha et Alexander Gelbukh


Question Answering (QA) is an important aspect of Natural Language Processing. It comprises building a system that automatically answers questions sought in natural language. Frequently Asked Questions (FAQs) are a set of listed questions and answers concerning a specific topic, which are most likely to be enquired by a user. This paper deals with developing an open domain QA system for retrieving a list of relevant FAQs related to the query issued by the user. Our approach combines the orthodox AND/OR searching with the Combinatorics searching technique which is able to produce an exhaustive list of results for a particular query generated.

Question Answering (QA) un aspetto importante di Natural Language Processing. Si compone di costruire un sistema che risponde automaticamente alle domande cercato in linguaggio naturale. Domande frequenti (FAQ) sono un insieme di domande elencate e risposte riguardanti un argomento specifico, che hanno più probabilità di indagato da un utente. Questo documento si occupa di sviluppo di un sistema di QA dominio aperto per il recupero di un elenco di domande frequenti pertinenti relativi alla query emesso da parte dell’utente. Il nostro approccio combina l’ortodossa e / o la ricerca con la tecnica di ricerca combinatorio che in grado di produrre un elenco esaustivo dei risultati per una determinata query generato.

Texte intégral

This work presented here falls under the research project Grant No. YSS/2015/000988 and supported by the Department of Science & Technology (DST) and Science and Engineering Research Board (SERB), Govt. of India. The authors would like to acknowledge the Department of Computer Science & Engineering, National Institute of Technology, Mizoram for providing infrastructural facilities in order to facilitate research on this task.

1 Introduction

1Question Answering (QA) is an emerging topic in today’s world. It is an aggregate of Information Retrieval (IR) and Natural Language Processing (NLP) and is concerned with developing an automated engine which is able to respond to the queries presented by users in natural language.

2Frequently Asked Questions (FAQs) represent an effective and efficient way to quickly resolve queries posed by users. They are usually represented as an ensembled list of questions and their answers.

3Searching within FAQs can be a tedious task. This becomes even more drawn out when paraphrasing comes into fray. As a result the user is pushed into a maze of questions and answers having to manually look for a particular one as shown in figure 1. It is here that a QA system comes of utmost importance retrieving the particular desired query instantly.

Figure 1: FAQs of Microsoft Download Center

Figure 1: FAQs of Microsoft Download Center

4The rest of this paper is organised as follows, Section 2 describes the corpus and its preprocessing, Section 3 describes our system’s architecture and the tools used, Section 4 describes the experiment. Section 5 describes the performance of the system, Section 6 analyses the results and Section 7 describes the conclusion and future works.

2 Corpus and Preprocessing

5The corpus obtained from the QA4FAQ task website1 provided us with FAQ in .csv (comma separated values) format, using ; as separator and in XML format. The CSV file was in UTF-8 format and contained 4 fields viz.,

  1. id: a number that uniquely identifies the FAQ;

  2. question: the question text of the current FAQ;

  3. answer: the answer text of the current FAQ;

  4. tag: a set of tags separated by ,.

6An example of the data provided is given below:

193;Cosa significa AEEGSI?; l’Autorit per l’Energia Elettrica il Gas ed il Sistema Idrico.;acqua, acquedotto, distribuzione, AEEGSI

2.1 Parsing

7For the purpose of pre-processing of the training data we developed a CSV parser which could extract the ID and the rest of the parts. Development dataset had 406 files with id, question, answer, tag(s). We extracted the question, answer and tags in a file and saved it in the file named ID.txt.

2.2 Stopword Removal

8In order to increase the efficiency of our input data, we decided to perform stopwords removal. Words which occur in 80% of the documents in the collection are the stop words. However while searching for a list of Italian stopwords, we realised that the existing ones had only 133 to 399 stopwords2 3 4. So, we merged them and developed our own exhaustive Italian stopword corpus from the existing ones. This corpus5 had approximately 546 unique stopwords in total. This operation helped us in getting rid of the unwanted words which would hinder the system’s performance.

3 System Architecture

9The architecture of our system is shown in figure 2.

Figure 2: Architecture of the implemented system

Figure 2: Architecture of the implemented system

10The architecture may be divided into two distinct parts as shown in figure. One part contains the architecture of Nutch6 enclosed in the rectangle. It contains all the basic components essential in the implementation of a Search Engine. The other part represents the aggregation of the searching techniques to be adopted while searching the FAQs. This includes a module that processes the queries obtained for both AND/OR searching as well as combinatorics based searching.The two major steps involved in developing the architecture were Crawling & Indexing and Searching (described in Section 4).

11The steps involved in crawling and indexing are described below:

  1. Run a generic Java code taking the ids (taken from ID.txt) as the input to generate URL seeds.

  2. Injector injects the list of seed URLs into the crawlDB.

  3. Generator takes the list of seed URLs from crawlDB, forms fetch list, adds crawl generate folder into the segments.

  4. These fetch lists are used by fetchers to fetch the raw content of the document. It is then stored in segments.

  5. Parser is called to parse the content of the document and parsed content is stored back in segments.

  6. The links are inverted in the link graph and stored in LinkDB.

  7. Indexing the terms present in segments is done and indices are updated in the segments.

  8. Information on the newly fetched documents are updated on the crawlDB.

4 Experiments

12The corpus obtained after pre-processing was experimented upon by means of various methodologies. A total of 1132 FAQs were available in the test data set. A prototype system was created by feeding the input data into Nutch. We performed two separate runs so as to perform a comparative study between unprocessed and pre processed data.

13We used Nutch’s own configuration for the Indexing, Searching and Ranking of the data for one of the runs and implemented our own configuration for the other run. The ranking provided by Nutch may be explained using the following equation:

Figure 3: Nutch’s Ranking Equation

Figure 3: Nutch’s Ranking Equation


  1. queryNorm() : indicates the normalization factor for the query.

  2. coord() : indicates how many query terms are present in the given document.

  3. norm() : score indicating field based normalization factor.

  4. tf: term frequency

  5. idf: inverse document frequency

  6. t.boost() : score indicating the importance of terms occurrence in a particular field

15Apart from this, we developed our own configuration which was a combination of both the traditional AND/OR search along with the Combinatorics approach. To implement this Combinatorics approach, we split the query by space separator and all possible combinations of a word in query were generated. This is the methodology adopted in subset generation from a given set. So, given n number of words in a query after removing stopwords, we would have 2n 1 possible combinations of query. These were then used for searching by Nutch and ranking was done based on the ranking algorithm we developed. Benefit of this approach was that, it was an exhaustive search and maximum number of relevant results would be retrieved using it using proper ranking algorithm.

16This approach could be explained using the following example:

Consider the following query:

numero verde aqp

For this query, all the possible combinations would be created in the following order :

numero verde aqp numero verde verde aqp

numero aqp numero verde



17From this example we can clearly visualize how this approach would be extremely efficient in retrieving the most relevant answers for queries provided by the user. After applying this approach, we were left with 29 unanswered queries. We also implemented our own ranking system which ranked the retrieved pages in the following way :

Consider a query of 4 words. We used a 4 point scale to rank the pages with the highest score being assigned to the page with 4*(number of matches) Thus, for a query of length n, the highest match would be assigned to n*(number of matches). Assuming we have a query of n words, all possible combinations i.e, 2n 1 possible queries were to be ranked according to the above mentioned algorithm.

Consider the query following query:

numero verde

and let the text be il numero verde non verde, un numero che può essere dipinta di verde.

Ranking of queries would be done as :

  1. numero verde : 2*1 = 2

  2. numero : 1*2 = 2

  3. verde : 1*3 = 3

18Since we get the highest score from the query verde so the most relevant document will be fetched by verde. Our system retrieved results based on this methodology.

5 Performance

19The relevant statistics of both the runs based on the experiments performed are outlined in Table 1.

Table 1: Statistics of both approaches

Run 1

Total no. of


No. of queries


No. of queries





Run 2

Total no. of


No. of queries


No. of queries





20As can be inferred from Table 1, while during Run 1 there were a large number of unanswered queries, they were significantly reduced in Run 2. This was possible due to the combinatorics approach used in Run 2. The performance of our system in both the runs is depicted in Table 2.

Table 2: Performance of NLP-NITMZ in both runs


Score Obtained

Run 1


Run 2


21Systems were ranked according to accuracy@1. In this method of ranking the precision of the system was computed taking into account only the first answer generated by the system. The formulation of c@1 is given as below:

Figure 4: Formula for c@1

Figure 4: Formula for c@1


  1. nR : number of questions correctly answered

  2. nR : number of questions unanswered

  3. n: total number of questions

6 Discussion

23As the evaluation was done according to accuracy@1 which considered only the first answer retrieved by the systems, the results obtained weren’t extremely accurate. We however managed to implement a search engine which was 97.33% accurate in retrieving queries, which resulted in a trivial amount of unanswered queries. This system conveyed a lot of information which made us realise that combinatorics can be an extremely powerful tool for searching if implemented in a proper way. However, the relevancy of the results obtained would depend on how efficiently the ranking is done.

7 Conclusion and Future Direction

24In this paper, we intended to frame an automated Question Answering (QA) system for Frequently Asked Questions (FAQs). We described the preprocessing of the corpus and the experiments performed on them. We also described the combinatorics approach used for searching. While the evaluation results were only decent, we did manage to materialise a remarkably accurate search engine for FAQs. Now that we have an adept search engine we would next endeavour towards perfecting our ranking techniques and algorithms in order to take steps towards implementing a state of the art QA system.


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Table des illustrations

Titre Figure 1: FAQs of Microsoft Download Center
Fichier image/jpeg, 380k
Titre Figure 2: Architecture of the implemented system
Fichier image/jpeg, 236k
Titre Figure 3: Nutch’s Ranking Equation
Fichier image/jpeg, 96k
Titre Figure 4: Formula for c@1
Fichier image/jpeg, 40k


Dept. of CSE NIT Mizoram India -

Dept. of CSE NIT Mizoram India -

Dept. of CSE NIT Mizoram India -

Dept. of CSE NIT Mizoram India -

CIC IPN Mexico Mexico -


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