EAST stands for the Enhanced Annotated Suffix Tree method for text analysis.
To install EAST, run:
$ pip install EAST
This may require admin permissions on your machine (and should then be run with sudo).
EAST comes both as a CLI application and as a python library (which can be imported and used in python code).
The basic use case for the AST method is to calculate matching scores for a set of keyphrases against a set of text files (the so-called keyphrase table). To do that with east, launch it as follows:
$ east [-f <table_format>] [-l <language>] [-s] [-d] [-a <ast_algorithm>] [-w <term_weighting>] [-v <vector_space>] [-y] keyphrases table <keyphrases_file> <directory_with_txt_files>
- The -s option determines the similarity measure to be used while computing the matching score. Its value is "ast" by default (as this package has been developed primarily as an implementation of the Annotated Suffix Tree method), but it can be also set to "cosine": the cosine similary will be used then to compute the relevance of keyphrases to documents (the text in the collection will be represented as vectors then).
- Depending on which relevance measure is used while computing the table, there are some auxiliary options to further specify the computation:
- For the AST relevance measure:
- The -a option defines the actual AST method implementation to be used. Possible arguments are "easa" (Enhanced Annotated Suffix Arrays), "ast_linear" (Linear-time and -memory implementation of Annotated Suffix Trees) and "ast_naive" (a slow and memory-consumptive implementation, present just for comparison).
- The -d option and specifies whether the the matching score should be computed in the denormalized form (normalized by default, see [Mirkin, Chernyak & Chugunova, 2012].
- For the Cosine relevance measure:
- The -v option specifies what elements should form the vector space, i.e. be the actual terms (these can be "stems", "lemmata" or just "words". In the first two cases, the words in the text collection get transformed into stems/lemmata automatically).
- The -w option determines which term weighting scheme should be used ("tf-idf" or just "tf").
- The -y option and determines whether the matching score should be computed taking into account the synonyms extracted from the text file.
- The -l option tells EAST about the language in which the texts in the collection and the keyphrases are written. In general, EAST does not need this information to compute the AST similarity scores. However, it is used to compute the cosine similarity scores (in case the user prefers this relevance measure type). English is the default language; all possible values of this parameter are: "danish" / "dutch" / "english" / "finnish" / "french" / "german" / "hungarian" / "italian" / "norwegian" / "porter" / "portuguese" / "romanian" / "russian" / "spanish" / "swedish".
- The -f option specifies the format in which the table should be printed. The format is XML by default (see an example below); the -f option can also take CSV as its parameter.
- Please note that you can also specify the path to a single text file instead of that for a directory. In case of the path to a directory, only .txt files will be processed.
If you want to print the output to some file, just redirect the EAST output (e.g. by appending > filename.txt to the command in Unix).
Sample output in the XML format:
<table>
<keyphrase value="KEYPHRASE_1">
<text name="TEXT_1">0.250</text>
<text name="TEXT_2">0.234</text>
</keyphrase>
<keyphrase value="KEYPHRASE_2">
<text name="TEXT_1">0.121</text>
<text name="TEXT_2">0.000</text>
</keyphrase>
<keyphrase value="KEYPHRASE_3">
<text name="TEXT_1">0.539</text>
<text name="TEXT_3">0.102</text>
</keyphrase>
</table>
The east software also allows to construct a keyphrases relation graph, which indicates implications between different keyphrases according to the text corpus being analysed. The graph construction algorithm is based on the analysis of co-occurrences of keyphrases in the text corpus. A keyphrase is considered to imply another one if that second phrase occurs frequently enough in the same texts as the first one (that frequency is controlled by the referral confidence parameter). A keyphrase counts as occuring in a text if its presence score for that text ecxeeds some threshold [Mirkin, Chernyak, & Chugunova, 2012].
$ east [-f <graph_format>] [-c <referral_confidence>] [-r <relevance_threshold>] [-p <support_threshold>] [-s] [-d] [-a <ast_algorithm>] [-w <term_weighting>] [-v <vector_space>] [-y] keyphrases graph <keyphrases_file> <directory_with_txt_files>
- The -p option configures the threshold for graph node support (the number of documents "containing" the corresponding keyphrase according to the AST method), starting with which the nodes get included into the graph.
- The -c option controls the referral confidence level above which the implications between keyphrases are considered to be strong enough to be added as graph arcs. The confidence level should be a float in [0; 1] and is 0.6 by default.
- The -r option controls the relevance threshold of the matching score - the minimum matching score value where keyphrases start to be counted as occuring in the corresponding texts. It should be a float in [0; 1] and is 0.25 by default.
- The -f option determines in which format the resulting graph should come to the output. Possible values are:
- "gml" (Graph Modelling Language, which can be used for graph visualization in tools like Gephi);
- "edges", which is just a list of edges in form Keyphrase -> <List of keyphrases it points to> (simple but convenient for a quick analysis of implications between keyphrases).
- The -s option, as well as its auxiliary options (-d, -a, -v, -w and -y) configure the relevance scores computation (exactly as for the keyphrases table command). Note that the relevance measure ("ast" / "cosine") used while computing the graph usually largely influences its shape.
Sample output in the edges format:
KEYPHRASE_1 -> KEYPHRASE_3 KEYPHRASE_2 -> KEYPHRASE_3, KEYPHRASE_4 KEYPHRASE_4 -> KEYPHRASE_1
The same graph in gml:
graph
[
node
[
id 0
label "KEYPHRASE_1"
]
node
[
id 1
label "KEYPHRASE_2"
]
node
[
id 2
label "KEYPHRASE_3"
]
node
[
id 3
label "KEYPHRASE_4"
]
edge
[
source 0
target 2
]
edge
[
source 1
target 2
]
edge
[
source 1
target 3
]
edge
[
source 3
target 0
]
]
The example below shows how to use the EAST package in code. Here, we build an Annotated suffix tree for a collection of two strings ("XABXAC" and "HI") and then calculate matching scores for two queries ("ABCI" and "NOPE"):
from east.asts import base
ast = base.AST.get_ast(["XABXAC", "HI"])
print ast.score("ABCI") # 0.1875
print ast.score("NOPE") # 0
The get_ast() method takes the list of input strings and constructs an annotated suffix tree using suffix arrays by default as the underlying data structure (this is the most efficient implementation known). The algorithm used for AST construction can be optionally specified via the second parameter to get_ast() (along with "easa", its possible values include "ast_linear" and "ast_naive")
Working with real texts already requires some preprocessing, such as splitting a single input text into a collection of small-sized strings, which later enables matching scores for queries to be more precise. There is a special method text_to_strings_collection() in EAST which does that for you. The following example processes a real text collection and calculates matching scores for an input query:
from east.asts import base
from east import utils
# Prepare your text collection (e.g. from a set of *.txt files)
text_collection = [...]
# Transform the list of texts into a list of shorter substrings
# (this will improve the precision of relevance scores)
strings_collection = text_collection_to_string_collection(text_collection)
# Construct an AST for these strings
ast = base.AST.get_ast(strings_collection)
# Compute the relevance of a keyphrase to the text collection indexed by this AST.
# The relevance score will always be in [0; 1]
print ast.score("Hello, world")