As the world moves towards increasing forms of digitization, the creation of text corpora has become an important activity for NLP and other fields of research. Parliamentary data is a rich corpus of discourse on a wide array of topics. The Lok Sabha website provides access to all kinds of reports, debates, bills related to the proceedings of the house. Similarly, the Rajya Sabha website also contains debates, bills, reports introduced in the house. The Lok Sabha website also contains information about members of the parliament who are elected by the people and debate in the house. Since the data is unstructured , it cannot be computationally analyzed. There is a need to shape the data into a structured format for analysis. This data is important as it can be used to visualize person, party and agenda level semantics in the house.The data that we get from parliamentary proceedings has presence of sarcasm, interjections and allegations which makes it difficult to apply standard NLP techniques BIBREF0 . Members of the parliament discuss various important aspects and there is a strong purpose behind every speech. We wanted to analyze this particular aspect. Traditional polar stances (for or against) do not justify for the diplomatic intricacies in the speeches. We created this taxonomy to better understand the semantics i.e the pragmatics of the speeches and to give enriched insights into member's responses in a speech. The study of the speaker's meaning, not focusing on the phonetic or grammatical form of an utterance, but instead on what the speaker's intentions and beliefs are is pragmatics. Pragmatics is a sub-field of linguistics and semiotics that studies the ways in which context contributes to meaning.After thorough investigation of many speeches we found that the statements made by members cannot be deemed strictly "for or against" a bill or government. A person maybe appreciating a bill or government's effort in one part of a speech but also asking attention to other contentious issues. Similarly, a person criticizing government for an irresponsible action could be giving some constructive suggestions elsewhere. A political discourse may not always be polar and might have a higher spectrum of meaning. After investigating and highlighting statements with different intentions we came up with a minimal set of 4 mutually exclusive categories with different degrees of correlation with the traditional two polar categories (for and against). It is observed that any statement by a participating member will fall into one of these categories namely - Appreciation, Call for Action, Issue, Blaming.For example, if the debate consists of more of issues, one can infer that the bill is not serving the its purpose in a well manner. Also, this preliminary step will lead to new areas of research such as detection of appreciation, blame in similar lines of argument mining which is evolving in the recent years in the field of linguistics. We will quote portions of a few speeches which will give an idea of the data being presented:This city has lost its place due to negligence of previous governments and almost all industries have migrated from here and lack of infrastructure facilities, business is also losing its grip. It is very unfortunate that previous UP Governments also did not do any justice to this city.  - Shri Devendra Singh Bhole, May 03, 2016As evident, the speaker is clearly blaming the previous governments for negligence on the city. In this sense the data is very rich and a lot of linguistic research is possible. Researchers can work on different aspects such as detection of critique made by members, suggestions raised by members etc. Given the data, it can be used for rhetoric, linguistic, historical, political and sociological research. Parliamentary data is a major source of socially relevant content. A new series of workshops are being conducted for the sole purpose of encouraging research in parliamentary debates ParlClarin.As a preliminary step, we created four major categories of the speeches spoken by the parliament members. The definitions and examples of the four categories are explained in the below tables respectively. The examples are taken from a debate on NABARD bill in Lok Sabha.A speech can be labelled with multiple categories as members can appreciate and raise issues in the same speech. The following points are the contributions of this paper :Many linguists around the globe are concentrating on creation of parliamentary datasets. BIBREF1 gives an overview of the parliamentary records and corpora from countries with a focus on their availability through Clarin infrastructure. A dataset of Japanese Local Assembly minutes was created and analyzed for statistical data such as number of speakers, characters and words BIBREF2 . BIBREF3 created a highly multilingual parallel corpus of European parliament and demonstrated that it is useful for statistical machine translation. Parliamentary debates are full of arguments. Ruling party members refute the claims made by opposition party members and vice versa. Members provide strong arguments for supporting their claim or refuting other's claim. Analyzing argumentation from a computational linguistics point of view has led very recently to a new field called argumentation mining BIBREF4 . One can perform argument mining on these debates and analyze the results. BIBREF5 worked on detecting perspectives in UK political debates using a Bayesian modelling approach. BIBREF6 worked on claim detection from UK political debates using both linguistic features text and features from speech.Stance classification is a relatively new and challenging approach to deepen opinion mining by classifying a user's stance in a debate i.e whether he is for or against the topic. BIBREF7 . BIBREF8 addressed the question of whether opinion mining techniques can be used on Congressional debates or not. BIBREF9 worked on stance classification of posts in online debate forums using both structural and linguistic features. BIBREF10 trained a svm BIBREF11 classifier with features of unigrams, bigrams and trigrams to predict whether a sentence is in agreement or disagreement and achieved an F-score of 0.55 for agreement and 0.81 for disagreement on the evaluation set. No one has worked on classifying speeches based on their purpose. This is the first novel work towards this aspect.Our dataset consists of synopsis of debates in the lower house of the Indian Parliament (Lok Sabha). The dataset consists of :In Lok Sabha, a session is referred to as all the debates held in a particular cycle of sitting. There are 55 debate types identified by the Lok Sabha. Table 3 identifies some of the debate types we have considered and their frequency between the years 2014 and 2017. We opted out debate types which do not occur regularly. Each debate type has its own style of proceedings. For example, in the debate type "Government Bills", a minister places a bill on the table and discussion is carried out on the bill where as in the debate type "Matter under 377", each speaker raises an issue of which he is concerned of but no discussion is done on the issues.The creation of the dataset involved 3 steps. The first step was to scrap the pdf files from the Lok Sabha website. Each pdf file is a session. The second step was to convert the pdf files into text files for easy parsing. The challenge here was to convert this unstructured information into a structured format. The third step is to extract relevant data using pattern matching. We developed a software parser for extracting the entities such as date, debate type, member name and speech. We used regex, pattern matching code to find out patterns from the text file. For example to segregate a speaker's name from his speech, we used :re.split(":")as name of the speaker and his/her speech is separated by a colon. An example pdf can be accessed using this URL . Right now, member name and bill name are needed to be stored manually which we plan to automate too. Sometimes the pattern matching fails due to irregularities in the pdf as those were written by humans though they were negligible. We stored the structured data into a Mongo database as different debate types have different schema. The database consists of the following tables : Sessions : all the debates happened on a particular day with date, secretary general name. Members : information about the members/speakers of the parliament i.e name and party affiliation. Debates : contains the member id and the corresponding speeches, summaries and keywords. Bills : the name of the bill. Debate Type : the name of the debate type.The software parser developed is very generic. As new sessions are being added on the Lok Sabha website, the software parser automatically identifies them, parses it and stores the structured data in the database. The database has been hosted in a online database hosting site, mLab. The mongo shell can be accessed using this command in any linux machine which has mongo installed.mongo ds235388.mlab.com:35388/synopsis -u public -p publicWe have annotated 1201 speeches with the four categories mentioned above, on the speeches. We also annotated stances of the speakers towards the bill/issue that is being debated on. There are two stances one is for and other is against. The statistics of the annotated data is shown in Table 4.Two humanities students were involved in the annotation of the four categories on 1201 speeches. The annotator agreement is shown in Table 5 and is evaluated using two metrics, one is the Kohen's Kappa BIBREF12 and other is the inter annotator agreement which is the percentage of overlapping choices between the annotators.The inter annotator agreement for the stance categories were 0.92. The high values of inter annotator scores clearly explain how easy it was to delineate each category. It also signifies that the definition of the category that needed to be annotated, were very clear.We have used TextRank which is an extractive summariser BIBREF13 for summarizing the entire debate and for finding keywords in the debate. TextRank is a graph based ranking model for text processing specifically KeyPhrase Extraction and Sentence Extraction. TextRank performs better in text summarization using graph based techniques BIBREF14 . We added these two extra fields i.e the keywords extracted by TextRank and the summary created by TextRank in the debates collection. An example summary is :The last National Health Policy was framed in 2002. The Policy informs and prioritizes the role of the Government in shaping health systems in all its dimensions investment in health, organization and financing of health care services, prevention of diseases and promotion of good health through cross-sectoral action, access to technologies, developing human resources, encouraging medical pluralism, building the knowledge base required for better health, financial protection strategies and regulation and progressive assurance for health. The Policy aims for attainment of the highest possible level of health and well-being for all at all ages, through a preventive and promotive health care orientation in all developmental policies, and universal access to good quality health care services without anyone having to face financial hardship as a consequence. The Policy seeks to move away from Sick-Care to Wellness, with thrust on prevention and health care promotion. Before this, the Policy was for the Sick-Care Health Policy. Now we are making it Promotional and Preventive Health Policy. While the policy seeks to reorient and strengthen the public health systems, it also looks afresh at strategic purchasing from the private sector and leveraging their strengths to achieve national health goals. As a crucial component, the policy proposes raising public health expenditure to 2.5 per cent of the GDP in a time bound manner. The Policy has also assigned specific quantitative targets aimed at reduction of disease prevalence/incidence under three broad components viz., (a) health status and programme impact, (b) health system performance, and (c) health systems strengthening, aligned to the policy objectives. To improve and strengthen the regulatory environment, the policy seeks putting in place systems for setting standards and ensuring quality of health care. The policy advocates development of cadre of mid-level service providers, nurse practitioners, public health cadre to improve availability of appropriate health human resource. The policy also seeks to address health security and Make in India for drugs and devices. It also seeks to align other policies for medical devices and equipment with public health goals.To detect the polarity of each speech, we have used VADER BIBREF15 sentiment analysis tool. The tool uses a simple rule-based model for general sentiment analysis and generalizes more favorably across contexts than any of many benchmarks such as LIWC and SentiWordNet. The tool takes as input a sentence and gives a score between -1 and 1. The polarity of a speech is calculated by taking the sum of the polarities of the sentences. If the sum is greater than zero, then it is classified as positive, if it is less than zero, then it is classified as negative and if it is equal to zero then it is classified as neutral. The statistics of the data is presented in Table 6. A Document in session collection.[s]""blue[l]:black{"_id" : ObjectId("5a4255c789.."),"indianDate" : "Vaisakha 9,1938(Saka)","debates" : {"5999649837.." : ObjectId("5a425b5.."),"5999644a37.." : ObjectId("5a425b06..")}"englishDate" : "Friday,April 29,2016","houseName" : "LOK SABHA","secretaryGeneralName" : "ANOOP MISHRA"}The _id is the unique key assigned by the mongo database. The keys in the debates key represent the debate types from the debate types collection. The values of the debates key refer to the corresponding debates in the debates collection. A Document in member collection. The table consists of name of the member spoken, the house of the parliament and the party to which he is affliated.[s]""blue[l]:black{"_id" : ObjectId("59a8e0e983"),"name" : "Dharambir Singh,Shri","house" : "Lok Sabha","party" : "BJP"} A Document in bill collection. The table consists of the bill name.[s]""blue[l]:black{"_id" : ObjectId("59de525596..."),"name" : "THE COMPENATION BILL, 2016"} A Document in debates collection of debate type Submission Members. The table consists of all the speeches made in a particular debate in an order with summary and keywords from TextRank.[s]""blue[l]:black{"_id" : ObjectId("5a42539889.."),"topic" : "Flood situation in ...","keywords" : "water state ... ","summary" : "...","speeches" : { "1" : { "speech" : "In Tamil Nadu and in...", "memberId" : "59a92d88a0b4...", "polarity" : "Negative" }, "2" : { "speech" : "We all have witness...", "memberId" : "59cbc3ef6636...", "polarity" : "Positive" }, "3" : { ... } ... ...}The memberId refers to the _id in the member's collection.In this section, we deal with two tasks, task one is the classification of the stances the speakers take and task two is the classification of categories based on purpose. Stance classification differs from sentiment analysis. For instance, the number of speeches that were annotated as for i.e 919 had only 719 labelled as positive and the number of speeches that were annotated as against i.e 282 had only 89 as negatively labelled. So, these statistics clearly indicate the difference between polarity detection and stance classification.Text classification is a core task to many applications, like spam detection, sentiment analysis or smart replies. We used fastText and SVM BIBREF16 for preliminary experiments. We have pre-processed the text removing punctuation's and lowering the case. Facebook developers have developed fastText BIBREF17 which is a library for efficient learning of word representations and sentence classification. The reason we have used fastText is because of its promising results in BIBREF18 .We divided our training and testing data in the ratio of 8:2 for classification. As mentioned above we used fastText and SVM for both the classification tasks. We report accuracy for each class as it is a multi-label classification problem. The results are shown in Table 7 and Table 8. Also, the parameters used for fastText is described in Table 9.We have not used hs (Hierarchical Soft-max) for binary classification, instead used regular softmax as it was giving better results in fastText.For SVM, the features were the word vectors trained using word2vec BIBREF19 with dimesion size of 300 whereas for fastText, the features were the word vectors trained using character n-gram embedding. We have achieved considerably good results. We plan to annotate more and check if the accuracy increase any further. The limitation that we feel is the number of annotations being done. We approached the classification problem as one vs rest classification problem. We performed the classification on document level. Later we would like to analyze at sentence level. The least accuracy was for Issue category and the highest is for Blame category. This research will inspire researchers to take on further research on mining appreciation, blaming from text in lines with the ongoing approaches of argument mining, hate speech, sarcasm generation etc.As we increase the number of epochs in the fastText, the scores also increase as evident from Table 10, but the increase stops after 25 epochs.In this paper, we presented a dataset of synopsis of Indian parliamentary debates. We developed a generic software parser for the conversion of unstructured pdfs into structured format i.e into a relational database using mongo database software. We analyzed the purpose of the speeches of the member of parliament and categorized them into 4 major categories and provided statistics of the categories. We also tried to identify them automatically using fastText algorithm and provided the results. The analysis is done for understanding the purpose of the speeches in the parliament. We also presented our results on binary stance classification of the speeches whether the member is in favour of the debate topic or not.In future, we would like to increase the size of the dataset by including sessions of previous years which are not yet digitized. Sessions before 2009 are yet to be digitalised by the Lok Sabha editorial of India. Also we plan to include Rajya Sabha debates into the dataset. We have used fastText for classifying categories. We plan to develop a set of features to increase the accuracy of the classification task as we believe that features like party affiliation will have greater impact and experiment with other machine learning approaches.TextRank is used for summarization. We feel that for political debates, summarization should emphasize on arguments made by members unlike TextRank. In the whole debate, a lot of themes are raised by the members. The debate revolves around these themes. So, developing a model for thematic summarization with arguments will capture the complete picture of the entire debate unlike TextRank. We plan to do this as our future work on these debates. A short summary of the important themes discussed with its arguments will benefit journalists, newspaper editors, common people etc.

Q: How many speeches are in the dataset?
A: 5575 speeches

Q: What classification models were used?

Q: Do any speeches not fall in these categories?