# Improving Sentence Retrieval Using Sequence Similarity
## 6 Conclusions  
In this paper, we thoroughly tested sentence retrieval with sequence similarity, which allowed us to match words that were similar but not identical. We tested sentence retrieval methods with the partial matching of terms using TREC data. We adapted TF-ISF, BM25 and the language modeling-based method to test the partial matching of terms using sequence similarity.  
We found out that the partial matching of terms using sequence similarity can benefit sentence retrieval in all three tested collection. We showed the benefits of partial matching using sequence similarity through statistically significant better results.  
The reason for the better position of the sentence when we used adapted methods using sequence similarity is the additional matching of terms between the query \(q\) and the sentence \(s\).  
We conclude that partial matching of words is beneficial when combined with sentence retrieval. However, we did not analyze whether some nonrelevant sentences were falsely high ranked. Therefore, future research will include a thorough analyses of the effect of the partial matching of words on  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Collection** & **Measures** & **BM** & **BM\({}_{part}\)** \\ \hline \multirow{3}{*}{TREC 2002} & P@10 & 0.142 & * 0.33 \\  & MAP & 0.105 & * 0.209 \\  & R-prec. & 0.097 & * 0.255 \\ \hline \multirow{3}{*}{TREC 2003} & P@10 & 0.628 & * 0.75 \\  & MAP & 0.464 & * 0.601 \\  & R-prec. & 0.4281 & * 0.565 \\ \hline \multirow{3}{*}{TREC 2004} & P@10 & 0.366 & * 0.472 \\  & MAP & 0.242 & * 0.342 \\ \cline{1-1}  & R-prec. & 0.236 & * 0.363 \\ \hline \hline \end{tabular}  
* Statistically significant differences in relation to the baseline methods.  
\end{table}
Table 4: Test results of methods BM25 and BM25\({}_{part}\) (\(k1=1.5\), \(b=0.75\), \(k3=0\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Collection** & **Measures** & **LM** & **LM\({}_{part}\)** \\ \hline \multirow{3}{*}{TREC 2002} & P@10 & 0.268 & * 0.356 \\  & MAP & 0.170 & * 0.207 \\  & R-prec. & 0.215 & * 0.250 \\ \hline \multirow{3}{*}{TREC 2003} & P@10 & 0.71 & 0.7 \\  & MAP & 0.528 & * 0.597 \\  & R-prec. & 0.501 & * 0.567 \\ \hline \multirow{3}{*}{TREC 2004} & P@10 & 0.388 & * 0.458 \\  & MAP & 0.287 & * 0.334 \\ \cline{1-1}  & R-prec. & 0.306 & * 0.355 \\ \hline \hline \end{tabular}  
* Statistically significant differences in relation to the baseline methods.  
\end{table}
Table 5: Test results of methods LM and LM\({}_{part}\) (\(\mu=100\)).  
sentence retrieval. Future research will also include experiments using pre-processing methods, such as stemming and lemmatization or some other technique.  
Conceptualization, I.B. and A.D.; methodology, I.B. and A.D. and S.G.; software, I.B. and A.D.; validation, A.D. and S.G.; formal analysis, I.B.; investigation, I.B.; resources, I.B. and A.D.; data curation, I.B. and A.D.; writing--original draft preparation, I.B.; writing--review and editing, I.B. and A.D.; visualization, I.B.; supervision, S.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.