AI For Lawyers Waisberg, Noah; Hudek, Alexander
2021/02/08, 2021, 2021-01-12
eBook
Discover how artificial intelligence can improve how your organization practices law with this compelling resource from the creators of one of the world's leading legal AI platforms. AI for Lawyers: ...How Artificial Intelligence is Transforming the Legal Profession explains how artificial intelligence can be used to revolutionize your organization's operations. Noah Waisberg and Dr. Alexander Hudek, a lawyer and a computer science Ph.D. who lead prominent legal AI business Kira Systems, have written an approachable and insightful book that will help you transform how your firm functions. AI for Lawyers explains how artificial intelligence can help your law firm: Win more business and find more clients Better meet and exceed client expectations Find hidden efficiencies Better manage and eliminate risk Increase associate and partner engagement Whether focusing on small or big law, AI for Lawyers is perfect for any lawyer who either feels uneasy about how AI might change law or is looking to capitalize on the evolving practice. With contributions from experts in the fields of e-Discovery, legal research, expert systems, and litigation analytics, it also belongs on the bookshelf of anyone who's interested in the intersection of law and technology.
Multiple genome alignment is an important problem in bioinformatics. An important subproblem used by many multiple alignment approaches is that of aligning two multiple alignments. Many popular ...alignment algorithms for DNA use the sum-of-pairs heuristic, where the score of a multiple alignment is the sum of its induced pairwise alignment scores. However, the biological meaning of the sum-of-pairs of pairs heuristic is not obvious. Additionally, many algorithms based on the sum-of-pairs heuristic are complicated and slow, compared to pairwise alignment algorithms. An alternative approach to aligning alignments is to first infer ancestral sequences for each alignment, and then align the two ancestral sequences. In addition to being fast, this method has a clear biological basis that takes into account the evolution implied by an underlying phylogenetic tree. In this study we explore the accuracy of aligning alignments by ancestral sequence alignment. We examine the use of both maximum likelihood and parsimony to infer ancestral sequences. Additionally, we investigate the effect on accuracy of allowing ambiguity in our ancestral sequences.
We use synthetic sequence data that we generate by simulating evolution on a phylogenetic tree. We use two different types of phylogenetic trees: trees with a period of rapid growth followed by a period of slow growth, and trees with a period of slow growth followed by a period of rapid growth. We examine the alignment accuracy of four ancestral sequence reconstruction and alignment methods: parsimony, maximum likelihood, ambiguous parsimony, and ambiguous maximum likelihood. Additionally, we compare against the alignment accuracy of two sum-of-pairs algorithms: ClustalW and the heuristic of Ma, Zhang, and Wang.
We find that allowing ambiguity in ancestral sequences does not lead to better multiple alignments. Regardless of whether we use parsimony or maximum likelihood, the success of aligning ancestral sequences containing ambiguity is very sensitive to the choice of gap open cost. Surprisingly, we find that using maximum likelihood to infer ancestral sequences results in less accurate alignments than when using parsimony to infer ancestral sequences. Finally, we find that the sum-of-pairs methods produce better alignments than all of the ancestral alignment methods.
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We present a pairwise local aligner, FEAST, which uses two new techniques: a sensitive extension algorithm for identifying homologous subsequences, and a descriptive probabilistic alignment model. We ...also present a new procedure for training alignment parameters and apply it to the human and mouse genomes, producing a better parameter set for these sequences. Our extension algorithm identifies homologous subsequences by considering all evolutionary histories. It has higher maximum sensitivity than Viterbi extensions, and better balances specificity. We model alignments with several submodels, each with unique statistical properties, describing strongly similar and weakly similar regions of homologous DNA. Training parameters using two submodels produces superior alignments, even when we align with only the parameters from the weaker submodel. Our extension algorithm combined with our new parameter set achieves sensitivity 0.59 on synthetic tests. In contrast, LASTZ with default settings achieves sensitivity 0.35 with the same false positive rate. Using the weak submodel as parameters for LASTZ increases its sensitivity to 0.59 with high error. FEAST is available at http://monod.uwaterloo.ca/feast/.
Towards Protecting Sensitive Text with Differential Privacy Fletcher, Sam; Roegiest, Adam; Hudek, Alexander K.
2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom),
2021-Oct.
Conference Proceeding
Natural language processing can often require handling privacy-sensitive text. To avoid revealing confidential information, data owners and practitioners can use differential privacy, which provides ...a mathematically guaranteeable definition of privacy preservation. In this work, we explore the possibility of applying differential privacy to feature hashing. Feature hashing is a common technique for handling out-of-dictionary vocabulary, and for creating a lookup table to find feature weights in constant time. Traditionally, differential privacy involves adding noise to hide the true value of data points. We show that due to the finite nature of the output space when using feature hashing, a noiseless approach is also theoretically sound. This approach opens up the possibility of applying strong differential privacy protections to NLP models trained with feature hashing. Preliminary experiments show that even common words can be protected with (0.04,10 −5 )-differential privacy, with only a minor reduction in model utility.
Genescript uses a number of publicly available analysis programs to annotate a DNA sequence. It provides an integrated display of results from each program, and includes an evidence-based scoring ...system that gives informative summaries of predicted gene models Availability: Genescript is available for download from http:://tcag.bioinfo.sickkids.on.ca/genescript/ Contact: alex@genet.sickkids.on.ca * To whom correspondence should be addressed.
Absorption for ABoxes Wu, Jiewen; Hudek, Alexander; Toman, David ...
Journal of automated reasoning,
10/2014, Volume:
53, Issue:
3
Journal Article
Peer reviewed
We consider the
instance checking
problem over
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Q
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knowledge bases, that is, the problem of determining if the class membership of a
given
object is logically implied by a knowledge base ...expressed in terms of the
description logic
(DL) dialect
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I
Q
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. Such problems are inevitable in conjunctive query evaluation over such knowledge bases, or indeed for any knowledge bases that rely on an ability to capture disjunction and/or negation in an underlying DL. This includes the problem of evaluating
basic graph patterns
occurring in SPARQL queries over RDF graphs with the so-called OWL 2
direct semantics
entailment regime, that is, where the RDF graph is an OWL 2 ontology. Our main result is a novel method for such problems that derives from an adaptation of binary absorption. We show that the method works particularly well for knowledge bases that have a very large collection of factual assertions about individual objects.
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The problem of answering multiple choice questions, based on the content of documents has been studied extensively in the machine learning literature. We pose the due diligence problem, where lawyers ...study legal contracts and assess the risk in potential mergers and acquisitions, as a multiple choice question answering problem, based on the text of the contract. Existing frameworks for question answering are not suitable for this task, due to the inherent scarcity and imbalance in the legal contract data available for training. We propose a question answering system which first identifies the excerpt in the contract which potentially contains the answer to a given question, and then builds a multi-class classifier to choose the answer to the question, based on the content of this excerpt. Unlike existing question answering systems, the proposed system explicitly handles the imbalance in the data, by generating synthetic instances of the minority answer categories, using the Synthetic Minority Oversampling Technique. This ensures that the number of instances in all the classes are roughly equal to each other, thus leading to more accurate and reliable classification. We demonstrate that the proposed question answering system outperforms the existing systems with minimal amount of training data.
We present and formalize the due diligence problem, where lawyers extract data from legal documents to assess risk in a potential merger or acquisition, as an information retrieval task. Furthermore, ...we describe the creation and annotation of a document collection for the due diligence problem that will foster research in this area. This dataset comprises 50 topics over 4,412 documents and ~15 million sentences and is a subset of our own internal training data. Using this dataset, we present what we have found to be the state of the art for information extraction in the due diligence problem. In particular, we find that when treating documents as sequences of labelled and unlabelled sentences, Conditional Random Fields significantly and substantially outperform other techniques for sequence-based (Hidden Markov Models) and non-sequence based machine learning (logistic regression). Included in this is an analysis of what we perceive to be the major failure cases when extraction is performed based upon sentence labels.
Interpreting keyword queries over web knowledge bases Pound, Jeffrey; Hudek, Alexander K.; Ilyas, Ihab F. ...
Proceedings of the 21st ACM international conference on Information and knowledge management,
10/2012
Conference Proceeding
Open access
Many keyword queries issued to Web search engines target information about real world entities, and interpreting these queries over Web knowledge bases can often enable the search system to provide ...exact answers to queries. Equally important is the problem of detecting when the reference knowledge base is not capable of answering the keyword query, due to lack of domain coverage.
In this work we present an approach to computing structured representations of keyword queries over a reference knowledge base. We mine frequent query structures from a Web query log and map these structures into a reference knowledge base. Our approach exploits coarse linguistic structure in keyword queries, and combines it with rich structured query representations of information needs.
Pairwise sequence alignment is a fundamental problem in bioinformatics with wide applicability. This thesis presents three new algorithms for this well-studied problem. First, we present a new ...algorithm, RDA, which aligns sequences in small segments, rather than by individual bases. Then, we present two algorithms for aligning long genomic sequences: CAPE, a pairwise global aligner, and FEAST, a pairwise local aligner.
RDA produces interesting alignments that can be substantially different in structure than traditional alignments. It is also better than traditional alignment at the task of homology detection. However, its main negative is a very slow run time. Further, although it produces alignments with different structure, it is not clear if the differences have a practical value in genomic research.
Our main success comes from our local aligner, FEAST. We describe two main improvements: a new more descriptive model of evolution, and a new local extension algorithm that considers all possible evolutionary histories rather than only the most likely. Our new model of evolution provides for improved alignment accuracy, and substantially improved parameter training. In particular, we produce a new parameter set for aligning human and mouse sequences that properly describes regions of weak similarity and regions of strong similarity. The second result is our new extension algorithm. Depending on heuristic settings, our new algorithm can provide for more sensitivity than existing extension algorithms, more specificity, or a combination of the two.
By comparing to CAPE, our global aligner, we find that the sensitivity increase provided by our local extension algorithm is so substantial that it outperforms CAPE on sequence with 0.9 or more expected substitutions per site. CAPE itself gives improved sensitivity for sequence with 0.7 or more expected substitutions per site, but at a great run time cost. FEAST and our local extension algorithm improves on this too, the run time is only slightly slower than existing local alignment algorithms and asymptotically the same.
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