Stopwords (or stop words) are "words which are filtered out before or after processing of natural language data" (Wikipedia), because they are "very common" words and "generally uninteresting to search for" (XTF Definition).
An important concept in text mining, information retrieval and natural language processing, they are fundamentally relative: the decision that a given lexical element carries no information and should be filtered out as background noise depends on a specific corpus and a specific purpose.
If you are not a linguist with a special interest in words like Latin "cum" or Greek "kai", if you have a large collection of Greek or Latin texts and want to make searches in these collection more efficient, or if you have to prepare an index to such a collection (probably based on automatic concordances), it is useful to have a list of stopwords handy.
Of course, such "uninteresting" words will not be excluded from your search results (thanks to the so called "bigramming", cf. the XTF Definition). Also, you can have both, providing to users of your collections searches with filtered stopwords and without such filter (as it is done in Perseus under PhiloLogic).
Most of the time, researchers compile stoplists when they need them (and if they have the time), instead of possibly improving on what others already did. This is why stopword lists openly available for Greek or Latin can be useful.
Here are the stopwords currently used by the Perseus Digital Library (see `GreekAnalyzer.java` and `LatinAnalyzer.java` in the source):
- Greek: μή, ἑαυτοῦ, ἄν, ἀλλ', ἀλλά, ἄλλος, ἀπό, ἄρα, αὐτός, δ', δέ, δή, διά, δαί, δαίς, ἔτι, ἐγώ, ἐκ, ἐμός, ἐν, ἐπί, εἰ, εἰμί, εἴμι, εἰς, γάρ, γε, γα, ἡ, ἤ, καί, κατά, μέν, μετά, μή, ὁ, ὅδε, ὅς, ὅστις, ὅτι, οὕτως, οὗτος, οὔτε, οὖν, οὐδείς, οἱ, οὐ, οὐδέ, οὐκ, περί, πρός, σύ, σύν, τά, τε, τήν, τῆς, τῇ, τι, τί, τις, τίς, τό, τοί, τοιοῦτος, τόν, τούς, τοῦ, τῶν, τῷ, ὑμός, ὑπέρ, ὑπό, ὡς, ὦ, ὥστε, ἐάν, παρά, σός – original Beta Code: mh/, e(autou=, a)/n, a)ll', a)lla/, a)/llos, a)po/, a)/ra, au)to/s, d', de/, dh/, dia/, dai/, dai/s, e)/ti, e)gw/, e)k, e)mo/s, e)n, e)pi/, ei), ei)mi/, ei)/mi, ei)s, ga/r, ge, ga^, h(, h)/, kai/, kata/, me/n, meta/, mh/, o(, o(/de, o(/s, o(/stis, o(/ti, ou(/tws, ou(=tos, ou)/te, ou)=n, ou)dei/s, oi(, ou), ou)de/, ou)k, peri/, pro/s, su/, su/n, ta/, te, th/n, th=s, th=|, ti, ti/, tis, ti/s, to/, toi/, toiou=tos, to/n, tou/s, tou=, tw=n, tw=|, u(mo/s, u(pe/r, u(po/, w(s, w)=, w(/ste, e)a/n, para/, so/s
- Caveat: if you use this list, you'll want to add τοῖς and ταῖς, and possibly remove the very unfrequent δαίς and ὑμός (see other problems below).
- Latin: ab, ac, ad, adhic, aliqui, aliquis, an, ante, apud, at, atque, aut, autem, cum, cur, de, deinde, dum, ego, enim, ergo, es, est, et, etiam, etsi, ex, fio, haud, hic, iam, idem, igitur, ille, in, infra, inter, interim, ipse, is, ita, magis, modo, mox, nam, ne, nec, necque, neque, nisi, non, nos, o, ob, per, possum, post, pro, quae, quam, quare, qui, quia, quicumque, quidem, quilibet, quis, quisnam, quisquam, quisque, quisquis, quo, quoniam, sed, si, sic, sive, sub, sui, sum, super, suus, tam, tamen, trans, tu, tum, ubi, uel, uero
- Caveat: if you use this list, you'll want to correct "adhic" to "adhuc" (see other problems below).
The statistical criteria used in selecting the words are not explicit. These lists were designed for a search engine, which also normalises some features of the corpus and of the user input. Accordingly, they cannot simply be re-used. Depending on your purpose and tools, especially whether lemmatisation is available or not, you will have to take into account problems like the following:
- In Greek: alternative breathings and accents, dialectal forms, final and lunate sigma, forms of beta, emphatic iota, iota subscript or adscript, crasis, elisions, one-letter words, and numerals, as well the normalisation of Unicode precomposed forms.
- In Latin: u/v and i/j variants, abbreviations of common praenomina, one-letter words, and numerals.
To determine which stopwords you need, you should analyse your corpus with the tool or programming language of your choice.
One approach may be to run a Lucene index on your corpus with no stopwords first, then use Luke to get the top n terms for your corpus and filter that result depending on what kind of stopword behavior you want.
To learn more about the problems and possibilities, please refer to projects offering alternative lists or methods:
The tag LatinWordStopList on bibsonomy provides a working bibliography of bookmarks and publications on word frequency in Latin.