Introduction. Description of the Project. Debopam Das

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1 Computational Analysis of Text Sentiment: A Report on Extracting Contextual Information about the Occurrence of Discourse Markers Debopam Das Introduction This report documents a particular task performed as part a larger project named Computational Analysis of Text Sentiment. The task involved the extraction of contextual information about the occurrence of discourse markers which is to be used later as a potential resource in a discourse parsing system. Discourse markers are lexical expressions, such as although, if, since and thus, which are usually drawn from different syntactic classes, such as conjunction, adverbials, and prepositional phrases. Discourse markers connect two adjacent spans, and signal the discourse relations that hold between them. Extracting the contextual information about the occurrence of discourse markers involves determining the specification of the distributional environments in which they are used to signal relations in a text. The distributional environments are defined in terms of RST (Rhetorical Structure Theory, Mann & Thompson, 1988) components, such as nucleus and satellite, and the position (beginning, middle or end) in which the discourse markers are present with respect to these components. This report is organized is as follows: In Section 2, an overview of the project Computational Analysis of Text Sentiment is given. Section 3 provides the description of the discourse parser used in the project, and elaborates on the role of discourse markers in the discourse parsing system. In section 4, the methodology involving the extraction of the contextual information about the occurrence of discourse markers is described. Finally, Section 5 provides the lists of the discourse markers and the contextual information associated with them while signalling particular discourse relations. Description of the Project Computational Analysis of Text Sentiment is an ongoing NSERC 1 -funded project led by Dr. Maite Taboada at the Department of Linguistics, Simon Fraser University. This project builds a system that automatically extracts sentiment or subjective content from unseen texts. More particularly, the project aims to determine the most effective methods and algorithms for extracting subjective content from texts, and to devise a system that will be beneficial for automatic classification of sentiment in a large corpus. Whether a text contains sentiment or not is determined by judging if it contains either positive or negative attitude, or opinions towards its subject content. Examples of texts containing 1 Natural Sciences and Engineering Research Council of Canada 1

2 sentiment include an opinion piece in a newspaper, a movie review, a report on a new product, an message, or a post on a bulletin board. The project adopts a twofold hypothesis: Given an unseen text, (i) the system is able to detect the subjective material if it contains any, and (ii) it can categorize the polarity of the subject material as positive or negative by parsing its discourse structure. The methodology adopted in this project comprises several stages. In the first stage, we conduct lower-level syntactic parsing, such as POS tagging, sentence parsing and extraction of sentence topics. The second stage employs discourse parsing which determines the discourse relations among parts of a text, and constructs the discourse structure of that text. In the third stage, the system, among the different discourse parts, identifies the most relevant parts or the parts containing the subjective content, and determines the degree (positive or negative) of the sentiment of the whole text in terms of a numeric value. The final stage evaluates the accuracy of the performance of the discourse parser compared to the human performance. Discourse Parser and the Role of Discourse Markers Discourse parsing, as mentioned above, is the second stage of the methodology which takes as input a syntactically parsed text (done in the first stage), and constructs its discourse structure. The theoretical framework adopted for discourse parsing is Rhetorical Structure Theory (RST, Mann & Thompson, 1988). RST, capable of being used in different computational applications, is one of the most widely-used theories of discourse structure which are used in discourse parsing, and natural language generation and text summarization. A rhetorical relation (or a discourse relation) within the RST framework is defined as the relationship that holds between two (sometimes more) nonoverlapping text spans. These spans are of two types: nucleus, referring to the central or the most important part of a text span, and satellite, referring to the peripheral or the secondary part of a text span. In cases where each span is equally important, they are considered as nuclei, and the relation is called multinuclear. According to the type of information or intention expressed, relations are classified into different types, such as Condition, Contrast, Concession, Cause, Background, etc. An RST structure of a text is often represented in the form of a tree diagram which represents contiguous parts of the text connected to each other by discourse relations. RST allows tree schemas to be applied in a recursive manner which provides a text an incremental hierarchical structure. The discourse parser performs two tasks: (i) segmenting discourse units and determining the status of those units as either nucleus or satellite; and (ii) identifying the relation that holds between the nucleus and satellite. For the first task, we have already built a discourse segmenter (SLSeg, Tofiloski et al., 2009) which takes an English text file as input, and segments the entire text into the elementary discourse units. For the second task, it is important to begin with a set of relations which serves as the inventory of all possible relations to be found in a text. Mann and Thompson (1988) consider the set of discourse relations to be an open set. Likewise Le Thanh (2007) also suggests that the 2

3 number of relations used in a system can be reduced, extended or modified depending on the level of detail required for a particular system. A small or a large set of relations has its own pros and cons. In the case of a small set of relations, the trees are easier to build while they lack details. On the other hand, large sets of relations render very informative trees, but they are often difficult to build. The number of relations in discourse literature ranges from two (Grosz & Sidner, 1986) to over seventy (Carlson & Marcu, 2001). For our purpose, we have prepared a set of 11 relations which, we believe, is most useful for the task of sentiment detection. These relations include Background, Cause, Circumstance, Concession, Condition, Elaboration, Evaluation, Purpose, Restatement, Result and Summary (see Taboada & Gonlzales (2010) for more detail). In the next step, the discourse parser determines the discourse relations that hold between two adjacent spans. Discourse relations are often (if not always) indicated by various signals, and discourse markers constitute the most explicit type of signals of all. We also consider discourse markers to be the most important source of information for identifying relations in our discourse parsing system. However, we should note that finding the correlation between relations and discourse markers is not a straightforward task, because the relationship between discourse relations and discourse markers is, in most of the cases, not one-to-one, but one-to-many. For instance, while a single relation can well be signalled by a number of markers (e.g., Condition is signalled by a number of markers, such as if, unless, given and since), a single marker can also indicate a number of relations (e.g. the discourse marker but is used to indicate a number of relations, such as Circumstance, Concession and Elaboration). As a result, the information based solely on the list of discourse markers indicative of particular relations proves to be insufficient for determining relations in unseen texts. As a solution to this problem, we noticed that a single discourse marker, in many cases, has different contextual distribution while signalling different relations. For instance, the marker so, while signalling a Cause relation, appears in the beginning of the satellite when the nucleus precedes the satellite. On the other hand, so appears in the beginning of the nucleus in the satellite-nucleus order while indicating a Result relation. Thus, we decided to use both the information about the list of discourse markers and the relevant contextual information about them for the purpose of identifying discourse relations by our discourse parsing system. Methodology Given the objective of extracting contextual information about discourse markers, we performed two tasks: (1) compiling a list of discourse markers indicative of particular relations, and (2) Finding the contexts in which those discourse markers appear while signalling those relations. For the first task i.e. compiling the list of discourse markers, we relied on the existing literature on discourse markers, relation marking, discourse parsing and taxonomies of relations. The references which were consulted in this regard include Schiffrin, 1987; Knott & Dale, 1994; Knott, 1996; Marcu, 1997; Carlson & Marcu, 2001; Schilder, 2002; Taboada, 2006; Prasad et al., 2007; Pardo & Nunes, All these works documented 3

4 some descriptions of a set of discourse markers with respect to the relations they signal. We considered one relation at a time and collected all the discourse markers which are suggested in these works to function as the markers for that relation. In this way, we accumulated a list of discourse markers and tabulated them in terms of specific relations that could possibly be signalled by them. For the second task i.e. extracting the contexts of the occurrence of discourse markers, we conducted a corpus study. We used two corpora, Epinions and COCA (The Corpus of Contemporary American English), as the source of our data. Epinions corpus is extracted from Epinions.com which is a general consumer review site that was established in At Epinions, visitors can read reviews about a variety of items in order to decide on a purchase or they can join for free and begin writing reviews that may earn them money and recognition. On the other hand, COCA is the largest online corpus of American English currently available, and it contains a wide array of texts from a number of genres. While Epinions corpus was chosen because of its genre which is very useful to the purpose of sentiment analysis, COCA was chosen for extracting the knowledge of the use of discourse markers from even larger domains. Table 1 shows the relation-wise distribution of the discourse markers collected for 11 relations. No. Relations Number of Discourse Markers Collected 1. Background Cause Circumstance Concession Condition Elaboration 8 7. Evaluation Purpose 3 9. Restatement Result Summary 5 Total 288 Table 1: The Relation-wise Distribution of Discourse Markers Collected The corpus study consisted of three steps: (i) searching for the instances of the discourse markers (compiled from the first task) from two corpora, (ii) examining those instances with respect to the relations signalled by those discourse markers, and (iii) extracting the contexts in which they appear while signalling the respective discourse relations. In the first step, a word/phrase representing the discourse marker in question was searched in a document using the string search methods. For Epinions texts, the documents were in text files from which the discourse markers were searched by using the search option find under edit in the text documents. For COCA, the markers were searched by using a concordance tool embedded into the COCA website. In both cases, we examined whether 4

5 a search result truly qualified for being considered as a discourse marker, or it was simply a word/phrase which was homophonous with a discourse marker and did not have any discourse signalling function (e.g. the word and can be used as a discourse marker when it conjoins two propositions; on the other hand, it can also be used as a conjunction in coordinated phrases in which the component phrases are not discourse propositions). In the second step, the relations which were signalled by the discourse markers were ascertained. In the third step, the contexts of the discourse markers which were minimally useful to identify the relations were identified. Accordingly, the contexts were extracted with respect to the position of the discourse markers in the text spans, and the order of the nuclei and satellites. In many cases, however, the context of a discourse marker for a specific relation could not be determined. This is because the corpora we worked on did not contain any instance of those discourse markers for some particular relations, and the search results (conducted in step 1 of the corpus study) produced zero matches for those discourse markers. Table 2 provides the relation-wise distribution of the discourse markers according to their contexts found or not found. No. Relation Number of Discourse Markers Number of Contexts Found Number of Contexts Not Found 1. Background Cause Circumstance Concession Condition Elaboration Evaluation Purpose Restatement Result Summary Total Table 2: The Distribution of Discourse Markers according to their Contexts Found and Not Found 5

6 Lists of Discourse Markers and their Contexts Conventions: No. Symbol Used Explanation 1. N Nucleus 2. S Satellite 3. S. N S precedes N. S is separated from N by a period (adjacent sentences). 4. S, N S precedes N. S is separated from N by a comma (adjacent clauses). 5. S./, N S precedes N. S is separated from N by either a period (adjacent sentences) or a comma (adjacent clauses). 6. S(,) N S precedes N. S can be (but not always be) separated from N by a comma. 7. S(./,) N S precedes N. S can be (but not always be) separated from N by either a period or a comma. 8. X or Y Temporal expression List of Discourse Markers and the Relevant Contextual Information Associated with them while Signalling Particular Relations. (Note: For the blank cells in the tables, no instances of the respective discourse markers were found.) 1. Background: 1. X earlier Beginning/end of the S when S./, N or N./, S 2. X later Beginning/end of the S when S./, N or N./, S 3. Over X Beginning/end of the S when S./, N or N./, S 4. From X to Y Beginning/end of the S when S./, N or N./, S 5. But X after 6. But X later 7. Between X and Y End of the S when S./, N or N./, S 6

7 8. In X Beginning/end of the S when S./, N or N./, S 9. But Beginning of the S when N./, S 10. Now Beginning of the N when S./, N 11. for 12. and Beginning of the N when S(,) N Beginning of the S when N(,) S 13. Previously Beginning/middle/end of the S when N./, S 14. Since X Beginning/end of the S when S./, N or N./, S 15. Thus Beginning of the N when S./, N 16. Up to now Beginning/middle/end of the S when S./, N or N./, S 17. Shortly before X Beginning/end of the S when S./, N or N./, S 18. Shortly after X Beginning/end of the S when S./, N or N./, S 2. Cause: 1. So Beginning of the S when N./, S 2. So that Beginning of the S when N./, S 3. In case 4. Because Beginning of the S when N(,) S or S, N 5. Since Beginning of the S when N(,) S or S, N 6. As Beginning of the S when N(,) S or S, N 7. After all 8. for 9. On the grounds that 10. Given that 11. Thus Beginning of the S when N./, S 12. Therefore Beginning of the S when N./, S 13. So Beginning of the S when N./, S 14. Hence Beginning of the S when N./, S 15. Consequently Beginning of the S when N./, S 16. As a consequence Beginning of the S when N./, S 17. As a result Beginning of the S when N./, S 18. It follows that Beginning of the S when N. S 7

8 19. Unless 20. Otherwise 21. If not 22. Or 23. Or else 24. Else 25. If Beginning of the S when S, N or N(,) S 26. On the assumption that 27. Supposing that Beginning of the S when S, N or N(,) S 28. If ever 29. Suppose (that) 30. Let us assume (that) 31. Providing (that) Beginning of the S when S, N or N(,) S 32. Provided (that) Beginning of the S when S, N or N(,) S 33. On condition that 34. Then Beginning of the S when N./, S 35. If so 36. In that case 37. When Beginning of the S when S, N or N(,) S 3. Circumstance: 1. When Beginning of the S when S, N or N(,) S 2. As Beginning of the S when S, N or N(,) S 3. After Beginning of the S when S, N or N(,) S 4. following Beginning of the S when S, N or N(,) S 5. Since 6. Without 7. But 8. Once Beginning of the S when S, N or N(,) S 9. Until 10. With 11. Before Beginning of the S when S, N or N(,) S 8

9 12. Now Beginning/end of the N when S. N 13. While Beginning of the S when S, N or N(,) S 14. If 15. Given 16. Because 17. Whether or 18. Either or 19. And then Beginning of the N when S(,) N 20. And when Beginning of the S when S, N 21. At first 22. Sometimes 23. Under it would be 24. Now that Beginning of the S when S, N 25. Verb-ing Beginning of the S when S, N 26. In Verb-ing Beginning of the S when S, N 27. Verb-en Beginning of the S when S, N 4. Concession: 1. Above all 2. Admitting Beginning of the S when S, N or N(,) S 3. After all 4. Against 5. Albeit Beginning of the S when S, N or N(,) S 6. Allowing that Beginning of the S when S, N or N(,) S 7. Although Beginning of the S when S, N or N(,) S 8. And even then Beginning of the N when S(./,) N 9. Anyway Beginning/end of the N when S./, N 10. Aside from Beginning of the S when S, N or N(,) S 9

10 11. At any cost 12. At least 13. But Beginning of the N when S./, N Beginning of the S when N./, S 14. But even so Beginning of the N when S./, N 15. Come what may 16. Despite Beginning of the S when S, N or N(,) S 17. Despite everything Beginning of the S when S, N or N(,) S 18. Despite the fact Beginning of the S when S, N or N(,) S 19. Distinct from 20. Even Beginning of the S when S, N or N(,) S 21. Even after Beginning of the S when S, N or N(,) S 22. Even as Beginning of the S when S, N or N(,) S 23. Even before Beginning of the S when S, N or N(,) S 24. Even if Beginning of the S when S, N or N(,) S 25. Even supposing Beginning of the S when S, N or N(,) S 26. Even though Beginning of the S when S, N or N(,) S 27. Even when Beginning of the S when S, N or N(,) S 28. Even while Beginning of the S when S, N or N(,) S 29. Even with Beginning of the S when S, N or N(,) S 30. Even yet Beginning/middle/end of the N when S. N 31. For all that 32. For one thing 33. Granted Beginning of the S when S, N or N(,) S 34. Granting Beginning of the S when S, N or N(,) S 35. Granting all these Beginning of the S when S, N or N(,) S 36. Howbeit Beginning of the N when S./, N 37. However Beginning/middle/end of the N when S./, N 38. In any case 39. In contempt of 40. In defiance of 41. In spite of Beginning of the S when S, N or N(,) S 42. In spite of all things Beginning of the S when S, N or N(,) S 10

11 43. In spite of everything Beginning of the S when S, N or N(,) S 44. In the face of 45. Much as 46. Nevertheless Beginning/middle of the N when S./, N 47. No matter what Beginning of the S when S, N or N(,) S 48. Nonetheless Beginning/middle/end of the N when S./, N 49. Not the less 50. Notwithstanding Beginning/end of the S when S./, N or N./, S 51. Of course 52. Only 53. Over all Beginning of the N when S./, N 54. Rather Beginning of the N when S./, N 55. Regardless Beginning of the S when S, N or N(,) S 56. Regardless of Beginning of the S when S, N or N(,) S 57. Still Beginning/middle of the N when S./, N 58. Supposing 59. Though too Beginning of the S when S, N or N(,) S 60. Undeterred by 61. When 62. Whereas Beginning of the S when S, N or N(,) S 63. Whether 64. While 65. Withal 66. Without considering 67. Without regard to 68. Yet Beginning/middle/end of the N when S./, N 5. Condition: 1. As Beginning of the S when S, N or N(,) S 2. As because Beginning of the S when N(,) S 3. As far as Beginning of the S when S, N or N(,) S 11

12 4. As long as Beginning of the S when S, N or N(,) S 5. Assuming that Beginning of the S when S, N or N(,) S 6. Conceding that Beginning of the S when S, N or N(,) S 7. Considering Beginning of the S when S, N or N(,) S 8. Considering that Beginning of the S when S, N or N(,) S 9. Contingent to 10. Contingent upon 11. Either 12. Especially if Beginning of the S when N(,) S 13. Especially when Beginning of the S when N(,) S 14. Even if Beginning of the S when S, N or N(,) S 15. Except Beginning of the S when S, N or N(,) S 16. Except after Beginning of the S when N(,) S 17. Except before Beginning of the S when N(,) S 18. Except if Beginning of the S when S, N or N(,) S 19. Except that Beginning of the S when S, N or N(,) S 20. Except when Beginning of the S when N(,) S 21. For one thing 22. Given Beginning of the S when S, N or N(,) S 23. Given that Beginning of the S when S, N or N(,) S 24. Granted that Beginning of the S when S, N 25. Having said that Beginning of the S when S, N 26. If Beginning of the S when S, N or N(,) S 27. If and only if Beginning of the S when S, N or N(,) S 28. If ever Beginning of the S when S, N or N(,) S 29. If not Beginning of the S when S, N or N(,) S 30. If only Beginning of the S when S, N or N(,) S 31. If so Beginning of the S when S, N 32. In case Beginning of the S when S, N or N(,) S 33. In case that Beginning of the S when S, N or N(,) S 34. In the case that 35. In the event 36. Inasmuch as Beginning of the S when S, N or N(,) S 12

13 37. Insofar as Beginning of the S when S, N or N(,) S 38. Nisi 39. Once Beginning of the S when S, N or N(,) S 40. On condition Beginning of the S when N(,) S 41. On condition that Beginning of the S when N(,) S 42. On the assumption that Beginning of the S when N(,) S 43. On the ground that Beginning of the S when N(,) S 44. On these terms 45. On the occasion that 46. Only if Beginning of the S when N(,) S 47. Only when Beginning of the S when N(,) S 48. Otherwise Beginning of the S when N./, S 49. Particularly if Beginning of the S when N(,) S 50. Particularly when Beginning of the S when N(,) S 51. Provided Beginning of the S when N(,) S 52. Provided that Beginning of the S when N(,) S 53. Providing Beginning of the S when N(,) S 54. Providing that Beginning of the S when N(,) S 55. Saving 56. Since Beginning of the S when S, N or N(,) S 57. Subject to 58. Supposing 59. Supposing that 60. The more often 61. Unless Beginning of the S when S, N or N(,) S 62. Until Beginning of the S when S, N or N(,) S 63. Upon any less condition that 64. When Beginning of the S when S, N or N(,) S 65. Whence 66. Whenever Beginning of the S when S, N or N(,) S 67. Wherever Beginning of the S when S, N or N(,) S 68. Whether 69. While Beginning of the S when S, N or N(,) S 13

14 70. With the condition that Beginning of the S when N(,) S 71. With the proviso Beginning of the S when N(,) S 72. With the understanding Beginning of the S when N(,) S 73. Yes or no 74. Under similar circumstances 75. Whether or 76. The less the more 6. Elaboration: 1. And Beginning of the S when N(./,) S 2. But Beginning of the S when N(./,) S 3. In fact Beginning of the S when N. S 4. In addition Beginning of the S when N. S 5. Also Beginning of the S when N. S 6. By verb-ing Beginning of the S when N(,) S 7. Verb-ing Beginning of the S when N(,) S 8. For example Beginning of the S when N. S 7. Evaluation: 1. Though 2. So Beginning of the S when N./, S Beginning of the N when S./, N 3. However Beginning of the S when N./, S Beginning of the N when S./, N 4. And Beginning of the S when N(,) S Beginning of the N when S. N 5. So far Beginning/middle/end of the S when N./, S Beginning/middle/end of the N when S./, N 6. But Beginning of the S when N./, S 14

15 Beginning of the N when S./, N 7. Yet Beginning of the S when N./, S Beginning of the N when S./, N 8. Finally Beginning of the S when N./, S Beginning of the N when S./, N 9. Without 10. Until 11. Nonetheless Beginning of the S when N./, S Beginning of the N when S./, N 12. Unlike 13. Such 14. Already 15. Altogether 16. Which 17. Thus Beginning of the S when N./, S Beginning of the N when S./, N 18. Still 19. Accordingly 8. Purpose: 1. And 2. So that Beginning of the S when N(,) S 3. That 9. Restatement: 1. So Beginning of the S when N./, S 2. Or Beginning of the S when N(./,) S 3. According to 15

16 4. That 5. If 6. But 7. Because 8. For instance Beginning of the S when N. S 9. As of 10. Unlike 10. Result: 1. Because of Beginning of the S when S, N or N(,) S 2. As a result of Beginning of the N when S. N 3. Because Beginning of the S when S, N or N(,) S 4. And Beginning of the N when S(,) N 5. So Beginning of the N when S./, N 6. As a result Beginning of the N when S. N 7. When Beginning of the S when S, N or N(,) S 8. As Beginning of the S when S, N or N(,) S 9. Since Beginning of the S when S, N or N(,) S 10. Now Beginning of the N when S. N 11. After Beginning of the S when S, N or N(,) S 12 The result 13. So far Beginning/middle/end of the N when S. N 14. Now that Beginning of the S when S, N 15. And so Beginning of the N when S N 16. Thus Beginning of the N when S. N 17. But 11. Summary: 1. All this 2. In any case Beginning of the S when N. S Beginning of the N when S. N 16

17 3. But 4. In what 5. The Following References Carlson, L., & Marcu, D. (2001). Discourse Tagging Manual.Unpublished manuscript. Grosz, Barbara J., & Sidner, Candace L. (1986). Attention, intentions, and the structure of discourse. Computaional Linguistics, 12(3), Knott, Alistair. (1996). A data-driven methodology for motivating a set of coherence relations. Unpublished Ph.D. dissertation, University of Edinburgh, Edinburgh, UK. Knott, Alistair, & Dale, Robert. (1994). Using linguistic phenomena to motivate a set of coherence relations. Discourse Processes, 18(1), Le Thanh, Huong. (2007). An approach in automatically generating discourse structure of text. Journal of Computer Science and Cybernetics, 23(3), Mann, William C., & Thompson, Sandra A. (1988). Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8(3), Marcu, Daniel. (1997). The Rhetorical Parsing, Summarization, and Generation of Natural Language Texts. Unpublished PhD dissertation, University of Toronto, Toronto, Canada. Pardo, Thiago Alexandre Salgueiro, & Nunes, Maria das Gracas Volpe. (2008). On the Development and evaluation of a Brazilian Portuguese discourse parser. Journal of Theoretical and Applied Computing, 15(2), Prasad, Rashmi, Miltsakaki, E., Dinesh, N., Lee, A., Joshi, A., Robaldo, L., & Webber, B. (2007). The Penn Discourse Treebank 2.0 Annotation Manual.Unpublished manuscript. Schiffrin, Deborah. (1987). Discourse Markers. Cambridge: Cambridge University Press. Schilder, Frank. (2002). Robust discourse parsing via discourse markers, topicality and position. Natural Language Engineering, 8(2/3), Taboada, Maite. (2006). Discourse markers as signals (or not) of rhetorical relations. Journal of Pragmatics, 38(4), Taboada, Maite, & Gonzales, Ashleigh Rhea. (2010). Taxonomies of rhetorical relations (Technical Report): Simon Fraser University. Tofiloski, Milan, Julian, Brooke, & Taboada, Maite. (2009). A Syntactic and Lexical- Based Discourse Segmenter. Paper presented at the 47th Annual Meeting of the Association for Computational Linguistics, Singapore. 17

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