Business Process Services. White Paper. Text Analytics: Enabling Agility and Accuracy in Legal Research

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1 Business Process Services White Paper Text Analytics: Enabling Agility and Accuracy in Legal Research

2 About the Authors Raghav Mathur Raghav Mathur has over eight years of experience in predictive analytics and machine learning. He is a part of the R&D team in Analytics and Insights (A&I) at Tata Consultancy Services (TCS). He has worked extensively on credit risk modeling, marketing analytics, customer lifetime value, recommendation engines, and so on. He has a Master's in Economics from Indira Gandhi Institute of Development Research (IGIDR), Mumbai. Chandan Kumar Chandan Kumar is a data scientist with experience in data analytics in mobile and telecom marketing. He is well versed in segmentation, predictive modeling, and machine learning and codes in SAS, SQL, R, and Python. Kumar has an M.Tech in Engineering from Indian Institute of Technology, Kharagpur.

3 Abstract Today's data driven environment, marked by growing volumes of structured and unstructured data, presents tremendous challenges to legal research. Legal practitioners find it tedious and time consuming to browse through voluminous book indexes manually or search for information online. Many leverage commercial software programs which provide electronic indices that aid in faster search using standard search options. However, attorneys are increasingly seeking more robust technologies that will speed up the process of retrieving relevant legal documents and references from past records to enable effective legal decision making. This paper describes the advantages of leveraging the text analytics approach to effectively and accurately find legal documents that are similar in nature. This technology provides the ability to identify contextual relationships between documents, which may be omitted during traditional searches. The paper also illustrates a versatile analytical model, and provides insights on how the model can be applied in similar scenarios outside legal research. Finally, it discusses the challenges in text analysis and suggests efficient ways to overcome them.

4 Contents Significance of Text Analytics in Legal Research 5 Leveraging a Text Analytics Model to Simplify Legal Research 5 Cleaning the data 5 Determining the importance of words in a document 5 Tabulating document similarity and ranking 6 Challenges in Text Analytics 7 Advantages of Leveraging the Text Analytics Model 8 Better outcomes in Legal Research 9

5 Significance of Text Analytics in Legal Research Legal research is a critical component of the legal profession. Often, attorneys' rate of success is contingent upon the accuracy and depth of their research. However, over time, research has become complex with the substantial growth in the legal case corpus. It is difficult for attorneys and their teams to sift through large volumes of legal documents to find the most relevant references that support and validate their points of view. Many software programs enable electronic searches of legal corpuses, in which search results are delivered based on the key word or phrase entered. The software programs also index historical documents using tags and headers. One of the disadvantages of these software programs is that the users are required to manually feed the most relevant key words or phrases before every search. Sometimes, certain relationships between legal cases are not reflected in the most obvious key words. Text analytics is effective in identifying similar documents quickly and efficiently. It eliminates the need for feeding key words or phrases into the search engine. The text analytics tool efficiently generates a list of similar legal documents based on the legal brief. Such a search process is faster, and gives the attorneys time to focus on their core tasks. Leveraging a Text Analytics Model to Simplify Legal Research We recommend an effective text analytics model, designed using publicly available datasets, for generating the document similarity score for all the documents in the available corpus. The model comprises the following key steps: Cleaning the data The first step in data processing is to clean the text by removing the 'stop words' from the full text documents. Stop words include words such as 'a', 'an', 'the', 'and', which by themselves do not convey any special meaning, and are meant to support the grammar. Removal of stop words reduces the number of variables needed for prediction, and thereby increases the efficiency of the model by reducing the algorithm running time. Determining the importance of words in a document After the data is cleaned, the text corpus is used to generate the term frequency inverse document frequency (TF-IDF) weightage matrix based on each document in the corpus. The TF-IDF weights provide a measure of the importance of a word in a document within a corpus of documents. TF-IDF (t,d) = TF (t,d) X IDF (t) Weight of a term (t) in a Document (d) Frequency of a term (t) in a Document (d) Factor IDF of a term (t) 5

6 For the document similarity mode, the full TF-IDF matrix pertaining to a typical corpus of data from a few legal cases would have very large rows and columns, to the tune of a few thousands, in size. Creation of the TF-IDF matrix is the most demanding step in the computation of the model. This is because the algorithm has to iterate over all the documents and update the calculations in each step. But once the matrix is calculated, it can be stored for future use, and does not require re-computation, unless the corpus gets updated with a new set of documents. Tabulating document similarity and ranking Once the TF-IDF matrix is computed, the user supplies the model with a document that needs to be scored. The model searches the TF-IDF matrix and calculates the document similarity distance using the TF-IDF grid. It then uses the cosine distance values to rank the corpus documents from the most similar to the least similar. Cosine similarity is a popular distance measure in text analytics. Since every document can be represented as a vector of TF-IDF values, it is possible to calculate the cosine similarity between every pair of documents in the corpus. A cosine similarity score of 100 percent indicates that the two documents are very similar, and zero percent indicates that the two documents are not similar to each other. Based on this score, the documents are ranked by the order of relevance from high to low. Table 1 depicts the typical output from this model. Document Name Document Similarity Score Doc 1 95% Doc 2 93% Doc 3 89% Doc 4 70% Doc N 7% Table 1: Similarity scores for a document Let's consider an example to illustrate how the similarity score actually performs in real life. The concerned corpus was scored by the model to get documents similar to a primary document called 'Case_A.txt'. Document 'Case_B.txt' with a similarity score of 72 percent was the first result returned by the model, which means that the two documents are similar to each other by a strength factor of 72 out of 100 points. Similarly, document 'Case_C.txt' was found to be similar to document 'Case_A.txt' by a strength factor of 7 points out of

7 Clearly, the first document is much more similar to the primary document as compared to the second document. Let's dig deeper. Table 2 shows the top five words for each of the three documents with the highest TF-IDF values. TF-IDF, as mentioned earlier, is the weight given to each word in a document. The higher the number of heavily weighted words that are common to the two documents, the higher their similarity. A word with a very high TF-IDF weight that is common to the two documents results in a much higher similarity score than multiple occurrences of common words with smaller weights. Case_A.txt Case_B.txt Case_C.txt Top Words TF-IDF Top Words TF-IDF Top Words TF-IDF Circle 4.17 Allphones 4.14 Mobile 1.58 Franchise 1.39 Plan B 0.90 Allphones 2.67 Company B 1.04 Circle 0.89 Admission 0.78 Mobile 0.77 Prepaid 3.36 Company A 2.66 Broadband 1.95 Plan A 1.79 Disadvantage 0.79 Table 2: Top five words by TF-IDF values for the three documents in the example It can be seen that documents Case_A.txt and Case_B.txt have many top TF-IDF words in common with each other as compared to Case_A.txt and Case_C.txt. The common words that have high TF-IDF values in the tables for Case_A.txt and Case_B.txt are absent from the top TF-IDF words of document Case_C.txt, indicating that document Case_B.txt is a better match to document Case_A.txt as compared to document Case_C.txt. Challenges in Text Analytics Although the text analytics method improves data processing, it also poses some challenges. One such challenge is that co-occurring words convey much more information than individual words. For example, a trigram (a contiguous sequence of three words) such as 'ABC vs. XYZ' conveys much more information than unigrams (single words) such as 'ABC', 'XYZ', and 'vs'. To identify such powerful co-occurring words, the TF-IDF matrix is enhanced by including not just unigrams but also bigrams and trigrams. A TF-IDF built using trigrams and bigrams becomes a much larger matrix to learn by the computing machines, which increases the computational need. But the search and accuracy of results of this approach are superior to those of the traditional unigram approach. Another limitation is the low signal to noise ratio in text analytics. This pertains to the problem that few words might convey the larger meaning in the given text while the signal from other words might be too 7

8 weak to be captured by the vast TF-IDF based word processing. This can be overcome over time by adding a second layer of a feedback loop to the above legal research framework. The feedback loop can learn word associations under manual supervision to boost the signal from some word combinations. These associations can then be stored as a lookup table in the form of a dynamic search index and used to boost the text search capability. 1 Key Word- Document Dictionary Dict of Doc - Keywords PDF to TXT processor Key words extraction Document Corpus Dynamic Search Index Query Framework 2 Query Query Processor Get Docs from Search Index Display Results Figure 1: Text analytics based document research framework Figure 1 is a schematic representation of the TF-IDF matrix-based search mechanism for legal research. It also illustrates how the low signal to noise ratio issue is overcome in text analytics. Advantages of Leveraging the Text Analytics Model The uniqueness of this model is that it is robust enough to identify documents with the same context in a legal corpus of 4,000 documents, even with weak TF-IDF scores. In the case of strong scores, we find that the two documents are extremely relevant to each other. The ranked scores thus help the legal teams to prioritize their efforts and first evaluate the most similar documents, thereby increasing the efficiency of the legal research process. Besides legal research, this method can be used in any scenario where the objective is to find documents similar to a given document. This model can also be deployed on Big Data platforms such as Hadoop to parse an extremely large database of documents. It can also be superimposed with an intelligent query framework to boost the quality of search results. This query framework along with a text analytics backend for the corpus search engine can be used for all intelligent document search and retrieval needs of large organizations. Businesses can thus derive added value by leveraging the replicability and versatility of the recommended analytical model. 8

9 Better outcomes in Legal Research Inadequate or time consuming legal research can result in far reaching implications for attorneys and other legal practitioners. It can impact their credibility and even lead to fines or penalties or lawsuits by clients. While the adoption of 'knowledge discovery technologies' such as text analytics is still at a nascent stage, it is increasingly being considered by legal users for the business benefits it delivers. It helps to efficiently sift through the document clutter quickly, lower costs by reducing the need for manual intervention, and ensure compliance. Text analytics is also being harnessed in other business domains to drive deeper business insights, enhance customer engagement and marketing campaigns, determine strategic business direction, and so on. With the growing demand for text analytics, businesses that take the lead and proactively leverage it are likely to stay ahead of the curve. 9

10 About TCS Business Process Services Unit Enterprises seek to drive business growth and agility through innovation in an increasingly regulated, competitive, and global market. TCS helps clients achieve these goals by managing and executing their business operations effectively and efficiently. TCS' Business Process Services (BPS) include core industry-specific processes, analytics and insights, and enterprise services such as finance and accounting, HR, and supply chain management. TCS TM creates value through its FORE simplification and transformation methodology, backed by its deep TM domain expertise, extensive technology experience, and TRAPEZE suite of solution accelerators and governance enablers. TCS complements its experience and expertise with innovative delivery models such as using robotic automation and providing Business Processes as a Service (BPaaS). TCS' BPS unit has been positioned in the leaders' quadrant for various service lines by many leading analyst firms. With over four decades of global experience and a delivery footprint spanning six continents, TCS is one of the largest BPS providers today. Contact For more information about TCS' Business Process Services Unit, visit: ( HYPERLINK "mailto:bps.connect@tcs.com" bps.connect@tcs.com Subscribe to TCS White Papers TCS.com RSS: Feedburner: About Tata Consultancy Services (TCS) Tata Consultancy Services is an IT services, consulting and business solutions organization that delivers real results to global business, ensuring a level of certainty no other firm can match. TCS offers a consulting-led, integrated portfolio of IT and IT-enabled infrastructure, engineering and TM assurance services. This is delivered through its unique Global Network Delivery Model, recognized as the benchmark of excellence in software development. A part of the Tata Group, India s largest industrial conglomerate, TCS has a global footprint and is listed on the National Stock Exchange and Bombay Stock Exchange in India. For more information, visit us at IT Services Business Solutions Consulting All content / information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content / information contained here is correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in any form without prior written permission from TCS. Unauthorized use of the content / information appearing here may violate copyright, trademark and other applicable laws, and could result in criminal or civil penalties. Copyright 2015 Tata Consultancy Services Limited TCS BPS Design Services I 04 I 15