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SearchArgs.java
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SearchArgs.java
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/**
* Anserini: A Lucene toolkit for replicable information retrieval research
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package io.anserini.search;
import org.kohsuke.args4j.Option;
import org.kohsuke.args4j.spi.StringArrayOptionHandler;
public class SearchArgs {
// required arguments
@Option(name = "-index", metaVar = "[path]", required = true, usage = "Path to Lucene index")
public String index;
@Option(name = "-topics", metaVar = "[file]", handler = StringArrayOptionHandler.class, required = true, usage = "topics file")
public String[] topics;
@Option(name = "-output", metaVar = "[file]", required = true, usage = "output file")
public String output;
@Option(name = "-topicreader", required = true, usage = "define how to read the topic(query) file: one of [Trec|Webxml]")
public String topicReader;
// optional arguments
@Option(name = "-threads", metaVar = "[Number]", usage = "Number of Threads")
public int threads = 1;
@Option(name = "-inmem", usage = "Boolean switch to read index in memory")
public Boolean inmem = false;
@Option(name = "-topicfield", usage = "Which field of the query should be used, default \"title\"." +
" For TREC ad hoc topics, description or narrative can be used.")
public String topicfield = "title";
@Option(name = "-skipexists", usage = "When enabled, will skip if the run file exists")
public Boolean skipexists = false;
@Option(name = "-searchtweets", usage = "Whether the search is against a tweet " +
"index created by IndexCollection -collection TweetCollection")
public Boolean searchtweets = false;
@Option(name = "-searchnewsbackground", usage = "Whether the search for News Track Background Linking Task " +
"index created by IndexCollection -collection WashingtonPostCollection")
public Boolean searchnewsbackground = false;
@Option(name = "-backgroundlinking.paragraph", usage = "construct one query string from each paragraph of the query document. " +
"The results will be a round-robin combination of the results from running these paragraph queries")
public boolean backgroundlinking_paragraph = false;
@Option(name = "-backgroundlinking.k", usage = "extract top k terms from the query document for TREC News Track Background " +
"Linking task. The terms are ranked by their tf-idf score from the query document")
public int backgroundlinking_k = 10;
@Option(name = "-backgroundlinking.weighted", usage = "Boolean switch to construct boosted query for TREC News Track Background " +
"Linking task. The terms scores are their tf-idf score from the query document")
public boolean backgroundlinking_weighted = false;
@Option(name = "-stemmer", usage = "Stemmer: one of the following porter,krovetz,none. Default porter")
public String stemmer = "porter";
@Option(name = "-keepstopwords", usage = "Boolean switch to keep stopwords in the query topics")
public boolean keepstop = false;
@Option(name = "-arbitraryScoreTieBreak", usage = "Break score ties arbitrarily (not recommended)")
public boolean arbitraryScoreTieBreak = false;
@Option(name = "-hits", metaVar = "[number]", required = false, usage = "max number of hits to return")
public int hits = 1000;
@Option(name = "-rerankCutoff", metaVar = "[number]", required = false, usage = "max number of hits " +
"for the initial round ranking. this is efficient since lots of reranking model only looks at " +
"the top documents from the initial round ranking.")
public int rerankcutoff = 50;
@Option(name = "-runtag", metaVar = "[tag]", required = false, usage = "runtag")
public String runtag = null;
@Option(name = "-ql", usage = "use query likelihood scoring model")
public boolean ql = false;
@Option(name = "-mu", handler = StringArrayOptionHandler.class, usage = "Dirichlet smoothing parameter")
public String[] mu = new String[] {"1000"};
// Why this value? We want to pick a value that corresponds to what the community generally considers to be "good".
// Zhai and Lafferty (SIGIR 2001) write "the optimal value of mu appears to have a wide range (500-10000) and
// usually is around 2,000. A large value is 'safer', especially for long verbose queries." We might consider
// additional evidence from TREC papers: the UMass TREC overview papers from 2002 and 2003 don't specifically
// mention query-likelihood as a retrieval model. The UMass overview paper from TREC 2004 mentions setting mu
// to 1000; incidentally, this is the first mention of what the community would later call RM3. So, this setting
// seems reasonable and does not contradict Zhai and Lafferty.
@Option(name = "-qld", usage = "use query likelihood Dirichlet scoring model")
public boolean qld = false;
@Option(name = "-qljm", usage = "use query likelihood Jelinek Mercer scoring model")
public boolean qljm = false;
@Option(name = "-qljm.lambda", handler = StringArrayOptionHandler.class, usage = "Jelinek Mercer smoothing parameter")
public String[] qljm_lambda = new String[] {"0.1"};
@Option(name = "-bm25", usage = "use BM25 scoring model")
public boolean bm25 = false;
// BM25 parameters: Robertson et al. (TREC 4) propose the range of 1.0-2.0 for k1 and 0.6-0.75 for b, with k1 = 1.2
// and b = 0.75 being a very common setting. Empirically, these values don't work very well for modern collections.
// Here, we adopt the defaults recommended by Trotman et al. (SIGIR 2012 OSIR Workshop) of k1 = 0.9 and b = 0.4.
// These values come from tuning on the INEX 2008 Wikipedia collection, which is less commonly used, so there isn't
// the danger of (inadvertently) training on test data. These settings are used in the ATIRE system and also in
// Lin et al. (ECIR 2016).
@Option(name = "-k1", handler = StringArrayOptionHandler.class, usage = "BM25 k1 parameter")
public String[] k1 = new String[] {"0.9"};
@Option(name = "-b", handler = StringArrayOptionHandler.class, usage = "BM25 b parameter")
public String[] b = new String[] {"0.4"};
@Option(name = "-inl2", usage = "use I(n)L2 scoring model")
public boolean inl2 = false;
@Option(name = "-inl2.c", metaVar = "[value]", usage = "I(n)L2 c parameter")
public String[] inl2_c = new String[] {"0.1"};
@Option(name = "-spl", usage = "use SPL scoring model")
public boolean spl = false;
@Option(name = "-spl.c", metaVar = "[value]", usage = "SPL c parameter")
public String[] spl_c = new String[] {"0.1"};
@Option(name = "-f2exp", usage = "use F2Exp scoring model")
public boolean f2exp = false;
@Option(name = "-f2exp.s", metaVar = "[value]", usage = "F2Exp s parameter")
public String[] f2exp_s = new String[] {"0.5"};
@Option(name = "-f2log", usage = "use F2Log scoring model")
public boolean f2log = false;
@Option(name = "-f2log.s", metaVar = "[value]", usage = "F2Log s parameter")
public String[] f2log_s = new String[] {"0.5"};
@Option(name = "-sdm", usage = "boolean switch to use Sequential Dependence Model query")
public boolean sdm = false;
@Option(name = "-sdm.tw", metaVar = "[value]", usage = "SDM term weight")
public float sdm_tw = 0.85f;
@Option(name = "-sdm.ow", metaVar = "[value]", usage = "ordered window weight in sdm")
public float sdm_ow = 0.1f;
@Option(name = "-sdm.uw", metaVar = "[value]", usage = "unordered window weight in sdm")
public float sdm_uw = 0.05f;
// RM3 Options: Anserini uses the same default options as in Indri.
// As of v5.13, the defaults in Indri are, from src/RMExpander.cpp:
//
// int fbDocs = _param.get( "fbDocs" , 10 );
// int fbTerms = _param.get( "fbTerms" , 10 );
// double fbOrigWt = _param.get( "fbOrigWeight", 0.5 );
// double mu = _param.get( "fbMu", 0 );
@Option(name = "-rm3", usage = "use RM3 query expansion model")
public boolean rm3 = false;
@Option(name = "-rm3.fbTerms", handler = StringArrayOptionHandler.class,
usage = "RM3 parameter: number of expansion terms")
public String[] rm3_fbTerms = new String[] {"10"};
@Option(name = "-rm3.fbDocs", handler = StringArrayOptionHandler.class,
usage = "RM3 parameter: number of documents")
public String[] rm3_fbDocs = new String[] {"10"};
@Option(name = "-rm3.originalQueryWeight", handler = StringArrayOptionHandler.class,
usage = "RM3 parameter: weight to assign to the original query")
public String[] rm3_originalQueryWeight = new String[] {"0.5"};
@Option(name = "-rm3.outputQuery",
usage = "RM3 parameter: print original and expanded queries")
public boolean rm3_outputQuery = false;
// BM25PRF Options
@Option(name = "-bm25prf", usage = "use bm25PRF query expansion model")
public boolean bm25prf = false;
@Option(name = "-bm25prf.fbTerms", handler = StringArrayOptionHandler.class,
usage = "bm25PRF parameter: number of expansion terms")
public String[] bm25prf_fbTerms = new String[] {"20"};
@Option(name = "-bm25prf.fbDocs", handler = StringArrayOptionHandler.class,
usage = "bm25PRF parameter: number of documents")
public String[] bm25prf_fbDocs = new String[] {"10"};
@Option(name = "-bm25prf.k1", handler = StringArrayOptionHandler.class,
usage = "bm25PRF parameter: k1")
public String[] bm25prf_k1 = new String[] {"0.9"};
@Option(name = "-bm25prf.b", handler = StringArrayOptionHandler.class,
usage = "bm25PRF parameter: b")
public String[] bm25prf_b = new String[] {"0.4"};
@Option(name = "-bm25prf.newTermWeight", handler = StringArrayOptionHandler.class,
usage = "bm25PRF parameter: weight to assign to the expansion terms")
public String[] bm25prf_newTermWeight = new String[] {"0.2"};
@Option(name = "-bm25prf.outputQuery",
usage = "bm25PRF parameter: print original and expanded queries")
public boolean bm25prf_outputQuery = false;
// Axiomatic semantic matching matching options.
@Option(name = "-axiom", usage = "use Axiomatic query expansion model for the reranking")
public boolean axiom = false;
@Option(name = "-axiom.outputQuery", usage = "output original and expanded query")
public boolean axiom_outputQuery = false;
@Option(name = "-axiom.deterministic", usage = "make the expansion terms axiomatic reranking results deterministic")
public boolean axiom_deterministic = false;
@Option(name = "-axiom.seed", handler = StringArrayOptionHandler.class, usage = "seed for the random generator in axiomatic reranking")
public String[] axiom_seed = new String[] {"42"};
@Option(name = "-axiom.docids", usage = "sorted docids file that for deterministic reranking. this file can be obtained " +
"by running CLI command `IndexUtils -index /path/to/index -dumpAllDocids GZ`")
public String axiom_docids = null;
@Option(name = "-axiom.r", handler = StringArrayOptionHandler.class, usage = "parameter R in axiomatic reranking")
public String[] axiom_r = new String[] {"20"};
@Option(name = "-axiom.n", handler = StringArrayOptionHandler.class, usage = "parameter N in axiomatic reranking")
public String[] axiom_n = new String[] {"30"};
@Option(name = "-axiom.beta", handler = StringArrayOptionHandler.class, usage = "parameter beta for Axiomatic query expansion model")
public String[] axiom_beta = new String[] {"0.4"};
@Option(name = "-axiom.top", handler = StringArrayOptionHandler.class, usage = "select top M terms from the expansion terms pool")
public String[] axiom_top = new String[] {"20"};
@Option(name = "-axiom.index", usage = "path to the external index for generating the reranking doucments pool")
public String axiom_index = null;
@Option(name = "-model", metaVar = "[file]", required = false, usage = "ranklib model file")
public String model = "";
@Option(name = "-qid_queries", metaVar = "[file]", usage="query id - query mapping file")
public String qid_queries = "";
}