Page 144 - 2019 - Leaders in Legal Business (n)
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of a type of lawsuit, any of those machine-learning algorithms could predict the likely cost of a
new matter once sufficient information is available and tell you how probable that cost would be.
As another example of machine learning, a regression model might explain and forecast
how fees and hours devoted to five common litigation tasks are associated with outcomes and
therefore can predict the likely outcomes for the next case that can identify the corresponding data.
Moreover, the machine-learning software can tell you which of the five tasks underpin the
strongest association with the outcomes as well as how confident you can be that your prediction
is correct.
Extracting insights from text.
Words in documents can be handled statistically by software as text mining. When a survey
returns free-text comments, for example, software can pick out not only which terms are used most
frequently, but also assess the sentiment (the positive or negative vibes of the comments). Even
more powerful are the algorithms that can assemble words from the survey comments into topics.
A person has to examine the words and identify the actual topic, but the laborious work of parsing
all the documents and doing the math can be done quickly by the computer. If you want to show
off, mention latent Dirichlet allocation (LDA) as your topic-modeling algorithm of choice!
A second form of machine learning would be at work when text mining software takes
thousands of emails, identifies patterns in words such as repetition or proximity to each other, or
pores through email messages to tag possible indicators of insider trading.
Classification and clustering.
Whenever a law firm or law department has collected a set of data, it can use a range of
software tools to cluster the observations. This means that the software brings together related
clients, matters, or law firms based on the information available to it about them. Once the software
has clustered the observations, managers can more easily detect patterns and understand
similarities and differences. A chart known as a dendrogram can depict the clustering of data and
how clusters relate to each other. Somewhat similarly, software can classify observations into
similar groups. Both of these types of analytics help partners see patterns that they could not
otherwise detect from a massive set of data.
Other models can also classify new observations into the most appropriately fitting group.
With several types of algorithms, including K-Nearest Neighbor or Support Vector Machines, you
can classify clients or other data. You would be able, for example, to identify publicly-traded
clients or clients likely to reach a certain realization level.
Other varieties of machine-learning software do not require labels. Their models cluster
the data into groupings that will reveal something. For example, they might cluster a firm’s clients
by profitability. The K-Means algorithm can do this, and with the Principal Components Analysis
you can aggregate “variables” to find out which of them is more influential.
Machine learning.
At this time, the most sophisticated data analytics that can help partners resides in a branch
of artificial intelligence known as machine learning. The term encompasses a range of methods
by which software chews its way through mounds of data and detects patterns. In one broad
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new matter once sufficient information is available and tell you how probable that cost would be.
As another example of machine learning, a regression model might explain and forecast
how fees and hours devoted to five common litigation tasks are associated with outcomes and
therefore can predict the likely outcomes for the next case that can identify the corresponding data.
Moreover, the machine-learning software can tell you which of the five tasks underpin the
strongest association with the outcomes as well as how confident you can be that your prediction
is correct.
Extracting insights from text.
Words in documents can be handled statistically by software as text mining. When a survey
returns free-text comments, for example, software can pick out not only which terms are used most
frequently, but also assess the sentiment (the positive or negative vibes of the comments). Even
more powerful are the algorithms that can assemble words from the survey comments into topics.
A person has to examine the words and identify the actual topic, but the laborious work of parsing
all the documents and doing the math can be done quickly by the computer. If you want to show
off, mention latent Dirichlet allocation (LDA) as your topic-modeling algorithm of choice!
A second form of machine learning would be at work when text mining software takes
thousands of emails, identifies patterns in words such as repetition or proximity to each other, or
pores through email messages to tag possible indicators of insider trading.
Classification and clustering.
Whenever a law firm or law department has collected a set of data, it can use a range of
software tools to cluster the observations. This means that the software brings together related
clients, matters, or law firms based on the information available to it about them. Once the software
has clustered the observations, managers can more easily detect patterns and understand
similarities and differences. A chart known as a dendrogram can depict the clustering of data and
how clusters relate to each other. Somewhat similarly, software can classify observations into
similar groups. Both of these types of analytics help partners see patterns that they could not
otherwise detect from a massive set of data.
Other models can also classify new observations into the most appropriately fitting group.
With several types of algorithms, including K-Nearest Neighbor or Support Vector Machines, you
can classify clients or other data. You would be able, for example, to identify publicly-traded
clients or clients likely to reach a certain realization level.
Other varieties of machine-learning software do not require labels. Their models cluster
the data into groupings that will reveal something. For example, they might cluster a firm’s clients
by profitability. The K-Means algorithm can do this, and with the Principal Components Analysis
you can aggregate “variables” to find out which of them is more influential.
Machine learning.
At this time, the most sophisticated data analytics that can help partners resides in a branch
of artificial intelligence known as machine learning. The term encompasses a range of methods
by which software chews its way through mounds of data and detects patterns. In one broad
129
