Experiments with Single Split Points

In this set of experiments, words are split into a single stem and a single suffix. During the segmentation, Equation 5.21 is used to determine the split position for each word.

The evaluation scores regarding the experiments with a training set of 10K words is given in Table 5.1. The maximum score is obtained with βs = 0.1 and

βm = 0.1 which is %47.01.

Some sample tree nodes obtained from trees with 10K words are given in Table 5.2. As seen from the table, morphologically similar words are grouped

βs, βm Precision(%) Recall(%) F-measure(%) 0.001 0.001 79.87..84.42 31.20..32.51 45.20..46.21 0.002 0.002 76.25..79.27 30.61..31.38 43.68..44.96 0.01 0.005 77.17..86.08 29.76..31.85 44.26..45.09 0.01 0.01 75.66..80.86 29.71..31.58 42.66..45.42 0.02 0.02 75.66..84.21 30.65..32.46 44.61..45.90 0.1 0.1 78.95..81.48 29.59..33.03 43.05..47.01 0.2 0.2 74.07..82.10 32.07..32.72 44.70..46.84

Table 5.1: Evaluation scores of single split point experiments obtained from the trees with 10K words.

together. The morphological similarity both refers to the similarity in terms of the endings and the stems of words. For example, words second+hand and third+largest are grouped in the same node through the word second+largest which shares the same stem with second+hand and same ending with third+largest.

Remark. For each set of experiments, some sample tree nodes are demon- strated to give an idea about how the tree nodes are organised generally. The discussion about the node contents are although given separately for each set of experiments, as the algorithm is the same, similar rules are to be met in each tree structure. However, as the training set is to change in each experiment, words are expected to be different each time.

Another set of experiments has been performed with 16K words. The results are demonstrated in Table 5.3. The maximum F-measure obtained is %50.02 with the setting of concentration parameters as βs = 0.001 and βm = 0.001. If we

compare the scores of the experiments with 10K and 16K words, it is noticeable that scores are slightly higher with the larger dataset.

Some sample tree nodes obtained from the trees with 16K words is given in Table 5.4. As seen from the sample nodes, prefixes can also be segmented al- though they are identified as roots; such that anti+fraud, anti+war, anti+tank, anti+nuclear. This gives a flexibility in the model by capturing the similarit- ies through either stems, suffixes, or prefixes. However, as mentioned before, the model does not consider any discrimination between different types of mor- phological forms during training. As the prefix pre- appears at the beginning of

scrambl+ed, scrambl+e, scrambl+ing, transferr+ed, influenc+ing, influenc+ed, plung+ed

downgrade+s, crash+ed, crash+ing, lack+ing, blind+ing, blind+, crash+, stifl+e, compris+ing, compris+es, stifl+ing, compris+ed, lack+s, assist+ing, blind+ed, blind+er,

third+-year, booth+, second+-largest, reign+ed, interpret+er, syndicat+ion, echo+ed, second+-round, second+ly, syndicat+ed, third+ly, second+-hand, interpret+ed, third+-largest, booth+s, reign+s, second+-best

near+ing, slight+est, deliberat+ing, slight+ly, afflict+ing, near+ly, downsiz+ing foot+age, foot+ing, foot+note, foot+-tall, slight

commut+ers, bondhold+ers, steel+ers, wrestl+ers, rul+ers anti-govern+ment, advance+ment, embezzle+ment, punish+ment

pragmat+ism, robot+ic, euphem+ism, pragmat+ic, marx+ism, popul+ism, aca- dem+ic

hen+ley, mcsor+ley, heff+ley, wheat+ley, tit+ley, mose+ley

clijst+ers, kais+er, helicopt+ers, kilomet+res, kilomet+er, teenag+er, kilomet+ers, mak+eshift, mak+ers

Table 5.2: Some tree nodes obtained from the trees with 10K words.

βs, βm Precision(%) Recall(%) F-measure(%)

0.001 0.001 78.75..86.84 31.84..35.92 46.45..50.02 0.002 0.002 76.25..81.17 30.61..34.25 43.68..48.17 0.01 0.01 78.75..85.00 31.73..34.27 45.45..48.84 0.02 0.02 79.61..83.75 32.31..34.41 46.40..48.78 0.1 0.1 80.82..86.36 29.87..34.32 44.39..48.18 0.2 0.2 71.25..86.36 32.07..33.65 45.71..47.07

Table 5.3: Evaluation scores of single split point experiments obtained from the trees with 16K words.

words, it is identified as a stem. However, identifying pre- as a stem does not yield to a change in the morphological analysis of the word.

Sometimes similarities may not yield a valid analysis of words. For example, the prefix pre- lead the words pre+mise, pre+sumed, pre+gnant, pre+cincts to be analysed wrongly whereas pre- is a valid prefix for the word pre+face (see Table 5.4). Another type of wrong analysis arises with common endings in Eng- lish as mentioned in the previous set of experiments with 10K words. For ex-

regard+less, base+less, shame+less, bound+less, harm+less, regard+ed, relent+less jave+lin, crome+lin, krem+lin, medel+lin, mer+lin

solve+d, high+-priced, lower+s, lower+-level, high+-level, lower+-income, his- tor+ians

imit+ation, sens+ation, acceler+ation, beginning+, liquid+ation, spill+s, spill+ed, be- ginning+s, privatis+ation

pre+mise, pre+face, pre+sumed, pre+, pre+cincts, pre+gnant

base+ment, ail+ment, over+looked, predica+ment, deploy+ment, compart+ment, embodi+ment

anti+-fraud, anti+-war, anti+-tank, anti+-nuclear, anti+-terrorism, switzer+, anti+gua, switzer+land

sharp+ened, strength+s, tight+ened, strength+ened, black+ened

reduc+es, trac+es, lifestyl+es, trac+tors, subcontrac+tors, phras+es, muffin+, ac- complic+es, tamal+es, illness+es, nois+es, edn+ey, institut+es, trac+ey, bon+dage, muffin+s, bon+es,

purc+ell, coldw+ell, grenf+ell, sew+ell, ferr+ell, sew+age, orw+ell, caldw+ell

Table 5.4: Some tree nodes obtained from the trees with 16K words.

ample, in Table 5.4, the words krem+lin, mer+lin, jave+lin, coldw+ell, sew+ell, orw+ellare analysed wrongly due to a number of words ending with -lin and ell. On the other side, the model can easily capture the common suffixes such that -less, -s, -ed, -mentetc.

The final set of experiments have been performed with a training set of 22K words. The maximum F-score acquired is %51.28 with the concentration para- meters βs = 0.002 and βs = 0.002 (see Table 5.5). When the evaluation scores

are compared with the first two sets of experiments’ scores, it is noticeable that scores are rather higher with the largest training set. Although, the highest scores of the experiments with 16K and 22K words are not very far from each other, the overall scores are much higher with 22K words.

Sample tree nodes obtained from the trees with 22K words are presented in Table 5.6. Similar features apply here as well; such that similar stems and endings tend to be grouped together. Another nice feature about the model is that compounds are easily captured through common stems; e.g. doubt+fire, bon+fire, gun+fire, clear+cut.

βs, βm Precision(%) Recall(%) F-measure(%) 0.001 0.001 82.50..84.62 35.69..36.02 50.15..50.33 0.002 0.002 83.55..89.04 34.66..36.01 49.16..51.28 0.01 0.01 81.51..87.18 33.49..35.48 47.85..50.43 0.02 0.02 80.86..85.00 34.60..36.16 48.47..50.36 0.1 0.1 80.67..84.18 33.45..35.36 47.70..49.17 0.2 0.2 80.26..87.50 34.52..35.59 48.28..50.50

Table 5.5: Evaluation scores of single split point experiments obtained from the trees with 22K words.

doubt+fire, bon+fire, stain+less, doubt+less, gun+fire close+s, close+ness, close+ly

investiga+tor, defini+te, investiga+te, determin+ation, determin+ing, defini+tively, determin+ed, admir+ation, determin+es, revolv+ed, investiga+tion, revolv+ing symbol+ic, hak+e, symbol+s, unit+s, admir+ed, hak+im, aesthetic+s, kind+er, sal+im, taci+t, sal+monella, unit+ed’s, admir+ers, admir+e, kind+s, taci+s, kind+red, aesthetic+

inspir+e, inspir+ing, inspir+ed, inspir+es, earn+ing, ponder+ing stok+es, utiliz+ing, utiliz+e, utiliz+ed

group+s, group+ing, interview+, interview+ing, account+ing, account+, group+ brig+ade, borrow+ings, fac+ade, jan+ice, earn+ed, earn+, appeal+, appeal+s, dis- pens+ing, appeal+ing, interview+ed, dispens+ed, interview+ers, co-ordinat+ion, co- ordinat+ed, earn+s, jan+, interview+s, fac+ed, jan+ata, appeal+ed

aid+es, inspir+ation, doubt+, prob+es, doubt+ful, doubt+s, prob+e, doubt+ed, aid+ed, aid+e, emancip+ation, prob+ation

clear+est, clear+, clear+-cut, clear+ance

Table 5.6: Some tree nodes obtained from the trees with 22K words.